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Nowoczesne metody segmentacji obrazów w wybranych przemysłowych i medycznych systemach wizyjnych

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PL
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PL
W rozprawie przedstawiono podsumowanie dotychczasowych prac autorki dotyczących problemu segmentacji obrazów cyfrowych. Omówione zagadnienia dotyczą zarówno opracowania nowoczesnych algorytmów dedykowanych rozważanemu problemowi, jak również ich zastosowania i weryfikacji w rzeczywistych aplikacjach systemów wizyjnych z dziedziny przemysłu oraz medycyny. W szczególności, zagadnienia referowane w rozprawie dotyczą czterech zasadniczych problemów, którymi są: wykorzystanie teorii grafów w segmentacji obrazów, wykorzystanie teorii błądzenia przypadkowego w segmentacji obrazów, zastosowanie technik subpikselowych do poprawy odwzorowania kształtu obiektów w procesie segmentacji oraz wykorzystanie procesu dyfuzji do sterowania przebiegiem segmentacji obrazów. W pierwszej kolejności skupiono się na najnowszych technikach grafowych stosowanych w segmentacji obrazów. Przedstawiono ich charakterystykę, przeanalizowano słabe strony oraz zaproponowano dwa autorskie rozwiązania, pozwalające na eliminację wskazanych wad. Następnie rozważano algorytm random walker oparty na teorii błądzenia przypadkowego. Zaproponowano sposób połączenia tej metody z prowadzonym w trzech wymiarach rozrostem obszaru, w taki sposób, aby skutecznie dokonywać segmentacji danych wolumetrycznych i jednocześnie unikać wycieków spowodowanych nieciągłością krawędzi poddawanych segmentacji obiektów. Znaczącą część rozprawy poświęcono również problemowi zastosowania subpikselowych metod przetwarzania i analizy obrazów w procesie segmentacji. Dokonano obszernego przeglądu istniejących metod z tej grupy oraz przeanalizowano ich przydatność w procesie segmentacji obrazów. Zaproponowano autorskie rozwiązanie dedykowane segmentacji obrazów z subpikselową precyzją. Ostatecznie rozważono możliwość połączenia anizotropowej filtracji obrazów opartej na procesie dyfuzji z segmentacją poprzez rozrost obszaru. W konsekwencji, opracowano uniwersalny algorytm, który pozwala na poprawną segmentację struktur o włosowatym kształcie. Wszystkie zaprezentowane w rozprawie algorytmy segmentacji obrazów zaimplementowano, a ich poprawność zweryfikowano poprzez zastosowanie w rzeczywistych systemach pomiarowych, reprezentujących trzy zasadniczo różne dziedziny, tj.: metalurgię, włókiennictwo oraz medycynę. Należy również podkreślić, że rozprawa jest pierwszym tak obszernym opracowaniem, poświęconym innym niż tradycyjne metodom segmentacji obrazów cyfrowych.
EN
The dissertation summarizes results of author’s research on digital image segmentation. The discussed issues concern both the development of modern algorithms dedicated to the regarded problem as well as their application and verification in a real-world vision systems from the field of industry and medicine. In particular the issues referred in the dissertation relate to the four main problems, namely: the use of graph theoretical methods in image segmentation, the application of random walk theory in image segmentation, the use of subpixel techniques for increasing accuracy of image segmentation and the application of the diffusion process for controlling image segmentation. Firstly, the latest graph based methods for image segmentation were regarded. Their characteristics were presented and their weaknesses were analyzed. As a result, two author’s methods were presented which allowed for elimination of the identified weaknesses. Next, the random walker algorithm was considered. The new approach based on the random walks theory and region growing was proposed. The introduced method is dedicated to effective segmentation of volumetric images and allows to avoid leakages caused by a weak boundaries of the segmented objects. A significant part of the dissertation was also devoted to the problem of application of subpixel methods of image processing and analysis in image segmentation. The exhaustive review of the existing subpixel methods was provided and their usefulness in image segmentation was examined. The author’s algorithm for image segmentation with subpixel precision was proposed. Finally, the possibility to combine diffusion-based image anisotropic filtering with region growing segmentation was regarded. As a result, a new universal method for segmentation of flow-like, capillary objects was introduced. All image segmentation algorithms proposed in the dissertation were implemented. Their correctness and accuracy were positively verified in the real-world measurement systems, representing three different fields, namely: metallurgy, textiles and medicine. It should also be underlined, that the dissertation is the first one, such exhaustive study devoted to other than the traditional ones image segmentation algorithms.
Rocznik
Tom
Strony
1--228
Opis fizyczny
Bibliogr. 547 poz., il., wykr.
Twórcy
  • Politechnika Łódzka. Wydział Elektrotechniki, Elektroniki, Informatyki i Automatyki, Instytut Informatyki Stosowanej
Bibliografia
  • Adams R., Bischof L., 1994: Seeded Region Growing. IEEE Transactions on, Pattern Analysis and Machine Intelligence, 16(6), pp. 641-647.
  • Adamson A., Gast A., 1997: Physical Chemistry of Surface. Wiley, USA.
  • Alakuijala J., Laitinen J., Sallinen S., Helminen H., 1995: New Efficient Image Segmentation Algorithm: Competitive Region Growing of Initial Regions. IEMBS’1995, Proceedings of IEEE 17th Annual Conference on Engineering in Medicine and Biology Society, pp. 409-410.
  • Al-Hujazi E., Sood A., 1990: Range Image Segmentation Combining Edge-Detection and Region- Growing Techniques with Applications to Robot Bin-Picking Using Vacuum Gripper. IEEE Transactions on Systems, Man and Cybernetics 20(6), pp. 1313-1325.
  • Al-Kofahi Y., Lassoued W., Lee W., Roysam B., 2010: Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images. IEEE Transactions on Biomedical Engineering, 57(4), pp. 841-852.
  • Alvarez L., Guichard F., Lions P.L., Morel J.M., 1993: Axioms and Fundamental Equations of Image Processing. Archive for Rational Mechanics and Analysis, 123(3), pp. 199-257.
  • Amankwah A., Aldrich C., 2011: Automatic Ore Image Segmentation Using Mean Shift and Watershed Transform. RADIOELEKTRONIKA, Proceedings of 21st International Conference Radioelektronika, pp. 1-4.
  • American Thoracic Society Statement, 1995: Standards for the Diagnosis and Care of Patients with Chronic Obstructive Pulmonary Disease. American Journal of Respiratory and Critical Care Medicine, 152, pp. S77-S121.
  • Argyle E., 1971: Techniques for Edge Detection. Proceedings of IEEE, 59, 285-287.
  • Armstrong C.J., Barrett W.A., Price B., 2006: Live Surface. Proceedings of IEEE VGTC Workshop on Volume Graphics ‘06, 22, pp. 661-670.
  • Arthur D., Vassilvitskii S., 2007: K-means++: The Advantages of Careful Seeding. SODA’2007, Proceedings of 18th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027-1035.
  • Ashoorirad M., Baghbani R., 2009: Blood Vessel Segmentation in Angiograms using Fuzzy Inference System and Mathematical Morphology. ICSPS’2009, Proceedings of International Conference on Signal Processing Systems, pp. 272-276.
  • Axler S., Gorkin P., Voss K., 2004: The Dirichlet Problem on Quadratic Surfaces. Mathematics of Computation, 73, pp. 637-651.
  • Aykac D., Hoffman E.A., McLennan G., Reinhardt J. M., 2003: Segmentation and Analysis of the Human Airway Tree From Three-Dimensional X-Ray CT Images. IEEE Transactions on Medical Imaging, 22(8), pp. 940-950.
  • Azzabou N., Paragios N., Guichard F., Cao F., 2006: Random Walks, Constrained Multiple Hypothesis Testing and Image Enhancement. ECCV’2006, Proceedings of 9th European Conference on Computer Vision, 1, pp. 379-390.
  • Bachevsky R.S., Naidich Y.V., Grygorenko M.F., Dostojny V.A., 1994: Evaluation of Errors in Automatic Image Analysis Determination of Sessile Drop Shapes. Proceedings of International Conference: High Temperature Capillarity, pp. 254-258.
  • Bailey D.G., 2005: Sub-pixel Profiling. ICICS’2005, Proceedings of 5th International Conference on Information Communications and Signal Processing, pp. 1311-1315.
  • Banachowski L., Diks K., Rytter W., 2003: Algorytmy i Struktury Danych. Wydawnictwa Naukowo- Techniczne, WNT.
  • Barella A., Manich A.M., 1997: Yarn Hairiness Updates. Textile Progress, 26(4), pp. 1-29.
  • Barella A., Manich A.M., 2002: Yarn Hairiness: A Further Update. Textile Progress, 31(4), pp. 1-44.
  • Barret W., Mortensen E., 1996: Fast, Accurate and Reproducible Live-Wire Boundary Extraction. Visualization in Biomedical Computing, 1131, pp. 183-192.
  • Bartz D., Mayer D., Fischer J., Ley S., del Rio A., Thust S., Heussel C.P, Kauczor H.U., Straber W., 2003: Hybrid Segmentation and Exploration of The Human Lungs. IEEE Visualization, pp. 177-184.
  • Basu S., Aksel A., Condron B., Acton S.T., 2009: Automatic Segmentation of Drosophila Neurons for Content Based Retrieval Using a Minimum Spanning Tree Approach. Asilomar’2009, Proceedings of Forty-Third Asilomar Conference on Signals, Systems and Computers, pp. 7-11.
  • Basu S., Condron B., Acton S.T., 2010: Path-Space Algebra for Constructing an Average Neuronal Atlas from Multi-Class Neuronal Datasets. ASILOMAR’2010, Proceedings of 44th Asilomar Conference on Signals, Systems and Computers, pp. 1080-1084.
  • Bauer C., Bischof H., 2008: A Novel Approach for Detection of Tubular Objects and its Application to Medical Image Analysis. DAGM’2008, Proceedings of 30th DAGM Symposium on Pattern Recognition, pp. 163-172.
  • Bauer C., Pock T., Bischof H., Beichel R., 2009: Airway Tree Reconstruction Based on Tube Detection. Proceedings of Second International Workshop on Pulmonary Image Analysis, pp. 203-213.
  • Bąkała M., Strzecha K., Koszmider T., Fabijańska A., 2010a: Skomputeryzowany system Thermo-Wet do wyznaczania właściwości fizykochemicznych lutów twardych. Przegląd Spawalnictwa, 10, pp. 67-71.
  • Bąkała M., Strzecha K., Koszmider T., Fabijańska A., 2010b: Automation of Vision and Transportation Modules, Implemented in the Thermo-Wet System. Zeszyty naukowe Automatyka, 14(3.1), pp. 395-401.
  • Belhomme P., Elmoataz A., Herlin P., Bloyet D., 1997: Generalized Region Growing Operator with Optimal Scanning: Application to Segmentation of Breast Cancer Images. Journal of Microscopy, 186 (1), pp. 41-50.
  • Berg H.C., 1993: Random Walks in Biology. Princeton University Press.
  • Berger P., Perot V., Desbarats P., Tunon-de-Lara J.M., Marthan R., Laurent F., 2005: Airway Wall Thickness in Cigarette Smokers: Quantitative Thin-section CT Assessment. Radiology, 235(3), pp. 1055- 1064.
  • Bergholm F., 1987: Edge Focusing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(6) pp. 726-741.
  • Bernsen J., 1986: Dynamic Thresholding of Gray Level Images. ICPR’86, Proceedings of International Conference on Pattern Recognition, pp. 1251-1255.
  • Beucher S., Meyer F., 1993: The Morphological Approach to Segmentation: The Watershed Transformation. Mathematical Morphology in Image Processing, E.R. Dougherty, Ed. New York: Marcel Dekker, pp. 433-481.
  • Beveridge J.R., Griffith J., Kohler R.R., Hanson A.R., Riseman E.M., 1989: Segmenting Images Using Localizing Histograms and Region Merging. International Journal of Computer Vision, 2(3), pp. 311-347.
  • Bezdek, J.C., 1981: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York.
  • Bezdek J.C., Keller J.M., Krishnapuram R., Pal N.R., 1999: Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer, New York.
  • Bianchi L., Gatti R., Lombardi L., Lombardi P., 2009: Tracking without Background Model for Time-of-Flight Cameras. Lecture Notes in Computer Science, 5414, pp. 726-737.
  • Bie H.X., Liu C.Y., 2009: Edge-Directed Sub-Pixel and Still Image Super-Resolution. ICISP’2009, Proceedings of 2nd International Congress on Image and Signal Processing, pp. 1-4.
  • Bilgen D., 2000: Segmentation and Analysis of the Human Airway Tree from 3D X-Ray CT Images. M. Sc thesis, Department of Biomedical Engineering, University Iowa, USA.
  • Bieniecki W., Grabowski S., Sekulska J., Turant M., Kałużyński A., 2003: Automatic Segmentation and Recognition of Pathomorphological Microscopic Images. CADSM’03, Proceedings of the 7th International Conference. The Experience of Designing and Application of CAD Systems in Microelectronics, pp. 461-464.
  • Bieniecki W., 2004: Oversegmentation Avoidance in Watershed-Based Algorithms for Color Images. TCSET’04, Proceedings of the International Conference Modern Problems of Radio Engineering, Telecommunications and Computer Science, pp. 169-172.
  • Bieniecki W., Grabowski S., Rozenberg W., 2007: Image Preprocessing for Improving OCR Accuracy. MEMSTECH’2007, Proceedings of International Conference on Perspective Technologies and Methods in MEMS Design, pp. 75-80.
  • Bin T.J., Lei A., Jiwen C., Wenjing K., Dandan L., 2008: Subpixel Edge Location Based on Orthogonal Fourier-Mellin Moments. Image and Vision Computing, 26(4), pp. 563-569.
  • Bondy A., Murty U. S. R., 2010: Graph Theory (Graduate Texts in Mathematics). Springer.
  • Boudhar A., Duchemin B., Hanich L., Jarlan L., Chaponniere A., Maisongrande P., Boulet G., Chehbouni A., 2010: Long-Term Analysis of Snow-Covered Area in the Moroccan High- Atlas Through Remote Sensing. International Journal of Applied Earth Observation and Geoinformation, 12S, pp. S109-S115.
  • Boyd D.S., Foody G.M., 2010: An Overview of Recent Remote Sensing and GIS Based Research in Ecological Informatics. Ecological Informatics 6, pp. 25-36.
  • Boykov Y., Veksler O., Zabih R., 1999: Fast Approximate Energy Minimization via Graph Cuts. ICCV’1999, Proceedings of International Conference on Computer Vision, 1, pp. 377-384.
  • Boykov Y., Jolly M.P., 2001: Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. ICCV’2001, Proceedings of International Conference on Computer Vision, pp. 105-112.
  • Boykov Y., Kolmogorov V., 2004: An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), pp. 1124-1137.
  • Boykov Y., Funka-Lea G., 2006: Graph Cuts and Efficient N-D Image Segmentation. International Journal of Computer Vision, 70(2), pp. 109-131.
  • Boykov Y., Kolmogorov V., Cremers D., Delong A., 2006: An Integral Solution to Surface Evolution PDEs Via Geo-Cuts. Lecture Notes in Computer Science, 3953, 3, pp. 409-422.
  • Breder R., Estrela V.V., de Assis J.T., 2009: Sub-Pixel Accuracy Edge Fitting by Means of BSpline. MMSP’2009, Proceedings of IEEE International Workshop on Multimedia Signal Processing, pp. 1-5.
  • Brejl M., Sonka M., 1998: Edge-Based Image Segmentation: Machine Learning from Examples. IJCNN’98, Proceedings of IEEE International Joint Conference on Neural Networks, pp. 814-819.
  • Brejl M., Sonka M., 2000: Object Localization and Border Detection Criteria Design in Edge- Based Image Segmentation: Automated Learning from Examples. IEEE Transactions on Medical Imaging, 19(10), pp. 973-985.
  • Brice C.R., Fennema C.L., 1970: Scene Analysis Using Regions. Artificial Intelligence, 1(3-4), pp. 205-226.
  • Brown M.S., McNitt M.F., Mankovich N.J., Goldin J., Aberle D.R., 1996: Knowledge-Based Automated Technique for Measuring Total Lung Volume from CT. Proceedings SPIE, 2709, pp. 63-74.
  • Brown M., de Bruijne M., van Ginneken B., Kiraly A., Kuhnigk J.-M., Lorenz C., McClelland J. R., Mori K., Reeves A., Reinhardt J.M. (Eds): The Second International Workshop on Pulmonary Image Analysis. CreateSpace, 2009.
  • Buck P.E., Sabol D.E., Gillespie A.R., 2003: Sub-Pixel Artifact Detection Using Remote Sensing. Journal of Archaeological Science, 30, pp. 973-989.
  • Buecher S., 1982: Watersheds of Functions and Picture Segmentation. ICASSP’82, Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 1928-1931.
  • Bunyak F., Palaniappan K., Glinskii O., Glinskii V., Glinsky V. Huxley V., 2008: Epifluorescencebased Quantitative Microvasculature Remodeling Using Geodesic Level-Sets and Shapebased Evolution. EMBS’2008, Proceedings of 30th Annual International IEEE EMBS Conference, pp. 3134-3137.
  • Burger W., Burge M.J., 2012: Principles of Digital Image Processing: Advanced Methods. Springer.
  • Busayarat S., Zrimec T., 2005: Automatic Detection of Pulmonary Arteries and Assessment of Bronchial Dilatation in HRCT Images of the Lungs. ICSC’2005, Proceedings of International Conference on Computational Intelligence: Methods & Applications, pp.
  • Busayarat S., Zrimec T., 2007: Detection of Bronchopulmonary Segments on High-Resolution CT-Preliminary Results. CBMS’2007, Proceedings of 20th IEEE International Symposium on Computer-Based Medical Systems, pp. 199-204.
  • Boukharouba S., Rebordao J.M., Wendel P.L., 1985: An Amplitude Segmentation Method Based on the Distribution Function of an Image. Graphical Models and Image Processing, 29, pp. 47-59.
  • Brink A.D., Pendock N.E.: Minimum Cross Entropy Threshold Selection. Pattern Recognition, 29, pp. 179-188.
  • Cai J., Liu Z.Q., 1998: A New Thresholding Algorithm Based on All-Pole Model. ICPR’98, Proceedings of International Conference on Pattern Recognition, pp. 34-36.
  • CaiHong S., Jing W., 2008: An Improved Sub-Pixel Edge Detection Method and Application in Measurement of Spring Dimension. IITA’2008, Proceedings of 2nd International Symposium on Intelligent Information Technology Application, pp. 442-446.
  • Canny J.,1986: A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, pp. 679-698.
  • Cao Y., Wang B., Xiao H., Jiang H., Zhu Z., Yin Q., 2009: An Efficient Sub-Pixel Edge Extraction Method for CT Brain Images. CiSE’2009, Proceedings of International Conference on Computational Intelligence and Software Engineering, pp. 1-4.
  • Carvalho V., Cardoso P., Vasconcelos R., Oliveira F., Belsley M., 2007: Optical Yarn Hairiness Measurement System. INDIN’2007, Proceedings of IEEE International Conference on Industrial Informatics, 5, pp. 359-364.
  • Caselles V., Catte F., Coll T., Dibos F., 1993: A Geometric Model for Active Contours. Numerische Mathematik, 66, pp. 1-31.
  • Caselles V., Kimmel R., Sapiro G., 1997: Geodesic Active Contours. International Journal of Computer Vision, 22(1), pp. 61-79.
  • Carlotto M.J., 1997: Histogram Analysis Using a Scale-Space Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9, pp. 121-129.
  • Ceresa M., Artaechevarria X., Munoz-Barrutia A., Ortiz-de-Solorzano C., 2010: Automatic Leakage Detection and Recovery for Airway Tree Extraction in Chest CT Images. ISBI’2010, Proceedings of International Symposium on Biomedical Imaging, pp. 568-571.
  • Chabat F., Xiao-Peng H., Hansell D.M., Guang-Zhong Y., 2001: ERS Transform for the Automated Detection of Bronchial Abnormalities on CT of the Lungs. IEEE Transactions on Medical Imaging, 20(9), pp. 942-952.
  • Chang C.I., Du Y., Wang J., Guo S.M., Thouin P.D., 2006: Survey and Comparative Analysis of Entropy and Relative Entropy Thresholding Techniques. IEE Proceedings-Vision, Image and Signal Processing, 153(6), pp. 837-850.
  • Chang Y.L., Li X., 1994: Adaptive Image Region-Growing. IEEE Transactions on Image Processing, 3(6), pp. 868-872.
  • Cheng H.D., Chen Y.H., Sun Y., 1999: A Novel Fuzzy Entropy Approach to Image Enhancement and Thresholding. Signal Processing, 75, pp. 277-301.
  • Chen Y., Yin R., Flynn P., Broschat S., 2003: Aggressive Region Growing for Speckle Reduction in Ultrasound Images. Pattern Recognition Letters, 24(4-5), pp. 677-691.
  • Cheng J., Rajapakse J.C., 2009: Segmentation of Clustered Nuclei With Shape Markers and Marking Function. IEEE Transactions on Biomedical Engineering, 56(3), pp. 741-748.
  • Cheng S.C., Tsai W.H., 1993: A Neural Network Approach of the Moment-Preserving Technique and Its Application to Thresholding. IEEE Transactions on Computers, 42, pp. 501-507.
  • Cheng C.M., Pei S.C., 1998: Sub-Pixel Color Edge Detection by Using Binary Quaternion- Moment-Preserving Thresholding Technique. UT’1998, Proceedings of International Symposium on Underwater Technology, pp. 295-298.
  • Cheng, Y., 1995: Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(8), pp. 790-799.
  • Chimeh M.Y., Tehran M.A., Latifi M., Mojtahedi M.R.M., 2005: Characterizing Bulkiness and Hairiness of Air-Jet Textured Yarn Using Imaging Techniques. Journal of the Textile Institute, 96(4), pp. 251-255.
  • Cho S., Haralick R., Yi S., 1989: Improvement of Kittler and Illingworths’s Minimum Error Thresholding. Pattern Recognition, 22, pp. 609-617.
  • Cohen L.D., 1989: On Active Contour Models And Balloons. Computer Graphics and Image Processing, 53(2), pp. 211-218.
  • Cohen L.D., Cohen I., 1993: Finite Element Methods for Active Contour Models and Balloons for 2D and 3D Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), pp. 1131-1147.
  • Comaniciu D., Meer P., 1997: Robust Analysis of Feature Spaces: Color Image Segmentation. CVPR 1997, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 750-755.
  • Comaniciu D., Meer, P., 2002: Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), pp. 603-619.
  • Cormen T.H., Leiserson, C.E., Rivest R.L., Stein C.: 2009: Introduction to Algorithms. MIT Press.
  • Cousty J., Couprie M., Najman L., Bertrand G., 2008: Fusion Graphs: Merging Properties and Watersheds. Journal of Mathematical Imaging and Vision, 30(1), pp. 87-104.
  • Cox I.J., Rao S.B., Zhong Y., 1996: Ratio Regions: A Technique for Image Segmentation. ICPR’1996, Proceedings of the International Conference on Pattern Recognition, pp. 557-564.
  • Cremers D., 2006: Dynamical Statistical Shape Priors for Level Set Based Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, pp. 1262-1273.
  • Cremers D., Osher S., Soatto S., 2006: Kernel Density Estimation And Intrinsic Alignment for Shape Priors in Level Set Segmentation. International Journal of Computer Vision, 69(3), pp. 335-35.
  • Cui J. W., Tan J. B., Zhou Y., Zhang H., 2006: Improvement of Vision Measurement Accuracy Using Zernike Moment Based Edge Location Error Compensation Model. Journal of Physics: Conference Series, 48, pp. 1353-1360.
  • Cui Y., Fu Q., Ge X., Yue Y., 2009: Study on Beeline Edge Subpixel Localization for Mechanical Part. ICISP’2009, Proceedings of 2nd International Congress on Image and Signal Processing, pp. 1-5.
  • Cullum J.K., Willoughby R.A., 2002: Lanczos Algorithms for Large Symmetric Eigenvalue Computations. Society for Industrial and Applied Mathematics, USA.
  • Cybulska M., 1999: Assessing Yarn Structure with Image Analysis Methods. Textile Research Journal, 69, pp. 369-373.
  • Cyganek B., 2002: Komputerowe Przetwarzanie Obrazów Trójwymiarowych. Exit.
  • Da F., Zhang H., 2010: Sub-pixel Edge Detection Based on an Improved Moment. Image and Vision Computing, 28(12), pp. 1645-1658.
  • Das P., Veksler O., Zavadsky V., Boykov Y., 2006: Semiautomatic Segmentation with Compact Shape Prior. CRV’2006, Proceedings of Canadian Conference on Computer and Robot Vision, pp. 26-38.
  • Davis L.S., 1975: A Survey of Edge Detection Techniques. Computer Graphics and Image Processing, 4, pp. 248-270.
  • Dehmeshki J., Amin H., Valdivieso M., Ye X., 2008: Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach. IEEE Transactions on Medical Imaging, 27(4), pp. 467-480.
  • Deng G., Pinoli, J.C., 1998: Differentiation-Based Edge Detection Using the Logarithmic Image Processing Model. Journal of Mathematical Imaging and Vision, 8, pp. 161-180.
  • Dennis J.E., Schnabel R.B, 1983: Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall, New Jersey.
  • Delong A., Boykov Y., 2008: A Scalable Graph-Cut Algorithm for N-D Grids. CVPR’2008. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8.
  • Delp E.J., Mitchell O.R., 1991: Moment-Preserving Quantization. IEEE Transactions on Communications, 39, pp.1549-1558.
  • Deravi F., Pal S.K., 1983: Grey Level Thresholding Using Second-Order Statistics. Pattern Recognition Letters, 1, pp. 417-422.
  • Deriche R., 1987: Using Canny’s Criteria To Derive An Optimal Edge Detector Recursively Implemented. International Journal of Computer Vision, 1, pp. 167-187.
  • Dijkstra E.W., 1959: A Note on Two Problems in Connexion with Graphs. Numerische Mathematik, 1, pp. 269-271.
  • Dima A., Scholz M., Obermayer K., 2002: Automatic Segmentation and Skeletonization of Neurons From Confocal Microscopy Images Based on the 3-D Wavelet Transform. IEEE Transactions on Image Processing, 11(7), pp. 790-801.
  • Domański M., 2010: Obraz Cyfrowy. Wydawnictwo Komunikacji i Łączności.
  • Doros A., 2005: Przetwarzanie Obrazów. Wydawnictwo Wyższej Szkoły Informatyki Stosowanej i Zarządzania w Warszawie.
  • Doyle P.G., Snell J.L, 2006: Random Walks and Electric Networks. Mathematical Association of America.
  • Duda R.O., Hart P.E., 1973: Pattern Classification and Scene Analysis. Wiley.
  • Duda R.O., Hart P.E., Stork D.G., 2000: Pattern Classification. Wiley-Interscience.
  • Dunn J.C., 1973: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3, pp. 32-57.
  • Durikovic R., Kaneda K., Yamashita H., 1995: Dynamic Contour: A Texture Approach and Contour Operations. The Visual Computer, 11, pp. 277-289.
  • Espona L., Carreira M.J., Penedo M.G., Ortega M., 2008: Retinal Vessel Tree Segmentation using a Deformable Contour Model. ICPR’08, Proceedings of IEEE International Conference on Pattern Recognition, pp. 1-4.
  • Estrada F., Fleet D., Jepson A., 2009: Stochastic Image Denoising. Proceedings of British Machine Vision Conference, pp. 117.1-117.11.
  • Extrand, C. W., Moon S. I., 2010: When Sessile Drops Are No Longer Small: Transitions from Spherical to Fully Flattened.
  • Langmuir, 26(14), pp. 11815-11822.
  • Fabijańska A., 2007: Algorytmy Poprawy Jakości Obrazów w Wysokotemperaturowych Pomiarach Właściwości Fizykochemicznych Wybranych Metali i ich Stopów. Rozprawa doktorska, Łódź, Politechnika Łódzka.
  • Fabijańska A., Sankowski D., 2008a: Filtry Optyczne w Systemie Wizyjnym do Wysokotemperaturowych Pomiarów Właściwości Powierzchniowych Metali i Ich Stopów. Zeszyty naukowe Automatyka, 12(3), pp. 625-639.
  • Fabijańska A., Sankowski D., 2008b: Algorithm of Optical Filter Self-Acting Change for High Temperature Applications of Vision Systems. ICSES’2008, Proceedings of IEEE International Conference on Signals and Electronic Systems, pp. 363-366.
  • Fabijańska A., Sankowski D., 2008c: Preprocessing of Images Obtained from High Temperature Vision System. IST’2008, Proceedings of IEEE International Workshop on Imaging Systems and Techniques, pp. 204-207.
  • Fabijańska A., Sankowski D., 2008d: Edge Detection in Brain Images. MEMSTECH’2008, Proceedings of IEEE 4th International Conference on Perspective Technologies and Methods in MEMS Design, pp. 60-62.
  • Fabijańska A., Kuzański M., Sankowski D., Jackowska-Strumiłło L., 2008: Application of Image Processing and Analysis in Selected Industrial Computer Vision Systems. MEMSTECH’2008, Proceedings of IEEE International Conference on Perspective Technologies and Methods in MEMS Design, pp. 27-31.
  • Fabijańska A., 2009a: Two-Pass Region Growing Algorithm for Segmenting Airway Tree From MDCT Chest Scans. Computerized Medical Imaging and Graphics, 33, pp. 537-546.
  • Fabijańska A., 2009b: A Fuzzy Segmentation Method for Images of Heat-Emitting Objects, Lecture Notes in Computer Science, 5856, Springer Berlin/Heidelberg, pp. 247-254.
  • Fabijańska A., 2009c: The Recursive Approach to Image Segmentation, MEMSTECH’2009, Proceedings of IEEE 5th International Conference Perspective Technologies and Methods in MEMS Design, pp. 53-55.
  • Fabijańska A., 2009d: Results of Applying Two-Pass Region Growing Algorithm for Airway Tree Segmentation to MDCT Chest Scans from EXACT Database. The Second International Workshop on Pulmonary Image Analysis, CreateSpace, USA, pp. 251-260.
  • Fabijańska A., 2009e: An Approach to Segmentation of Bronchial Tree form Volumetric CT Chest Scans, Recent Advances in Numerical Modelling, Electrotechnical Institute Publishing House, pp. 143-147.
  • Fabijańska A., 2009f: Switching Median Filter for Denosing Images Corrupted by Impulse Noise. Recent Advances in Numerical Modelling, Electrotechnical Institute Publishing House, pp. 175-179.
  • Fabijańska A., 2009g: Two-Pass Median Filter for Impulse Noise Removal. Zeszyty naukowe Automatyka, 13(3), pp. 807-820.
  • Fabijańska A., Janaszewski M., Postolski M., Babout L., 2009: Airway Tree Segmentation from CT Scans Using Gradient-Guided 3D Region Growing. Lecture Notes in Computer Science, Vol. 5856, Springer Berlin/Heidelberg, pp. 217-224.
  • Fabijańska A., Sankowski D., 2009a: Computer Vision System for High Temperature Measurements of Surface Properties. Machine Vision and Applications, 20(6), pp. 411-421.
  • Fabijańska A., Sankowski D., 2009b: Improvement of Image Quality of High-Temperature Vision System. Measurement Science and Technology, 20, 104018, 9 pp.
  • Fabijańska A., 2010a: Sub-pixel Approach to Detection of Significantly Blurred Edges. ICSES’2010, Proceedings of IEEE International Conference on Signals and Electronic Systems, pp. 135-138.
  • Fabijańska A., 2010b: Lokalizacja Krawędzi na Poziomie Subpikselowym w Obrazach Rozgrzanych Metali i Ich Stopów. Elektronika-Konstrukcje, Technologie, Zastosowania, 12, pp. 58-61.
  • Fabijańska A., 2010c: A Survey Over Moment-Based Sub-pixel Approaches to Edge Detection in Grayscale Images. SiS’2010, Proceedings of XVII International Conference on Information Technology Systems-Theory, Design, Implementations, Applications, artykuł#17001.
  • Fabijańska A., 2010d: Region Growing Segmentation for Textile Yarn Images. IST’2010, Proceedings of IEEE International Conference on Imaging Systems and Techniques, pp. 437-440.
  • Fabijańska A., Koszmider T., Strzecha K., Bąkała M., 2010a: Precise Edge Detection in Images of Melted Specimens of Metals and Alloys. MEMSTECH’2010, Proceedings of IEEE 6th International Conference Perspective Technologies and Methods in MEMS Design, pp. 67-70.
  • Fabijańska A., Strzecha K., Koszmider T., Bąkała M., 2010b: Refining Edges to Sub-Pixel Level in Images of Molten Metals and Alloys. Zeszyty naukowe Automatyka, 14(3.1), AGH, Kraków, pp. 411-422.
  • Fabijańska A., Sankowski D., 2010: Edge Detection with Sub-Pixel Accuracy in Images of Molten Metals. IST’2010, Proceedings of IEEE International Conference on Imaging Systems and Techniques, pp. 186-191.
  • Fabijańska A., 2011a: Variance Filter for Edge Detection and Edge-Based Image Segmentation. MEMSTECH’07, Proceedings of IEEE International Conference Perspective Technologies and Methods in MEMS Design, pp. 151-154.
  • Fabijańska A., 2011b: Yarn Image Segmentation Using the Region Growing Algorithm. Measurement Science and Technology, 22, 114024, 9pp.
  • Fabijańska A., 2011c: Modified Ranked Order Adaptive Median Filter for Impulse Noise Removal, Computer Recognition Systems 4, Advances in Intelligent and Soft Computing, 95, Springer Berlin/Heidelberg, pp. 73-82.
  • Fabijańska A., 2011d: Graph Based Image Segmentation. Zeszyty naukowe Automatyka, 15(3), AGH, Kraków, pp. 93-104.
  • Fabijańska A., Sankowski D., 2011: Noise Adaptive Switching Median-Based Filter for Impulse Noise Removal from Extremely-Corrupted Images, IET Image Processing, Institution of Engineering and Technology, 5(5), pp. 472-480.
  • Fabijańska A., Jackowska-Strumiłło L., 2012: Image Processing and Analysis Algorithms for Yarn Hairiness Determination. Machine Vision and Applications, 23(3), pp. 527-540.
  • Fabijańska A., 2012a: Extraction of Pulmonary Vessels From MDCT Thorax Scans. IST’2012, Proceedings of IEEE International Conference Imaging Systems and Techniques, pp. 63-67.
  • Fabijańska A., 2012b: A Survey of Subpixel Edge Detection Methods on Images of Heat-emitting Metal Specimens. International Journal of Applied Mathematics and Computer Science, 22(3), w druku.
  • Fabijańska A., 2012c: Normalized Cuts and Watersheds For Image Segmentation. IETIPR’ 2012, Proceedings of IET Image Processing Conference, pp. 1-6.
  • Fabijańska A., 2012d: On Graph Based Image Segmentation Using Graph Cuts in Feature Space. MEMSTECH’2012, Proceedings of IEEE 8th International Conference Perspective Technologies and Methods in MEMS Design, pp. 5-8.
  • Falcão A.X., Udupa J.K., 1997: Segmentation of 3D Objects Using Livewire. In SPIE Medical Imaging, 3034, pp. 228-239.
  • Falcão A.X., Udupa J.K., Samarasekera S., Sharma S., Elliot B. H., de A. Lotufo R., 1998: User- Steered Image Segmentation Paradigms: Live Wire and Live Lane. Graphical Models and Image Processing, 60, pp. 233-260.
  • Falcão A.X., Udupa J.K., Miyazawa F.K., 2000: Ultrafast User-Steered Image Segmentation Paradigm: Livewire-on-the-Fly. IEEE Transactions on Medical Imaging, 19(1), pp. 55-62.
  • Falcão A.X., Udupa J.K., 2000: A 3D Generalization of User-Steered Live-Wire Segmentation. Medical Image Analysis, 4, pp. 389-402.
  • Fan J., Wang R., Zhang L., Xing D., Gan F., 1996: Image Sequence Segmentation Based on 2D Temporal Entropy. Pattern Recognition Letters, 17, pp. 1101-1107.
  • Fan J., Zhang L., Gan F., 1997: Spatiotemporal Segmentation Based on Spatiotemporal Entropic Thresholding. Optical Engineering, 36, pp. 2845-2851.
  • Fan J., Yau D.K.Y., Elmagarmid A.K., Aref W.G., 2001: Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing. IEEE Transactions on Image Processing, 10 (10), pp. 1454-1466.
  • Fan J., Zeng G., Body M., Hacid M.S., 2005: Seeded Region Growing: An Extensive and Comparative Study. Pattern Recognition Letters, 26 (8), pp. 1139-1156.
  • Fan L., Chen C.W., 2000: Reconstruction of Airway Tree Based on Topology and Morphological Operations. Proceedings SPIE Medical Imaging, 3978, pp. 46-57.
  • Feldman J., Yakimovsky Y., 1974: Decision Theory and Artificial Intelligence: I. A Semantics Based Region Analyzer. Artificial Intelligence, 5(4), pp. 349-371.
  • Fetita C.I., Prêteux F., 1999: Three-Dimensional Reconstruction of Human Bronchial Tree In HRCT. Proceedings SPIE, 3646, pp. 281-295.
  • Fetita C.I., Prêteux F., 2002: Quantitative 3D CT Bronchography. ISBI’2002, Proceedings of 1st IEEE International Symposium on Biomedical Imaging, pp. 221-224.
  • Fetita C.I., Prêteux F., Beigelman-Aubry C., Grenier P., 2004: Pulmonary Airways: 3-D Reconstruction from Multislice CT and Clinical Investigation. IEEE Transactions on Medical Imaging, 23(11), pp. 1353-1364.
  • Felzenszwalb P.F., Huttenlocher D.P., 2004: Efficient Graph Based Image Segmentation. International Journal of Computer Vision, 59(2), pp. 167-181.
  • Foppa N., Stoffel A., Meister R., 2007: Synergy of in Situ and Spaceborne Observation for Snow Depth Mapping in the Swiss Alps. International Journal of Applied Earth Observation and Geoinformation, 9(3), pp. 294-310.
  • Ford, L.R., Fulkerson, D.R., 1956. Maximal Flow Through a Network. Canadian Journal of Mathematics, 8, pp. 399-404.
  • Frangi A. F., Niessen W. J., Vincken K. L., Viergever M. A., 1998: Multiscale Vessel Enhancement Filtering. Lecture Notes in Computer Science, 1496, pp. 130-137.
  • Freedman D., Zhang T., 2005: Interactive Graph Cut Based Segmentation With Shape Priors. CVPR’2005, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1, pp. 75-762.
  • Frei W., Chen C.C, 1977: Fast Boundary Detection: A Generalization and a New Algorithm. IEEE Transactions on Computers, 26(10), pp. 988-998.
  • Freixenet J., Muñoz X., Raba D., Martí J., Cufí X, 2002: Yet Another Survey on Image Segmentation: Region and Boundary Information Integration. Lecture Notes in Computer Science, 2352, pp. 21-25.
  • Fu K.S., Mui J.K., 1981: A Survey on Image Segmentation. Pattern Recognition, 13(1), pp. 3-16.
  • Fukunaga K., Hostetler L.D., 1975: The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Transactions on Information Theory, 21(1), pp. 32-40.
  • Gambotto J.P., 1993: A New Approach to Combining Region Growing and Edge Detection. Pattern Recognition Letters, 14 (11), pp. 869-875.
  • Gao D., Gao X., Ni C., Zhang T., 2011: MGRG-Morphological Gradient Based 3D Region Growing Algorithm for Airway Tree Segmentation in Image Guided Intervention Therapy. ISBB’2011, Proceedings of International Symposium on Bioelectronics and Bioinformatics, pp. 76-79.
  • Gao W., Zhang X., Yang L., Liu H., 2010: An Improved Sobel Edge Detection. ICCSIT’10, Proceedings of IEEE International Conference on Computer Science and Information Technology, pp. 67-71.
  • Gauch J.M., 1999: Image Segmentation and Analysis via Multiscale Gradient Watershed Hierarchies. IEEE Transactions on Image Processing, 8(1), pp. 69-79.
  • Gevers T., Smeulders A.W.M., 1997: Combining Region Splitting and Edge Detection Through Guided Delaunay Image Subdivision. CVPR’97, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1021-1026.
  • Ghosal S., Mehrotra R., 1992: Edge Detection Using Orthogonal Moment-Based Operators. IAPR’1992, Proceedings of 11th International Conference on Pattern Recognition, 3, pp. 413-416.
  • Ghosal S., Mehrotra R., 1993: Orthogonal Moment Operators for Subpixel Edge Detection. Pattern Recognition Letters, 26(2), pp. 295-305.
  • Ghosal S., Mehrotra R., 1994a: Detection of Composite Edges. IEEE Transactions on Image Processing, 3(1), pp. 14-25.
  • Ghosal S., Mehrotra R., 1994b: Zernike Moment-Based Feature Detectors. ICIP’1994, Proceedings of International IEEE Conference on Image Processing, 1, pp. 934-938.
  • Giordano F., Goccia M., Dellepiane S., 2005: Segmentation of Coherence Maps for Flood Damage Assessment. ICIP’2005, Proceedings of International Conference on Image Processing, pp. 233-236.
  • Gonzalez R.C., Woods, R.E., 2008: Digital Image Processing, 3rd Edition. Pearson-Prentice Hall.
  • Gomory R.E., Hu T.C., 1961: Multi Terminal Network Flows. SIAM Journal on Applied Mathematics, 9, pp. 551-570.
  • Grabowski S., Bieniecki W., 2003: A Two-Pass Median Filter for Impulse Noise Attenuation in Color Images. CADSM’2003, Proceedings of7th International Conference on CAD Systems in Microelectronics, pp. 101-104.
  • Grady L., Funka-Lea G., 2004: Multi-Label Image Segmentation for Medical Applications Based on Graph-Theoretic Electrical Potentials. CVAMIA’2004, Proceedings of the 8th ECCV Workshop on Computer Vision Approaches to Medical Image Analysis and Mathematical Methods in Biomedical Image Analysis, pp. 230-245.
  • Grady L., 2005: Multilabel Random Walker Image Segmentation Using Prior Models. CVPR’2005, Proceedings International Conference on Computer Vision and Pattern Recognition 1, pp. 763-770.
  • Grady L., 2006: Random Walks for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(11), pp. 1768-1783.
  • Graham M.W., Gibbs J.D., Higgins W.E., 2008. Robust System for Human Airway-Tree Segmentation. Proceedings SPIE, 6914, pp. 69141J-1-69141J-18.
  • Graham M.W., Gibbs J.D., Cornish D.C., Higgins W.E., 2010: Robust 3D Airway Tree Segmentation for Image-Guided Peripheral Bronchoscopy. IEEE Transactions on Medical Imaging, 29(4), pp. 982-997.
  • Grau V., Mewes A.U.J., Alcaniz M., Kikinis R., Warfield S.K., 2004: Improved Watershed Transform for Medical Image Segmentation Using Prior Information. IEEE Transactions on Medical Imaging, 23(4), pp. 447-458.
  • Grenier T., Revol-Muller C., Costes N., Janier M., Gimenez G., 2006: 3D Robust Adaptive Region Growing for Segmenting [18F] Fluoride Ion PET Images. Proceedings of IEEE Nuclear Science Symposium Conference Record, 5, pp. 2644-2648.
  • Grinstead C.M., Snell J.L., 2006: Introduction to Probability. American Mathematical Society, dostęp on-line: http://www.math.dartmouth.edu/~prob/prob/prob.pdf Gross J., Yellen J., 1998: Graph Theory and Its Applications. CRC Press.
  • Guha A., Amarnath C., Pateria S., Mittal R., 2009: Measurement of Yarn Hairiness by Digital Image Processing. Journal of the Textile Institute, 99(6), pp. 1754-2340.
  • Gunn S.R., Nixon M.S., 1994: A Model Based Dual Active Contour. BMVC’94, Proceedings of British Machine Vision Conference, pp. 305-314.
  • Gunn S. R., Nixon M. S., 1997: A Robust Snake Implementation: A Dual Active Contour. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(1), pp. 63-68.
  • Guo R., Pandit S.M., 1998: Automatic Threshold Selection Based on Histogram Modes and a Discriminant Criterion. Machine Vision and Applications, 10, pp. 331-338.
  • Gupta G., Psarrou A., Angelopoulou A., 2012: Image Segmentation Based on Semi-Greedy Region Merging. IET-IPR’2012, Proceedings of IET Image Processing Conference, pp.1-4.
  • Haddon, J.F., Boyce, J.F., 1990: Image Segmentation by Unifying Region and Boundary Information. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10), pp. 929-948.
  • Hagen L., Kahng A., 1992: New Spectral Methods for Ratio Cut Partitioning and Clustering. IEEE Transactions on Computer-Aided Design, 11(9), 1074-1085.
  • Halada L., Osokov G.A., 1987: Histogram Concavity Analysis by Quasi Curvature. Computer Artificial Intelligence, 6, pp. 523-533.
  • Hamarneh G., Yang J., McIntosh C., Langille M., 2005. 3D Live-Wire-Based Semiautomatic Segmentation of Medical Images. Proceedings of the SPIE Medical Imaging: Image Processing, 5747, pp. 1597-1603.
  • Hansen F.K., 1993: Surface Tension by Image Analysis: Fast and Automatic Measurements of Pendant and Sessile Drops and Bubbles. Journal of Colloid and Interface Science, 160, pp. 209-217.
  • Hao X., Bruce C., Pislaru C., Greenleaf J.F., 2000: A Novel Region Growing Method for Segmenting Ultrasound Images. IUS’2000, Proceedings of IEEE International Ultrasonics Symposium, 2, pp. 1717-1720.
  • Haralick R.M., 1984: Digital Step Edges from Zero-Crossing of Second Directional Derivatives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(1), pp. 58-68.
  • Haralick R.M., Shapiro L.G., 1985: Image Segmentation Techniques. Computer Vision Graphics and Image Processing, 29(1), pp. 100-132.
  • Haris K., Efstratiadis S.N., Maglaveras N., Katsaggelos A.K., 1998: Hybrid Image Segmentation Using Watersheds and Fast Region Merging. IEEE Transactions on Image Processing, 7(12), pp. 1684-1699.
  • Hartigan J.A., Wong M.A., 1979: A K-Means Clustering Algorithm. Applied Statistics, 28(1), pp. 100-108.
  • Hertz L., Schafer R.W., 1988: Multilevel Thresholding Using Edge Matching. Computer Vision Graphics and Image Processing, 44, pp. 279-295.
  • Hijjatoleslami S.A., Kittler J., 1998: Region Growing: A New Approach. IEEE Transactions on Image Processing, 7(7), pp. 1079-1084.
  • Hoiem D., Rother C., Winn J., 2007: 3D Layout CRF for Multi-View Object Class Recognition and Segmentation. CVPR’2007, Proceedings of IEEE Computer Vision and Pattern Recognition, pp. 1-8.
  • Hong L., Wan Y., Jain A., 1998: Fingerprint Image Enhancement: Algorithm and Performance Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), pp. 777-789.
  • Horowitz S.L., Pavlidis T., 1974: Picture Segmentation by a Directed Split and Merge Procedure. ICPR’74, Proceedings of International Conference on Pattern Recognition, pp. 424-433.
  • Horowitz S.L., Pavlidis T., 1976: Picture Segmentation by a Tree Traversal Algorithm. Journal of the ACM, 23, pp. 368-388.
  • Huang Z.K., Chau K.W., 2008: A New Image Thresholding Method Based on Gaussian Mixture Model. Applied Mathematics and Computation, 205(2), pp. 899-907
  • Huart J., Bertolino P., 2005: Similarity-Based and Perception-Based Image Segmentation, ICIP’05, Proceedings of IEEE International Conference on Image Processing, pp. 1148-1151.
  • Hueckel M.F., 1971: An Operator which Locates Edges in Digitized Pictures. Journal of ACM, 18(1), pp. 113-125.
  • Hueckel M.F., 1973: A Local Visual Operator which Recognizes Edges and Lines. Journal of ACM, 20(4), pp. 634-647.
  • Hueckel M.F., 1974: Erratum: A Local Visual Operator which Recognizes Edges and Lines. Journal of ACM, 21(2), pp. 350.
  • Huertas A., Medioni G., 1986: Detection Of Intensity Changes Using Laplacian-Gaussian Masks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8, pp. 651-664.
  • Huh C., Reed R.L., 1983: A Method for Estimating Interfacial Tensions and Contact Angles From Sessile Aand Pendant Drop Shapes. Journal of Colloid and Interface Science, 9, pp. 1472-1484.
  • Hung, M.C., Ridd, M., 2002: A Subpixel Classifier for Urban Land-Cover Mapping Based on a Maximum-Likelihood Approach and Expert System Rules. Photogrammetric, Engineering & Remote Sensing, 68, pp. 1173-1180.
  • Ilea D.E., Whelan P.F., 2011: Image Segmentation Based on the Integration of Colour-Texture Descriptors-A Review. Pattern Recognition, 44(10-11), pp. 2479-2501.
  • Jabłoński B., 2007: Równania Różniczkowe Cząstkowe w Problemach Filtracji Obrazów i Trajektorii Przestrzennych. Rozprawa doktorska, Politechnika Wrocławska, Instytut Informatyki, Automatyki i Robotyki, dostęp on-line pod adresem: http://www.dbc.wroc.pl/dlibra/docmetadata?id=1925&from=FBC
  • Jackowski T., Chylewska B., Cyniak D., 1994: The Hairiness of Yarns Cotton and Cotton Type Fibres. Fibres & Textiles in Eastern Europe, 2, pp. 22-23.
  • Jackson M., Acar M., Siong L.Y., Whitby D., 1995: A Vision Based Yarn Scanning System. Mechatronics, 5(2/3), pp. 133-146.
  • Jachimski J., Mikrut S., 1998: Próba Subpikselowej Lokalizacji Linii Konturowych z Wykorzystaniem Drugiej Pochodnej Obrazu Cyfrowego. Archiwum Fotogrametrii, Kartografii i Teledetekcji, 8, Kraków.
  • Jain A.K., 1988: Fundamentals of Digital Image Processing. Prentice Hall.
  • Jawahar C.V., Biswas P.K., Ray A.K., 1997: Investigations on Fuzzy Thresholding Based on Fuzzy Clustering. Pattern Recognition, 30(10), pp. 1605-1613.
  • Jähne B., 2012: Digital Image Processing. Springer.
  • Jermyn I., Ishikawa H., 2001: Globally Optimal Region and Boundaries as Minimum Ratio Weight Cycles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, pp. 1075-1088.
  • Jeżewski S., 2006: Model Oświetlenia Wysokotemperaturowego w Zagadnieniach Przetwarzania Obrazu Próbek na Granicy Fazy Stałej i Ciekłej. Rozprawa doktorska. Kraków, Akademia Górniczo-Hutnicza.
  • Ji X., Wang K., Wei Z., 2009: Structured Light Encoding Research Based on Sub-Pixel Edge Detection. ICIECS’2009, Proceedings of International Conference on Information Engineering and Computer Science, pp. 1-4.
  • Jiang H., Toriwaki J., Suzuki H., 1993: Comparative Performance Evaluation of Segmentation Methods Based on Region Growing and Division. Systems and Computers in Japan, 24(13), pp. 28-42.
  • Jin J.S., 1990: An Adaptive Algorithm for Edge Detection with Subpixel Accuracy in Noisy Images. Proceedings of IAPR Workshop on Machine Vision Applications, pp. 249-252.
  • Jung C., Kim C., 2010: Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization. IEEE Transactions on Biomedical Engineering, 57(10), pp. 2600-2604.
  • Justice R.K., Stokely E.M., Strobel J.S., Ideker R.E., Smith W.M., 1997: Medical Image Segmentation Using 3D Seeded Region Growing. Proceedings of SPIE-The International Society for Optical Engineering, 3034, pp. 900-910.
  • Kaftan J.N., Kiraly A.P., Naidich D.P., Novak C.L., 2006: A Novel Multipurpose Tree and Path Matching Algorithm with Application to Airway Trees. Proceedings SPIE Medical Imaging, 6143, pp. 215-224.
  • Kampke T., Kober R., 1998: Nonparametric Optimal Binarization. ICPR’98, Proceedings of International Conference on Pattern Recognition, pp. 27-29.
  • Kapur J.N., Sahoo P.K., Wong A.K.C., 1985: A New Method For Gray-Level Picture Thresholding Using the Entropy of the Histogram. Graphical Models and Image Processing, 29, pp. 273-285.
  • Kass M., Witkin A., Terzopoulos D., 1988: Snakes: Active Contour Models. International Journal of Computer Vision, 1(4), pp. 321-331.
  • Kelkar D., Gupta S., 2008: Improved Quadtree Method for Split Merge Image Segmentation. ICETET’2008, Proceedings of First International Conference on Emerging Trends in Engineering and Technology, pp. 44-47.
  • Kiraly A.P., Higgins W.E., McLennan G., Hoffman E.A., Reinhardt J.M., 2002: Three-Dimensional Human Airway Segmentation Methods for Clinical Virtual Bronchoscopy. Academic Radiology, 9(10), pp. 1153-1168.
  • Kittler J., Illingworth J., 1985: On Threshold Selection Using Clustering Criteria. IEEE Transactions on System Man and Cybernetics, 15, pp. 652-655.
  • Kimia B.B., Tannenbaum A.R., Zucker S. W., 1995: Shapes, Shocks, and Deformations I: The Components of Two-Dimensional Shape and the Reaction-Diffusion Space. International Journal of Computer Vision, 15, pp. 189-224.
  • Kimmel R., Bruckstein A.M., 2003: Regularized Laplacian Zero Crossings as Optimal Edge Integrators. International Journal of Computer Vision 53(3), pp. 225-243.
  • Kittler J., Illingworth J., 1986: Minimum Error Thresholding. Pattern Recognition, 19, pp. 41-47.
  • Kirsch R., 1971: Computer Determination of the Constituent Structure of Biological Images. Computers and Biomedical Research, 4, pp. 315-328.
  • Kisworo M., Venkatesh S., West G., 1991: 2-D Edge Feature Extraction to Subpixel Accuracy Using The Generalized Energy Approach. TENCON’1991, Proceedings of IEEE Region 10 International Conference on EC3-Energy, Computer, Communication and Control Systems, pp. 344-348.
  • Kisworo M., Venkatesh S., West G.A., 1999: Detection of Curved Edges at Subpixel Accuracy Using Deformable Models. IEEE Proceeding Vision, Image & Signal Processing, 142(5), pp. 304-311.
  • Knapp M., Kanitsar A., Gröller M.E., 2004: Semi-Automatic Topology Independent Contour- Based 2 ½ D Segmentation Using Live-Wire. Journal of WSCG, 12(2), pp. 229-236.
  • Kolmogorov V., Zabih R., 2004: What Energy Functions Can Be Minimized Via Graph Cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(2), pp. 147-159.
  • Kolmogorov V., Boykov Y., 2005: What Metrics Can Be Approximated by Geo-Cuts, or Global Optimization of Length/Area and Flux. ICCV’2005, Proceedings of International Conference on Computer Vision, 1, pp. 564-571.
  • Korzyńska A., Przytulska M., 2005: Przetwarzanie Obrazów-Ćwiczenia. Wydawnictwo Polsko- Japońskiej Wyższej Szkoły Technik Komputerowych.
  • Koszmider T., 2009: Zintegrowany System Komputerowy do Pomiaru Cech Geometrycznych Próbek Metali i ich Stopów Znajdujących się w Wysokich Temperaturach. Rozprawa doktorska. Łódź, Politechnika Łódzka.
  • Koszmider T., Bąkała M., Fabijańska A., Strzecha K., 2010a: Methods for Reduction of Thermal Effects for Analysis of Images Presenting Melted Specimens of Metals and Alloys. MEMSTECH’2010, Proceedings of IEEE 6th International Conference Perspective Technologies and Methods in MEMS Design, pp. 35-37.
  • Koszmider T., Bąkała M., Fabijańska A., Strzecha K., 2010b: Experimental Comparison of Segmentation Algorithms on Images of Heat-Emitting Objects and Methods for Their Accuracy Improvement. MEMSTECH’2010, Proceedings of IEEE 6th International Conference Perspective Technologies and Methods in MEMS Design, pp. 38-39.
  • Koszmider T., Strzecha K., Fabijańska A., Bąkała M., 2011a: Algorithm for Accurate Determination of Contact Angles in Vision System for High Temperature Measurements of Metals and Alloys Surface Properties. Computer Recognition Systems 4, Advances in Intelligent and Soft Computing, 95, pp. 441-448.
  • Koszmider T., Bąkała M., Fabijańska A., Strzecha K., 2011b: An Improved Method for Contact Angles Determination from Images of Heat-Emitting Objects. MEMSTECH’2011, Proceedings of IEEE 7th International Conference Perspective Technologies and Methods in MEMS Design, pp. 133-134.
  • König S., Hesser J., 2005: 3D Live-Wires on Pre-Segmented Volume Data. SPIE Medical Imaging: Image Processing, pp. 1674-1679.
  • Kremser K., Plangger C., Bosecke R., Pallua A., Aichner F., Felber R., 1997: Image Registration of MR and CT Images Using Frameless Fiducial Marker System, Magnetic Resonance Imaging, 15(5), pp. 579-588.
  • Krissian K., Malandain G., Ayache N., Vaillant R., Trousset Y., 2000: Model-Based Detection of Tubular Structures in 3D Images. Computer Vision and Image Understanding, 80(2), pp. 130-171.
  • Kundra H., Aashima, Verma M., 2009: Image Enhancement Based On Fuzzy Logic. International Journal of Computer Science and Network Security, 9(10), pp. 141-145.
  • Kuzański M., Jackowska-Strumiłło L., 2005: Yarn Hairiness Determination by the Use of Image Processing and Analysis Versus Classical Methods. CADSM’2005, Proceedings of IEEE International Conference The Experience of Designing and Application of CAD Systems in Microelectronics, pp. 405-407.
  • Kuzański M., 2006: Measurement Methods for Yarn Hairiness Analysis-The Idea and Construction of Research Standing. MEMSTECH’2006, Proceedings of IEEE International Conference on Perspective Technologies and Methods in MEMS Design, pp. 87-90.
  • Kuzański M., Jackowska-Strumiłło L., 2007: Yarn Hairiness Determination-The Algorithms of Computer Measurement Methods. MEMSTECH’2007, Proceedings of IEEE International Conference on Perspective Technologies and Methods in MEMS Design, pp. 154-157.
  • Kuzański M., Fabijańska A., Sankowski D., Jackowska-Strumiłło L., 2008: Machine Vision- Automation of Selected Measurement Systems. MEMSTECH’2008, Proceedings of IEEE 4th International Conference on Perspective Technologies and Methods in MEMS Design, pp. 65-68.
  • Kwok S.H., Constantinides A.G., 1997: A Fast Recursive Shortest Spanning Tree for Image Segmentation and Edge Detection. IEEE Transactions on Image Processing, 6(2), 1997, pp. 328-332.
  • Lawler G.F, Limic V., 2010: Random Walk: A Modern Introduction. Cambridge University Press.
  • Lee C.H., 1986: Recursive Region Splitting at Hierarchical Scope Views. Computer Vision, Graphics and Image Processing, 33(2), pp. 237-258.
  • Lee W.C., Chen H.C., 2008: Subpixel Edge Location Using Orthogonal Fourier-Mellin Moments Based Edge Location Error Compensation Model. ISDA’2008, Proceedings of 8th International Conference on Intelligent Systems Design and Applications, pp. 346-351.
  • Lei Y., Jiafa N., 2008: Subpixel Edge Detection Based on Morphological Theory, Proceedings of World Congress on Engineering and Computer Science, pp. 1195-1198.
  • Lemkin P., 1979: An Approach to Region Splitting. Computer Graphics and Image Processing, 10(3), pp. 281-288.
  • Lempitsky V., Kohli P., Rother C., Sharp T., 2009: Image Segmentation with A Bounding Box Prior. ICCV’2009, Proceedings of IEEE 12th International Conference on Computer Vision, pp. 277-284.
  • Leung C.K., Lam F.K., 1996: Performance Analysis of a Class of Iterative Image Thresholding Algorithms. Pattern Recognition, 29(9), pp. 1523-1530.
  • Levner I., Zhang H., 2007: Classification-Driven Watershed Segmentation. IEEE Transactions on Image Processing, 16(5), pp.1437-1445.
  • Li C.H., Lee C.K., 1993: Minimum Cross-Entropy Thresholding. Pattern Recognition, 26, pp. 617-625.
  • Li C.H., Tam P.K.S., 1998: An Iterative Algorithm for Minimum Cross-Entropy Thresholding. Pattern Recognition Letters, 19, pp. 771-776.
  • Li D., Zhang G., Wu Z., Yi L., 2010: An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation. IEEE Transactions on Image Processing, 19(10), pp. 2781-2787.
  • Li K., Wu X., Chen D.Z., Sonka M.: Optimal Surface Segmentation in Volumetric Images- A Graph-Theoretic Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(1), pp. 119-134.
  • Li J. Q., Wang J.W., Chen S.B., Wu L., 2003: Improved Algorithm of Subpixel Edge Detection Using Zernike Orthogonal Moments. Optical Technique, 29(4), pp. 500-503.
  • Li Y., Sun J., Tang C. K., Shum H. Y., 2004: Lazy Snapping. ACM Transaction on Graphics, 23(3), pp. 303-308.
  • Li Y., Belkasim S., Chen X., Edwards D., Antonsen B., 2006: Fuzzy Contour Matching for 3D Reconstruction and Retrieval. FUZZ-IEEE’2006, Proceedings on IEEE International Conference on Fuzzy Systems, pp. 1287-1291.
  • Liang T.K., Tanaka T., Nakamura H., Shirahata T., Sugiura H., 2009: Segmentation of Airway Trees from Multislice CT using Fuzzy Logic, ASILOMAR’2009, Proceedings of Asilomar Conference on Signals, Systems and Computers, pp. 1614-1617.
  • Lipski W., 2004: Kombinatoryka dla Programistów. Wydanie III, rozszerzone. Wydawnictwa Naukowo-Techniczne WNT.
  • Liu C., Xia Z., Niyokindi S., Pei W., Song J., Wang L., 2004: Edge Location to Sub-Pixel Value in Color Microscopic Images. ICIMA’2004, Proceedings International Conference on Intelligent Mechatronics and Automation, pp. 548-551.
  • Liu J., Sun J., Shum H. Y., 2009: Paint Selection. ACM Transactions on Graphics (SIGGRAPH), 28(3), pp. 69:1-69:8.
  • Liu Y., Fenrich R., Srihari S.N., 1993: An Object Attribute Thresholding Algorithm for Document Image Binarization. ICDAR’93, Proceedings of 2nd International Conference on Document Analysis and Recognition, pp. 278-281.
  • Liu Y., Srihari N., 1994: Document Image Binarization Based on Texture Analysis. Proceedings SPIE, 2181, pp. 254-263.
  • Liu X., Veksler O., Samarabandu J., 2008: Graph Cut with Ordering Constraints on Labels and its Applications. CVPR’2008, Proceedings of IEEE Computer Vision and Pattern Recognition, pp. 1-8.
  • Ljosa V., Singh A.K., 2006: Probabilistic Segmentation and Analysis of Horizontal Cells. ICDM’2006, Proceedings of the 6th International Conference on Data Mining, pp. 1-6.
  • Lo P., de Brujine M., 2008: Voxel Classification Based Airway Tree Segmentation. Proceedings SPIE Medical Imaging, 6914, pp. 69141k-69141k-12.
  • Lo P. van Ginneken B., Reinhardt J., de Bruijne M., 2009: Extraction of Airways from CT (EXACT’09). Second International Workshop on Pulmonary Image Analysis, pp. 175-189.
  • Lo P., Sporring J., Haseem A., Pedersen J., de Bruijne M., 2010: Vessel-Guided Airway Tree Segmentation: A Voxel Classification Approach. Medical Image Analysis, 14, pp. 527-538.
  • Lo P., van Ginneken B., Reinhardt J. M., Yavarna T., de Jong P.A., Irving B., Fetita C., Ortner M., Pinho R., Sijbers J., Feuerstein M., Fabijańska A., Bauer C., Beichel R., Mendoza C.S., Zayed S., Wiemker R., Lee J., Reeves A.P., Born S., Weinheimer O., van Rikxoort E.M., Tschirren J., Mori K., Odry B., Naidich D.P., Hartmann I., Hoffman E.A., Prokop M., Pedersen J.H., de Bruijne M., 2012: Extraction of Airways from CT (EXACT’09). IEEE Transactions on Medical Imaging, 31(8), w druku.
  • Lu Y., Jiang T., Zang Y., 2003: Region Growing Method for the Analysis of Functional MRI Data. NeuroImage, 20(1), pp. 455-465.
  • Lyvers E.P., Mitchell O.R., Akey M.L., Reeves A.P., 1989: Subpixel Measurements Using a Moment-Based-Edge Operator. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(12), pp. 1293-1309.
  • Machuca R., Gilbert A.L.,1981: Finding Edges in Noisy Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3, pp. 103-111.
  • MacQueen J.B., 1967: Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, pp. 281-297.
  • MacVicar-Whelan P.J., Binford T.O., 1981a: Line Finding with Subpixel Precision. Proceedings of DARPA Image Understanding Workshop, pp. 26-31.
  • MacVicar-Whelan P.J., Binford T.O., 1981b: Intensity Discontinuity Location to Subpixel Precision. IJCAI’1981, Proceedings of International Joint Conference on Artificial Intelligence, pp. 752-754.
  • Malina W., Ablameyko S., Pawlak W., 2002: Podstawy Cyfrowego Przetwarzania Obrazów. Wydawnictwo EXIT, Warszawa.
  • Malina W., Smiatacz M., 2005: Metody Cyfrowego Przetwarzania Obrazów. Wydawnictwo EXIT, Warszawa.
  • Malkiel B.G., 2011: A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing, W. W. Norton & Company.
  • Malladi R., Sethian J.A., Vemuri B.C., 1995: Shape Modeling With Front Propagation: A Level Set Approach. IEEE Transactions in Pattern Analysis and Machine Intelligence, 17(2), pp. 158-175.
  • Mancas M., Gosselin B., Macq B., 2005: Segmentation Using a Region-Growing Thresholding. Proceedings of the SPIE, 5672, pp. 388-398.
  • Markus T., Powell D.C., Wang J.R., 2006: Sensitivity of Passive Microwave Snow Depth Retrievals to Weather Effects and Snow Evolution. IEEE Transactions on Geoscience and Remote Sensing, 44(1), pp. 68-77.
  • Marr D., 1976: Early Processing of Visual Information. Artificial Intelligence Lab Publications, AIM-340.
  • Marr D., Hildreth E., 1980: Theory of Edge Detection. Proceedings of the Royal Society of London. Series B, Biological Sciences, 207(1167), pp. 187-217.
  • Martin D., Fowlkes C., Tal D., Malik J., 2001: A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. Proceedings of 8th International Conference on Computer Vision, 2, pp.416-423.
  • Maška M., Hubený J., Svoboda, D., Kozubek M., 2007: A Comparison of Fast Level Set-Like Algorithms for Image Segmentation in Fluorescence Microscopy. Lecture Notes in Computer Science, 4842, pp. 571-581.
  • Materka A., 1991: Elementy Cyfrowego Przetwarzania i Analizy Obrazów. PWN, Warszawa.
  • Materka A., Strumiłło P., 2009: Wstęp do Komputerowej Analizy Obrazów. Wydawnictwo Politechniki Łódzkiej.
  • Mayer D., Bartz D., Ley S., Thust S., Heussel C.P., Kauczor H.U., Straßer W., 2003a: Segmentation and Virtual Exploration of Tracheobronchial Trees. CARS’2003, Proceedings of 17th International Congress and Exhibition Computer Aided Radiology and Surgery, pp. 35-40.
  • Mayer D., Bartz D., Fischer J., Ley S., del Río A., 2003b: Hybrid Segmentation and Virtual Bronchoscopy Based on CT Images. Academic Radiology, 11(5), pp. 551-565.
  • Mayer D., Bartz D., Fischer J., Ley S., del Rio A., Thust S., Kauczor H. U., Heussel C. P., 2004: Hybrid Segmentation and Virtual Bronchoscopy Based on CT Images. Academic Radiology, 11, pp. 551-565.
  • Mazonakis M., Damilakis J., Varveris H., Prassopoulos P., Gourtsoyiannis N., 2001: Image Segmentation in Treatment Planning for Prostate Cancer Using the Region Growing Technique. British Journal of Radiology, 74(879), pp. 243-248.
  • McAteer R.T.J., Gallagher P.T., Ireland J., Young C.A., 2005: Automated Boundary-Extraction and Region-Growing Techniques Applied to Solar Magnetograms. Solar Physics, 228 (1-2), pp. 55-66.
  • McInerney T., Terzopoulos D., 1995: Topologically Adaptable Snakes. ICCV’1995, Proceedings of International Conference on Computer Vision, pp. 840-845.
  • Mehnert A., Jackway P., 1997: An Improved Seeded Region Growing Algorithm. Pattern Recognition Letters, 18(10), pp. 1065-1071.
  • Meila M., Shi J., 2000: Learning Segmentation by Random Walks. Advances in Neural Information Processing Systems, pp. 873-879.
  • Metternich G., Hurni L., Gogu R., 2005: Remote Sensing of Landslides: An Analysis of the Potential Contribution to Geospatial Systems for Hazard Assessment in Mountainous Environments. Remote Sensing of Environment, 98(2-3), pp. 284-303.
  • Meyer F., Beucher S., 1990: Morphological Segmentation. Journal of Visual Communication and Image Representation, 1(1), pp. 21-46.
  • Meyer F., 1994: Topographic Distance and Watershed Lines. Signal Processing, 38, pp. 113-125.
  • Mikuła K., Sarti A., Sgallari F., 2005: Co-Volume Level Set Method in Subjective Surface Based Medical Image Segmentation. In: Wilson D., Laxminarayan S. (Eds.): Handbook of Biomedical Image Analysis: Volume 1: Segmentation Models Part A, Springer.
  • Mills K.C., Su Y.C., 2006, Review of Surface Tension Data for Metallic Elements and Alloys: Part 1-Pure Metals, International Materials Reviews, 51(6), pp. 329-351.
  • Misiak D., Posch S., Stohr D., Huttelmaier S., Moller B., 2009: Automatic Analysis of Flourescence Labeled Neurites in Microscope Images. WACV’2009, Proceedings of Workshop on Applications of Computer Vision, pp. 1-7.
  • Mobahi H., Rao S., Yang A., Sastry S., Ma Y., 2011: Segmentation of Natural Images by Texture and Boundary Compression. International Journal of Computer Vision, 95(1), pp. 86-98.
  • Morris O.J., Lee M.J., Constantinides A.G., 1986: Graph Theory for Image Analysis: An Approach Based on the Shortest Spanning Tree. IEE Proceedings-F. Communications Radar & Signal Processing, 133, pp. 146-152.
  • Mortensen E.N., Barrett W.A., 1995: Intelligent Scissors for Image Composition. SIGGRAPH ‘95: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, pp. 191-198.
  • Mortensen E.N., Barrett W.A., 1998: Interactive Segmentation with Intelligent Scissors. Graphical Models and Image Processing, 60, pp. 349-384.
  • Muñoz X., Freixenet J., Cufı́ X., Martı́ J., 2003: Strategies for Image Segmentation Combining Region and Boundary Information. Pattern Recognition Letters, 24,(1-3), pp. 375-392.
  • Murthy C.A., Pal S.K., 1990: Fuzzy Thresholding: A Mathematical Framework, Bound Functions and Weighted Moving Average Technique. Pattern Recognition Letters, 11, pp. 197-206.
  • Nadernejad E., Sharifzadeh S., Hassanpour H., 2008: Edge Detection Techniques: Evaluations and Comparisons. Applied Mathematical Sciences, 2(31), pp. 1507-1520.
  • Najman L., Schmitt M., 1996: Geodesic Saliency of Watershed Contours and Hierarchical Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(12), pp. 1163-1173.
  • Nakano Y., Muro S., Sakai H., Hirai T., Chin K., Tsukino M., Nishimura K., Itoh H., Paré P.D., Hogg J.C., Mishima M., 2000: Computed Tomographic Measurements of Airway Dimensions and Emphysema in Smokers Correlation with Lung Function. American Journal of Respiratory and Critical Care Medicine, 162(3), pp. 1102-1108.
  • Nakagawa Y., Rosenfeld A., 1979: Some Experiments on Variable Thresholding. Pattern Recognition, 11(3), pp. 191-204.
  • Nevtia R., Babu K., 1978: Linear Feature Extraction. Proceedings DARPA Image Understanding Workshop, pp. 73-78.
  • Niblack W., 1986: An Introduction to Image Processing, Prentice-Hall, pp. 115-116.
  • O’Gorman L., 1994: Binarization and Multithresholding of Document Images Using Connectivity. Graphical Models and Image Processing, 56, pp. 494-506.
  • Ohlander R., Price K., Reddy, D.R., 1979: Picture Segmentation Using a Recursive Region Splitting Method. Computer Graphics and Image Processing, 8(3), pp. 313-333.
  • Olivo J.C., 1994: Automatic Threshold Selection Using the Wavelet Transform. Graphical Models and Image Processing, 56, pp. 205-218.
  • Osher S., Sethian J., 1988: Fronts Propagating With Curvature Dependent Speed: Algorithms Based on the Hamilton-Jacobi Formulation. Journal of Computational Physics, 79, pp. 12-49.
  • Osher S., Fedkiw R., 2001: Level Set Methods: An Overview and Some Recent Results. Journal of Computational Physics, 169, pp. 463-502.
  • Osher S., Fedkiw R., 2003: Level Set Methods and Dynamic Implicit Surfaces. Springer, New York, USA.
  • Ostrovsky R., Rabani Y., Schulman L., Swamy C., 2006: The Effectiveness of Lloyd-Type Methods for the k-Means Problem. FOCS’2006, Proceedings of 47th IEEE Symposium on the Foundations of Computer Science, pp. 165-174.
  • Otsu N., 1979: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on System Man and Cybernetics, 9, pp. 62-66.
  • Ozkaya, Y.A., Acar, M., Jackson, M.R., 2005: Digital Image Processing and Illumination Techniques for Yarn Characterization. Journal of Electronic Imaging, 14(2), pp.
  • Pal S.K., Rosenfeld A., 1988: Image Enhancement and Thresholding by Optimization of Fuzzy Compactness. Pattern Recognition Letters, 7, pp. 77-86.
  • Pal N.R., Pal S.K., 1993: A Review on Image Segmentation Techniques. Pattern Recognition, 26(9), pp. 1277-1294.
  • Pal N.R., 1996: On Minimum Cross-Entropy Thresholding. Pattern Recognition, 29(4), pp. 575-580.
  • Pan Z., Lu J., 2007: A Bayes-Based Region-Growing Algorithm for Medical Image Segmentation. Computing in Science and Engineering, 9(4), pp. 32-38.
  • Pantofaru C., Hebert M., 2005: A Comparison of Image Segmentation Algorithms. tech. report CMU-RI-TR-05-40, Robotics Institute, Carnegie Mellon University.
  • Park W., Hoffman E.A., Sonka M., 1998: Segmentation of Intrathoracic Airway Trees: A Fuzzy Logic Approach. IEEE Transactions on Medical Imaging, 17(4), pp. 489-497.
  • Passat N., Ronse C., Baruthio J., Armspach J.P., Maillot C., Jahn C., 2005: Region-Growing Segmentation of Brain Vessels: An Atlas-Based Automatic Approach. Journal of Magnetic Resonance Imaging, 21(6), pp. 715-725.
  • Patras I., Hendriks E.A., Lagendijk R.L., 2001: Video Segmentation by MAP Labeling of Watershed Segments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(3), pp. 326-332.
  • Pavlidis T., Liow Y. T., 1990: Integrating Region Growing and Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (3), pp. 225-233.
  • Pawluk P., Fabijańska A., 2011: Edge Detection in Images of Heat-Emitting Objects. NOTICE’2011, Proceedings of 2nd International Conference on Novel Computer Science & Technology Applications, pp. 56-60.
  • Pawluk P., Fabijańska A., 2012: Algorithm for Subpixel Edge Detection in Images of Heat- Emitting Objects. w Romanowski A., Sankowski D. (Eds): Computer Science in Novel Applications, Technical University of Lodz Press, Monograph Series, w druku.
  • Pearson k., 1905: The problem of the Random Walk. Nature. 72, pp. 294-294.
  • Perkins W.A., 1980: Area Segmentation of Images Using Edge Points. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(1), pp. 8-15.
  • Petrick N., Chan H.P., Sahiner B., Helvie M.A., 1999: Combined Adaptive Enhancement and Region-Growing Segmentation of Breast Masses on Digitized Mammograms. Medical Physics, 26(8), pp. 1642-1654.
  • Petrou M., Petrou C., 2010: Image Processing. The Fundamentals. Willey.
  • Pham D.L., Xu C., Prince J., 2000: Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering, 2, pp. 315-337.
  • Pham T.D., Crane D.I., 2005: Segmentation of Neuronal-Cell Images from Stained Fields and Monomodal Histograms. EMBS’2005, Proceedings of the IEEE 27th Annual Conference on Engineering in Medicine and Biology , pp. 6289-6292.
  • Pikaz A., Averbuch A., 1996: Digital Image Thresholding Based on Topological Stable State. Pattern Recognition, 29, pp. 829-843.
  • Piotrowski M., Szczepaniak P.S., 2000: Active Contour Based Segmentation of Low-Contrast Medical Images. MEDSIP’2000, Proceedings of 1st International Conference on Advances in Medical Signal and Information Processing, pp. 104-109.
  • Pisupati C., Wolf L., Mitzner W., Zerhouni E., 1996: Segmentation of 3D Pulmonary Trees Using Mathematical Morphology. In: Maragos P., Schafer R.W., Butt M. A. (Eds.): Mathematical Morphology and Its Applications to Image and Signal Processing, pp. 409-416.
  • Pohle R., Toennies K.D., 2001: Segmentation of Medical Images Using Adaptive Region Growing. Proceedings of SPIE-The International Society for Optical Engineering, 4322(3), pp. 1337-1346.
  • Postolski M., Janaszewski M., Fabijańska A., Babout L., Couprie M., Jędrzejczyk M., Stefańczyk L., 2009a: Reliable Airway Tree Segmentation Based on Hole Closing in Bronchial Walls. Computer Recognition Systems 3, Springer, pp. 389-396.
  • Postolski M., Janaszewski M., Fabijańska A., Babout L, Jędrzejczyk M., Stefańczyk L., 2009b: Segmentacja Drzewa Oskrzelowego z Wykorzystaniem Algorytmu Zamykania Otworów, Zeszyty naukowe Automatyka, 13(3), pp. 949-948.
  • Porter A.W., 1933: The Calculation of Surface Tension from Experiment. Philosophical Magazine, 15, pp. 163-169.
  • Pratt W.K., 2007: Digital Image Processing. Wiley-Interscience.
  • Prewitt J., 1970: Object Enhancement and Extraction. Lipkin B., Rosenfeld A. (Eds): Picture Processing and Psychopictorics, New York: Academic, pp. 75-149.
  • Pu J., Fuhrman C., Good W.F., Sciurba F.C., Gur D., 2011: A Differential Geometric Approach to Automated Segmentation of Human Airway Tree. IEEE Transactions on Medical Imaging, 30(2), pp. 266-278.
  • Pun T., 1980: A New Method for Gray-Level Picture Threshold Using the Entropy of the Histogram. Signal Processing, 2(3), pp. 223-237.
  • Pun T., 1981: Entropic Thresholding: A New Approach. Computer Graphics and Image Processing, 16, pp. 210-239.
  • Putiatin E., Averin S., 1990: Przetwarzanie Obrazów w Robotyce. Mashinostrojenie (Обработка изображений в робототехнике. Машиностроение) (w języku rosyjskim).
  • Qu Y.-D., Cui C.-S., Chen S.-B., Li J.-Q., 2005: A Fast Subpixel Edge Detection Method Using Sobel-Zernike Moments Operator. Image and Vision Computing, 23(1), pp. 11-17.
  • Ramar K., Arunigam S., Sivanandam S.N., Ganesan L., Manimegalai D., 2000: Quantitative Fuzzy Measures for Threshold Selection. Pattern Recognition Letters, 21, pp. 1-7.
  • Ramesh N., Yoo J.H., Sethi I.K., 1995: Thresholding Based on Histogram Approximation. IEE Proceedings-Vision, Image, and Signal Processing, 142(5), pp. 271-279.
  • Rao D.H., Panduranga P.P., 2006: A Survey on Image Enhancement Techniques: Classical Spatial Filter, Neural Network, Cellular Neural Network, and Fuzzy Filter. ICIT’2006, Proceedings of IEEE International Conference on Industrial Technology, pp. 2821-2826.
  • Revol-Muller C., Peyrin F., Carrillon Y., Odet C., 2002: Automated 3D Region Growing Algorithm Based on an Assessment Function. Pattern Recognition Letters, 23 (1-3), pp. 137-150.
  • Ridler T.W., Calvard S., 1978: Picture Thresholding Using an Iterative Selection Method. IEEE Transactions on System Man and Cybernetics, SMC-8, pp. 630-632.
  • Robinson G.S., 1976: Detection and Coding of Edges Using Directional Masks. University of Southern California, Los Angeles, CA, USC-IPL Rep. 660, pp. 40-57.
  • Robinson G.S., 1977: Edge Detection By Compass Gradient Masks. Computer Graphics and Image Processing, 6, pp. 492-501.
  • Roberts L.G., 1965: Machine Perception of Three-Dimensional Solids. Tippett J. T., et al., (Eds): Optical and Electro-Optical Information Processing, Cambridge, MA: MIT Press.
  • Robertson N.M., Chan T., 2009: Aerial Image Segmentation for Flood Risk Analysis. ICIP’2009, Proceedings of IEEE International Conference on Image Processing, pp. 597-600.
  • Rockett P., 1999: The Accuracy of Sub-Pixel Localisation in the Canny Edge Detector. BMVC’2010, Proceedings of British Machine Vision Conference, dostęp on-line: http://www.bmva.ac.uk/bmvc/1999/papers/39.pdf
  • Rosenfeld A., 1969: Picture Processing by Computer. Academic Press.
  • Rosenfeld A., De la Torre P., 1983: Histogram Concavity Analysis as an Aid in Threshold Selection. IEEE Transactions on System Man Cybernetics, 13, pp. 231-235.
  • Rosenfeld A., 1984: The Fuzzy Geometry of Image Subsets. Pattern Recognition Letters, 2, pp. 311-317.
  • Rosenthal W., Dozier J., 1996: Automated Mapping of Montane Snow Cover at Subpixel Resolution from the Landsat Thematic Mapper. Water Resources Research, 32, pp. 115-130.
  • Rother C., Kolmogorov V., Blake A., 2004: Grabcut Interactive Foreground Extraction Using Iterated Graph Cuts. ACM Transactions on Graphics (SIGGRAPH), 23(3), pp. 309-314
  • Rother C., Kolmogorov V., Blake A., Brown M., 2012: Image and Video Editing-GrabCut. dostęp on-line pod adresem: http://research.microsoft.com/enus/um/cambridge/projects/visionimagevideoediting/segmentation/grabcut.htm
  • Roy A., Royer A., Turcotte R., 2010: Improvement of Springtime Streamflow Simulations in a Boreal Environment by Incorporating Snow-Covered Area Derived From Remote Sensing Data. Journal of Hydrology, 390, pp. 35-44.
  • Rudzki M., 2009: Vessel Detection Method Based on Eigenvalues of the Hessian Matrix and its Applicability to Airway Tree Segmentation. OWD’2009, Proceedings of XI International PhD Workshop, pp. 100-105.
  • Rundell P.W., Graham E.A., Allen M.F., Fisher J.C., Harmon T.C., 2009: Environmental Sensor Networks in Ecological Research. New Phytologist, 182, pp. 589-607.
  • Ruzon M.A., Tomasi C., 1999: Color Edge Detection with the Compass Operator. CVPR’99, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2, pp. 160-166.
  • Rymarczyk T., Filipowicz S.F., 2009: Wykorzystanie Metody Zbiorów Poziomicowych w Procesie Segmentacji i Rekonstrukcji Obrazu. Prace Instytutu Elektrotechniki, 242, pp. 89-102.
  • Saad N.M., Abu-Bakar S.A.R., Muda S., Mokji M., Abdullah A.R., 2012: Fully Automated Region Growing Segmentation of Brain Lesion in Diffusion-weighted MRI. IAENG International Journal of Computer Science, 39(2), pp. 1-10.
  • Saarinen K., 1994: Color Image Segmentation by a Watershed Algorithm and Region Adjacency Graph Processing. ICIP’94, Proceedings of IEEE International Conference on Image Processing, pp. 1021-1025.
  • Sahasrabudhe S.C., Gupta K.S.D., 1992: A Valley-Seeking Threshold Selection Technique. Computer Vision and Image Understanding, 56, pp. 55-65.
  • Sahoo P.K., Soltani S., Wong A.K.C., Chen Y.C., 1988: A Survey of Thresholding Techniques. Computer Vision Graphics and Image Processing, 41(2), pp. 233-260.
  • Sahoo P., Wilkins C., Yeager J., 1997: Threshold Selection Using Renyi’s Entropy. Pattern Recognition, 30, pp. 71-84.
  • Sankowski D., Strzecha K., Jeżewski S., 2000: Digital Image Analysis in Measurement of Surface Tension and Wettability Angle. TCSET’2000, Proceedings of IEEE International Conference on Modern Problems in Telecommunication, Computer Science and Engineers Training, pp. 129-130.
  • Sankowski D., Senkara J., Strzecha K., Jeżewski S., 2001: Automatic Investigation of Surface Phenomena in High Temperature Solid and Liquid Contacts. IMTC’2001, Proceedings of 18th IEEE Instrumentation and Measurement Technology Conference, pp. 346-249.
  • Sankowski D., Strzecha K., Fabijańska A., 2006: Edge Detection Algorithm-The New Approach. SiS’2006, XIV Konferencja Sieci i Systemy Informatyczne, pp. 195-197.
  • Sankowski D., Mosorov W., Strzecha K., 2011: Przetwarzanie i Analiza Obrazów w Systemach Przemysłowych-Wybrane zastosowania. PWN.
  • Sankur B., Sezgin M., 2004: A Survey Over Image Thresholding Techniques and Quantitative Performance Evaluation. Journal of Electronic Imaging, 13(1), pp. 146-165.
  • Sapiro G., Tannenbaum A., 1993: Affine Invariant Scale-Space. International Journal of Computer Vision, 11(1), pp. 25-44.
  • Sato Y., Nakajima S., Atsumi H., Koller T., Gerig G., Yoshida S., Kikinis R., 1997: 3D Multi-Scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Images. Lecture Notes in Computer Science, 1205, pp. 213-222.
  • Sauvola J., Pietaksinen M., 2000: Adaptive Document Image Binarization. Pattern Recognition, 33, pp. 225-236.
  • Schaffhauser A., Adams M., Fromm R., Jörg P., Luzi G., Noferini L., Sailer R., 2008: Remote Sensing Based Retrieval of Snow Cover Properties. Cold Regions Science and Technology, 54, pp.164-175.
  • Schenk A., Prause G., Peitgen H.O., 2001: Local Cost Computation for Efficient Segmentation of 3D Objects with Live Wire. SPIE Medical Imaging, 4322, pp. 1357-1364.
  • Schlathoelter T., Lorenz C., Carlsen I.C., Renisch S., Sonka M., Fitzpatrick J.M., 2002: Simultaneous Segmentation and Tree Reconstruction of the Airways for Virtual Bronchoscopy. Proceedings SPIE Medical Imaging, 4684, pp. 103-113.
  • Scott N., 2010: Remote Sensing Tutorial. http://rst.gsfc.nasa.gov/Sect13/Sect13_2.html
  • Sonka M., Park W., Hoffman E., 1996: Rule-Based Detection of Intrathoracic Airway Trees. IEEE Transactions on Medical Imaging, 15(3), pp. 314-326.
  • Steinhaus H., 1957: Sur La Division Des Corps Matériels En Parties. Bull. Acad. Polon. Sci., 4(12), pp. 801–804.
  • Sun K., Chen Z., Jiang S., 2012: Local Morphology Fitting Active Contour for Automatic Vascular Segmentation. IEEE Transactions on Biomedical Engineering, 59(2), pp. 464-473
  • Swift R.D., Kiraly A.P., Sherbondy A.J., Austin A.L., Hoffman E.A., McLennan G., Higgins W.E., 2002: Automatic Axes-Generation for Virtual Bronchoscopic Assessment of Major Airway Obstructions. Computerized Medical Imaging and Graphics, 26(2), pp. 103-118.
  • Szczepański M., 2004: Zastosowanie Teorii Błądzenia Przypadkowego w Przetwarzaniu Wstępnym Barwnych Obrazów Cyfrowych, Przegląd Telekomunikacyjny-Wiadomości Telekomunikacyjne, 10, pp. 395-398.
  • Sheng Y., Shen L., 1994: Orthogonal Fourier-Mellin Moments for Invariant Pattern Recognition. Journal of the Optical Society of America, 11(6), pp. 1748-1757.
  • Senthilkumar B., Umamaheswari G., Karthik J., 2010: A Novel Region Growing Segmentation Algorithm for the Detection of Breast Cancer. ICCIC’2010, Proceedings of IEEE International Conference on Computational Intelligence and Computing Research, pp. 1-4.
  • Senthilkumaran N., Rajesh R., 2009: Edge Detection Techniques for Image Segmentation- A Survey of Soft Computing Approaches. International Journal of Recent Trends in Engineering, 1(2), pp. 250-254.
  • Serra J., 2008: Connective Segmentation. ICIP’08, Proceedings of IEEE International Conference on Image Processing, pp. 2192-2195.
  • Sethian J.A., 1999: Level Set Methods and Fast Marching Methods. Cambridge University Press, UK.
  • Sezan M.I., 1985: A Peak Detection Algorithm and Its Application to Histogram-Based Image Data Reduction. Graphical Models and Image Processing, 29, pp. 47-59.
  • Shafarenko L., Petrou M., Kittler J., 1997: Automatic Watershed Segmentation of Randomly Textured Color Images. IEEE Transactions on Image Processing, 6(11), pp. 1530-1544.
  • Shanbag A. G., 1994: Utilization of Information Measure as a Means of Image Thresholding. Computer Vision Graphics and Image Processing, 56, pp. 414-419.
  • Shi J., Malik J., 2000: Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 2000, pp. 888-905.
  • Shih F.Y., Cheng S., 2005: Automatic Seeded Region Growing for Color Image Segmentation. Image and Vision Computing, 23(10), pp. 877-886.
  • Sidiropoulos N.D., Baras J.S., Berenstein C.A., 1992: Discrete Random Sets: An Inverse Problem, Plus Tools for the Statistical Inference of the Discrete Boolean Model. Proceedings SPIE, 1769, pp. 32-43.
  • Silveira M., Heleno S., 2009: Classification of Water Regions in SAR Images Using Level Sets and Non-Parametric Density Estimation. ICIP’2009, Proceedings of International Conference on Image Processing, pp. 1685-1688.
  • Sivewright G.J., Elliott P.J., 1994: Interactive Region and Volume Growing for Segmenting Volumes in MR and CT Images. Medical Informatics 19(1), pp. 71-80.
  • Smith T.G., Marks W.B., Lange G.D., Sheriff W.H., Neale E.A., 1988: Edge Detection in Images Using Marr-Hildreth Filtering Techniques. Journal of Neuroscience Methods, 26(1), pp. 75-81.
  • Smółka B., Wojciechowski K.W., 2010: Random Walk Approach to Image Enhancement. Signal Processing, 81, pp. 465-482.
  • Sonka M., Hlavac V., Boyle R., 2007: Image Processing, Analysis, and Machine Vision. CLEngineering.
  • Spitzer F., 2001: Principles of Random Walk. Springer-Verlag.
  • Stanke G., Zedler L., Zorn A., Weckend F., Weide H.G., 1998: Sub-Pixel Accuracy by Optical Measurement of Large Automobile Components. IECON’1998, Proceedings of 24th Annual Conference of the IEEE Industrial Electronics Society, pp. 2431-2433.
  • Strzecha K., 2002: Zastosowanie Przetwarzania i Analizy Obrazów w Wysokotemperaturowych Pomiarach Własności Fizyczno-Chemicznych Wybranych Materiałów. Rozprawa doktorska, Łódź, Politechnika Łódzka.
  • Strzecha K., Fabijańska A., Sankowski D., 2006a: Nowe Algorytmy Segmentacji w Wysokotemperaturowym Przemysłowym Systemie Analizy Obrazów. Zeszyty naukowe Automatyka, 10(3), pp. 283-297.
  • Strzecha K., Fabijańska A., Sankowski D., 2006b: Optical filters in computer high-temperature image processing and analysis systems. SiS’2006, XIV Konferencja Sieci i Systemy Informatyczne, pp. 203-206.
  • Strzecha K., Fabijańska A., Sankowski D., 2006c: Application Of The Edge-Based Image Segmentation. MEMSTECH’2006, Proceedings of IEEE International Conference on Perspective Technologies and Methods in MEMS Design, pp. 28-31.
  • Strzecha K., Fabijańska A., Sankowski D., Koszmider T., Bąkała M., 2010a: Thermo-Wet- Skomputeryzowany System Pomiarowy Własności Fizyko-Chemicznych Wybranych Materiałów w Wysokich Temperaturach. Zeszyty naukowe Politechniki Łódzkiej, Elektryka, 121, pp. 223-240.
  • Strzecha K., Bąkała M., Fabijańska A., Koszmider T., 2010b: The Evolution of Thermo-Wet- The Computerized System for High Temperature Measurements of Surface Properties. Zeszyty naukowe Automatyka, 14(3.1), pp. 525-535.
  • Strzecha K., Bąkała M., Fabijańska A., Koszmider T., 2010c: New Ideas in High Temperature Computerized Measurements of Surface Properties. MEMSTECH’2010, Proceedings of IEEE 6th International Conference Perspective Technologies and Methods in MEMS Design, pp. 81-84.
  • Strzelecki M., Materka A., Kociński M., Szczypiński P., Deistung A., Reichenbach J., 2010: Ocena Metody Zbiorów Poziomicowych Stosowanych do Segmentacji Trójwymiarowych Obrazów Fantomów Cyfrowych oraz Obrazów Naczyń Krwionośnych Mózgu TOF-SWI Rezonansu Magnetycznego. Inżynieria biomedyczna, 16(2), pp. 167-172.
  • Szczypiński P., 2001: Modele deformowalne do ilościowej analizy i rozpoznawania obiektów w obrazach cyfrowych. Rozprawa Doktorska, Politechnika Łódzka, Wydział Elektrotechniki, Elektroniki, Informatyki i Automatyki.
  • Tabatabai A.J., Mitchell O.R., 1984: Edge Location to Sub pixel Values in Digital Imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(2), pp. 188-201.
  • Tadeusiewicz R., Korohoda P., 1997: Komputerowa Analiza i Przetwarzanie Obrazów. Wydawnictwo Fundacji Postępu Telekomunikacji, Kraków.
  • Tajimaa R., Kato Y., 2011: Comparison of Threshold Algorithms for Automatic Image Processing of Rice Roots Using Freeware ImageJ. Field Crops Research, 121(3), pp. 460-463.
  • Tavakoli M., Mehdizadeh A.R., Pourreza R., Pourreza H.R., Banaee T., Bahreini Toosi M.H., 2011: Radon Transform Technique for Linear Structures Detection: Application to Vessel Detection in Fluorescein Angiography Fundus Images. Proceedings of 2011 IEEE Nuclear Science Symposium Conference Record, MIC12.M-145, pp. 3051- 3056.
  • Terzopoulos D., 1986: On Matching Deformable Models to Images. Technical Report 60, Schlumberger Palo Alto Research, November 1986.
  • Thakur A., Anand R.S., 2005: A Local Statistics Based Region Growing Segmentation Method for Ultrasound Medical Images. International Journal of Information and Communication Engineering, 1(3), pp. 141-146.
  • Tian L., Kamata S.I., 2008: Image Enhancement by Analysis on Embedded Surfaces of Images and a New Framework for Enhancement Evaluation. Journal IEICE-Transactions on Information and Systems, E91-D(7), pp. 1946-1954.
  • Tomczyk A., Szczepaniak P.S., 2008: Active Contour Segmentation of Disjoint Objects Applied to Medical Images. Journal of Medical Informatics & Technologies, 12, pp. 163-168.
  • Torre V., Poggio T., 1984: On Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(2), pp. 147-163.
  • Tremeau A., Borel N., 1997: A region growing and merging algorithm to color segmentation. Pattern Recognition, 30(7), pp. 1191-1203.
  • Tremeau A., Colantoni P., 2000: Regions Adjacency Graph Applied to Color Image Segmentation. IEEE Transactions on Image Processing, 9(4), pp. 735-744.
  • Trussel H.J., 1979: Comments on Picture Thresholding Using Iterative Selection Method. IEEE Transactions on System Man and Cybernetics, 9, pp. 311.
  • Tsai W.H., 1985: Moment-Preserving Thresholding: A New Approach. Graphical Models and Image Processing, 19, pp. 377-393.
  • Tsai D.M., 1995: A Fast Thresholding Selection Procedure for Multimodal and Unimodal Histograms. Pattern Recognition Letters, 16, pp. 653-666.
  • Tschirren J., Hoffman E.A., McLennan G., Sonka M., 2005: Intrathoracic Airway Trees: Segmentation and Airway Morphology Analysis From Low-Dose CT Scans. IEEE Transactions on Medical Imaging, 24(12), pp. 1529-1539.
  • Urquhart R., 1982: Graph Theoretical Clustering Based on Limited Neighborhood Sets. Pattern Recognition, 15(3), pp. 173-187.
  • Vanhamel I., Pratikakis I., Sahli H., 2003: Multiscale Gradient Watersheds of Color Images. IEEE Transactions on Image Processing, 12(6), pp. 617-626.
  • Van Kampen N.G.,1992: Stochastic Processes in Physics and Chemistry. North-Holland.
  • Veksler O., 2008: Star Shape Prior for Graph-Cut Image Segmentation. ECCV’2008, Proceedings of European Conference on Computer Vision.
  • Viitaniemi V., Laaksonen J.T., 2008: Techniques for Image Classification, Object Detection and Object Segmentation. VISUAL ‘08, Proceedings of the 10th International Conference on Visual Information Systems: Web-Based Visual Information Search and Management, pp. 231-234.
  • Vincent L., Soille P., 1991: Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6), pp. 583-598.
  • Wan S.Y., Higgins W.E., 2003: Symmetric Region Growing. IEEE Transactions on Image Processing, 12(9), pp.1007-1015.
  • Wang M., Zeng, X., Huang X., 2009: A Novel Sub-Pixel Edge Detection for Micro-Parts Manipulation. ROBIO’2009, Proceedings of IEEE International Conference on Robotics and Biomimetics, pp. 1297-1301.
  • Wang S., Siskind J.M., 2001: Image Segmentation with Minimum Mean Cut. ICCV’2001, Proceedings of International Conference on Computer Vision, 1, pp. 517-524.
  • Wang S., Siskind J.M., 2003: Image Segmentation with Ratio Cut, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(6), pp. 675-690.
  • Wang W., 2007: Generalized Explicit Schemes for Coherence Enhancing Diffusion Filtering. ICIG’2007, Proceedings of 4th International Conference on Image and Graphics, pp. 126-132.
  • Wang X., Gao J., Wang L., 2004: A Survey of Subpixel Object Localization for Image Measurement. ICIA’2004, Proceedings of International Conference on Information Acquisition, pp. 398-401.
  • Wang X.H., Wang J.Y., Zhang J.L., Liang H.W., Kou P.M., 2010: Study on the Detection of Yarn Hairiness Morphology Based on Image Processing Technique. ICMLC’ 2010, Proceedings of International Conference on Machine Learning and Cybernetics, 5, pp. 2332-2336.
  • Weiss G.H., 1994: Aspects and Applications of the Random Walk. North-Holland.
  • Weng Q.H., Lu D.S., 2009: Landscape as a Continuum: An Examination of the Urban Landscape Structures and Dynamics of Indianapolis City, 1991-2000, by Using Satellite Images. International Journal of Remote Sensing, 30, pp. 2547-2577.
  • Wertheimer M., 1938: Laws of Organization in Perceptual Forms. in: Ellis, W.B. (Ed.): A Sourcebook of Gestalt Psychology, Harcourt, Brace, pp. 71-88.
  • Weszka J.S., 1978: A Survey of Threshold Selection Techniques. Computer Graphics and Image Processing, 2, pp. 259-265.
  • Weszka J.S., Rosenfeld A., 1979: Histogram Modification for Threshold Selection. IEEE Transactions on System Man and Cybernetics, 9, pp. 38-52.
  • Węgliński T., Fabijańska A., 2011a: Brain Tumor Segmentation from MRI Data Sets Using Region Growing Approach. MEMSTECH’2011, Proceedings of 7th International Conference Perspective Technologies and Methods in MEMS Design, pp. 185-188.
  • Węgliński T., Fabijańska A., 2011b: The Concept of Image Processing Algorithms for Assessment and Diagnosis of Hydrocephalus in Children. Prace Instytutu Elektrotechniki, 251, Instytut Elektrotechniki Politechniki Warszawskiej, Warszawa, pp. 156-177.
  • Węgliński T., Fabijańska A., 2011c: Image Segmentation Algorithms for Diagnosis Support of Hydrocephalus in Children. Zeszyty naukowe Automatyka, 15(3), pp. 309-320.
  • Węgliński T., Fabijańska A., 2012a: Survey Over Modern Image Segmentation Algorithms on CT Scans of Hydrocephalic Brains. Zeszyty naukowe Automatyka, 16(3), w druku.
  • Węgliński T., Fabijańska A., 2012b: On Cerebrospinal Fluid Segmentation From CT Brain Scans Using Interactive Graph Cuts. WD’2012, Warsztaty Doktoranckie, pp. 102-103.
  • Węgliński T., Fabijańska A., 2012c: Min-Cut/Max-Flow Segmentation of Hydrocephalus in Children from CT Datasets. ICSES’2012, Proceedings of International Conference on Signal and Electronic Systems, w druku.
  • Whatmough R.J., 1991: Automatic Threshold Selection from a Histogram Using the Exponential Hull. Graphical Models and Image Processing, 53, pp. 592-600.
  • White J.M., Rohrer G.D., 1983: Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction. IBM Journal of Research and Development, 27(4), pp. 400-411.
  • Wiatr K., 2003: Akceleracja Obliczeń w Systemach Wizyjnych. WNT.
  • Williams J., Wolff L., 1997: Analysis of the Pulmonary Vascular Tree Using Differential Geometry Based Vector Fields. Computer Vision and Image Understanding, 65, pp. 226-236.
  • Weickert J., 1998: Anisotropic Diffusion in Image Processing. ECMI Series, Teubner-Verlag, dostęp on-line http://www.mia.uni-saarland.de/weickert/book.html.
  • Weickert J., 1999: Coherence-Enhancing Diffusion Filtering. International Journal of Computer Vision, 31, pp. 111-127.
  • Wilson R.J., 2007: Wprowadzenie do Teorii Grafów. Wydawnictwo Naukowe PWN.
  • Winn J., Shotton J., 2006: The Layout Consistent Random Field for Recognizing and Segmenting Partially Occluded Objects. CVPR’2006, Proceedings of IEEE Computer Vision and Pattern Recognition, 1, pp. 37-44.
  • Wood S., Zerhouni A., Hoffman E.A., Mitzner W., 1995: Measurement of Three-Dimensional Lung Tree Structures Using Computed Tomography. Journal of Applied Physiology, 79(5), pp. 1687-1697.
  • Woźnicki J., 1996: Podstawowe Techniki Przetwarzania Obrazu. Wydawnictwo Komunikacji i Łączności.
  • Wu K.L., Yang M.S., 2002: Alternative C-means Clustering Algorithms. Pattern Recognition, 35, pp. 2267-2278.
  • Wu Z., Leahy R., 1990: Tissue Classification in MR Images Using Hierarchical Segmentation. Proceedings of IEEE International Conference on Medical Imaging, pp. 1410-1414.
  • Wu Z., Leahy R., 1993: An Optimal Graph Theoretic Approach to Data Clustering: Theory and its Application to Image Processing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11, pp. 1101-1113.
  • Xie L., Hu Y., Chen Y., Luo L., 2010: Maximum a Posteriori Based Coronary Angiograms Segmentation Method with Vessel-Like Feature and Markov Random Field. MIACA’2010, Proceedings of IEEE International Conference of Medical Image Analysis and Clinical Application, pp. 123-126.
  • Xiong W., Ong S.H., Lim J.H., 2010: A Recursive and Model-Constrained Region Splitting Algorithm for Cell Clump Decomposition. ICPR’2010, Proceedings of International Conference on Pattern Recognition, pp. 4416-4419.
  • Xiaoliang W., Chunsheng L., Renbiao W., 2011: River Boundaries Extraction in Mountain Areas for SAR Images with Fusing GIS Information. Proceedings of IEEE CIE International Conference on Radar, pp. 1586-1588.
  • Xu C., Prince J.L., 1998: Snakes, Shapes and Gradient Vector Flow. IEEE Transactions on Image Processing, 7(3), pp. 359-369.
  • Xu G.S., 2009a: Sub-pixel Edge Detection Based on Curve Fitting. ICCS’2009, Proceedings of 2nd International Conference on Information and Computing Science, pp. 373-375.
  • Xu G.S., 2009b: Linear Array CCD Image Sub-pixel Edge Detection Based on Wavelet Transform. ICCS’2009, Proceedings of 2nd International Conference on Information and Computing Science, pp. 204-206.
  • Xu Y., Weaver J.B., Healy D.M., Lu J., 1994: Wavelet Transform Domain Filters: A Spatially Selective Noise Filtration Technique. IEEE Transactions on Image Processing, 3(6), pp. 747-758.
  • Yachida M., Tsuji S., 1971: Application of Color Information to Visual Perception. Pattern Recognition, 3(3), pp. 307-318.
  • Yager R., 1979: On the Measure of Fuzziness and Negation. Part I: Membership in the Unit Interval. International Journal of General Systems, 5, pp. 221-229.
  • Yakimovsky Y., 1974: Boundary and Object Detection in Real World Images. Proceedings of IEEE Conference on Decision and Control including the 13th Symposium on Adaptive Processes, pp. 460-467.
  • Yang X., Li H., Zhou X., 2006: Nuclei Segmentation Using Marker-Controlled Watershed, Tracking Using Mean-Shift, and Kalman Filter in Time-Lapse Microscopy. IEEE Transactions on Circuits and Systems I, 53(11), pp. 2405-2414.
  • Yanni M.K., Horne E., 1994: A New Approach to Dynamic Thresholding. EUSIPCO’94, Proceedings of 9th European Conference on Signal Processing, 1, pp. 34-44.
  • Yanowitz S.D., Bruckstein A.M., 1989: A New Method for Image Segmentation. Computer Graphics and Image Processing, 46, pp. 82-95.
  • Yasuda Y., Dubois M., Huang T.S., 1980: Data Compression for Check Processing Machines. Proceedings IEEE, 68, pp. 874-885.
  • Yao Y., Ju H., 2009: A Sub-Pixel Edge Detection Method Based on Canny Operator. FSKD’2009, Proceedings of 6th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 97-100.
  • Yen J.C., Chang F.J., Chang S., 1995: A New Criterion for Automatic Multilevel Thresholding. IEEE Transactions on Image Processing, 4, pp. 370-378.
  • Yi J., Ra J.B., 2003: A Locally Adaptive Region Growing Algorithm for Vascular Segmentation. International Journal of Imaging Systems and Technology, 13(4), pp. 208-214.
  • Yoo S.T. (Ed.), 2004: Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis. A K Peters, Ltd.
  • Yu Y., 1990: Segmentation Coding Using Edge Detection and Region Merging. BMVC’90, Proceedings of British Machine Vision Conference, pp. 419-423.
  • Yu Y.W., Wang J.H., 1999: Image Segmentation Based on Region Growing And Edge Detection. SMC’99, Proceedings of the IEEE International Conference on Systems Man and Cybernetics, 6, pp. VI-798-VI-803.
  • Yu Q., Clausi D.A., 2008: IRGS: Image Segmentation Using Edge Penalties and Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(12), pp. 2126-2139.
  • Yue Y., Gong J., Wang D., 2010: The Extraction of Water Information Based on SPOT5 Image Using Object-oriented Method. Proceedings of IEEE 18th International Conference on Geoinformatics, pp. 1-5.
  • Zahn C.T., 1971: Graph-Theoretic Methods for Detecting and Describing Gestalt Clusters. IEEE Transactions on Computing, 20, pp. 68-86.
  • Zawada-Tomkiewicz A., 1999: Komputerowa Analiza i Przetwarzanie Obrazów. Wydawnictwo Uczelniane Politechniki Koszalińskiej, Koszalin.
  • Zeng Y., Samaras D., Chen W., Peng Q., 2008: Topology Cuts: A Novel Min-Cut/Max-Flow Algorithm for Topology Preserving Segmentation in N-D Images. Journal of Computer Vision and Image Understanding, 112(1), pp. 81-90.
  • Zenzo S.D., 1983: Advances in Image Segmentation. Image and Vision Computing, 1(4), pp. 196-210.
  • Zhang Y.J., 1997: Evaluation and Comparison of Different Segmentation Algorithms. Pattern Recognition Letters, 18(10), pp. 963-974.
  • Zhang Y.J., (Ed.), 2006: Advances in Image and Video Segmentation. IRM Press.
  • Zhao J., Yu H., Gu X., Wang S., 2010: The Edge Detection of River model Based on Selfadaptive Canny Algorithm and Connected Domain Segmentation. Proceedings of IEEE 8th World Congress on Intelligent Control and Automation, pp. 1333-1336.
  • Zhao S., Zhou M., Xu F., 2010: MIP-Guided Blood Vessel Segmentation Using SEM Statistical Mixture Model. ISISI’2010, Proceedings of IEEE 3rd International Symposium on Information Science and Engineering, pp. 263-266.
  • Zheng X.P., Bi Y.W., 2009: Improved Algorithm about Subpixel Edge Detection Based on Zernike Moments and Three-Grayscale Pattern. CISP’2009, Proceedings of 2nd International Congress on Image and Signal Processing, pp. 1-4.
  • Zhou Y., Starkey J., Mansinha L., 2004: Segmentation of Petrographic Images by Integrating Edge Detection and Region Growing. Computers and Geosciences, 30(8), pp. 817-831.
  • Zhu C., Qi S., van Triest H., Wang S., Kang Y., Yue Y., 2010: Automatic 3D Segmentation of Human Airway Tree In CT Image. BMEI’2010, Proceedings of 3rd International Conference on Biomedical Engineering and Informatics, pp. 132-136.
  • Zhu L., Fan H., Tang Y., Han J., 2011: An Active Contour Method for Tubular Object Segmentation. ICISP’2011, Proceedings of IEEE 4th International Congress on Image and Signal Processing, pp. 1184-1188.
  • Zhu S.C., Yuille A., 1996: Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(9), pp. 884-900.
  • Zimmer C., Olivo-Marin J.C., 2005: Coupled Parametric Active Contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(11), pp. 1838-1842.
  • Ziou D., Tabbone S., 1998: Edge Detection Techniques: An Overview. International Journal of Pattern Recognition and Image Analysis, 8(4), pp. 537-559.
  • Zrimec T., Busayarat S., 2007: A System for Computer Aided Detection of Diseases Patterns in High Resolution CT Images of the Lungs. CBMS’2007, Proceedings of 20th IEEE International Symposium on Computer-Based Medical Systems, pp. 41-46.
  • Zucker S.W., 1976: Region Growing: Childhood and Adolescence. Computer Graphics and Image Processing, 5(3), pp. 382-399.
  • Zunfeng H., Hongshe D., Xiaorui L., 2008: A Novel Fast Subpixel Edge Location Method Based on Sobel-OFMM. ICAL’2008, Proceedings of IEEE International Conference on Automation and Logistics, pp. 828-832.
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