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Using connected components to quide image understanding and segmentation

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Języki publikacji
EN
Abstrakty
EN
In this paper, we propose a method for understanding an image with the help of the theory of parameter-dependent connected components developed by us in a previous work. We may study various properties of an image at the connected component level, from the low level vision to an intermediate level vision. Using the information obtained from various component histograms and certain pre-knowledge, we describe how to select suitable values of the parameters so that an object in a gray image may be represented by a parameter-dependent component. Segmentation of the object could be conducted by locating the corresponding component. Our approach can be applied to a wide variety of images as we do not make any assumptions about the image formation model, and the method is independent of the type of the grid system used for the digitization process and the type of pixel adjacency relation.
Twórcy
autor
  • Computer Science Department, Southwest Missouri State University, Sprongfield, MO 65804
Bibliografia
  • [1] Zadeh L. A.: Fuzzy Sets. Info. Contr., 8, 338-353. 1963.
  • [2] Prewitt J. M. S.: Object enhancement and extraction. In Lipkin B. S., Rosenfeld A., (Eds.): Picture Processing and Psychopictorics, AP, NY, 75-149. 1970.
  • [3] Rosenfeld A.: Connectivity in Digital Pictures. Journal of the Association for Computing Machinery, 17, 146-160. 1970.
  • [4] Rosenfeld A., Hummel R. A., Zucker S. W.: Scene Labeling by Relaxation Operations. IEEE Trans. SMC, 6, 420-433. 1976.
  • [5] Sklansky J., Kibler D. F.: A theory of nonuniformly digitized binary pictures. IEEE Trans. SMC, 6, 637-647. 1976.
  • [6] Zucker S.: Region growing: childhood and adolescence. CGIP, 5,: 382-399. 1976.
  • [7] Pavlidis T.: Structural Pattern Recognition. S-V, New York. 1977.
  • [8] Rosenfeld A.: Iterative methods in image analysis. PR, 10, 181-187. 1978.
  • [9] Rosenfeld A.: Fuzzy digital topology: introduction and survey. Information and Control, 40, 76-87. 1979.
  • [10] Kanade T.: Region segmentation: signal vs. semantics. CVIP, 13, 279-297. 1980.
  • [11] Fu K. S., Mui J. K.: A survey of image segmentation. PR, 13, 3-16. 1981.
  • [12] Marr D.: Vision. W. H. Freeman, New York. 1982.
  • [13] Rosenfeld A., Kak A. C.: Digital Picture Processing. (2 Ed.) AP, New York. 1982.
  • [14] Serra J.: Image Analysis and Mathematical Morphology. AP, New York. 1982.
  • [15] Deravi F.; Pal S. K.: Grey level thresholding using second-order statistics. PRL, 1, 417-422. 1983.
  • [16] Rosenfeld A.: On connectivity properties of grayscale pictures. PR, 16, 47-50. 1983.
  • [17] Rosenfeld A.: The fuzzy geometry of image subsets. PRL, 2, 311-317. 1984.
  • [18] Haralick R. M., Shapiro L. G.: Survey: image segmentation techniques. CVIP, 29, 100-132. 1985.
  • [19] Horn B. K. P.: Robot Vision. The MIT Press, Cambridge, MA. 1986.
  • [20] Pal S. K., Majumder D. K. D.: Fuzzy Mathematical Approach to Pattern Recognition. Wiley, New York. 1986.
  • [21] Salwo P. K., Soltani, Wong A. K. C., Chen Y. C.: A survey of thresholding techniques. CVGIP, 41, 233-260. 1988.
  • [22] Beaulieu J. M., Goldberg M.: Hierarchy in picture segmentation: a stepwise optimization approach. IEEE Trans. PAMI, 11, 150-163. 1989.
  • [23] Beveridge J. R., Griffith J., Kohler R. R., Hansen A. R., Riseman E. M.: Segmenting images using localized histograms and region merging. Int. J. of Computer Vision, 2, 311-347. 1989.
  • [24] Kong T. Y., Rosenfeld A.: Digital topo logy: introduction and survey. CVIP, 48, 357-393. 1989.
  • [25] Pal N. R., Pal S. K.: Entropic thresholding. Signal Processing, 16, 97-108. 1989.
  • [26] Taxt T., Flynn P. J., Jain A. K.: Segmentation of document images. IEEE Trans. PAMI, 11, 1322-1329. 1989.
  • [27] Yanowitz S. D., Bruckstein A. M.: A new method for image segmentation. CVIP, 46, 82-95. 1989.
  • [28] Lee S. U., Chung S. Y., Park R. H.: A comparative performance study of several global thresholding techniques for segmentation. CVGIP, 52, 171-190. 1990.
  • [29] Matsuyama T., Hwang V. S.: SIGMA: A Knowledge-based Aerial Image Understanding System. Plenum Press, New York. 1990.
  • [30] Meyer F., Beucher S.: Morphological segmentation. J. of Visual Communication and Image Representation, 1, 21-46. 1990.
  • [31] Geiger D., Yuille A.: A common framework for image segmentation. Int. J. of Computer Vision, 6, 227-243. 1991.
  • [32] Montanavert A., Meer P., Rosenfeld A.: Hierarchical image analysis using irregular tesselations. IEEE Trans. PAMI, 13, 307-316. 1991.
  • [33] Pratt W. K.: Digital Image Processing, (2nd. ed.) Wiley, New York. 1991.
  • [34] Qian K., Bhattacharya P.: A template polynomial approach for image processing and visual recognition. PR, 25, 1505-1515. 1992.
  • [35] Marshall A. D., Martin R. R.: Computer Vision, Models and Inspection. World Scientific, New Jersey. 1992.
  • [36] Zhou Y. T., Chellappa R.: Artificial Neural Networks for Computer Vision. S-V, New York. 1992.
  • [37] Bongiovanni G., Cinque L. Levialdi S., Rosenfeld A.: Image segmentation by a multiresolution approach. PR, 26, 1845-1854. 1993.
  • [38] Herlin I. L., Giraudon G.: Performing segmentation of ultrasound images using temporal information. Proc. IEEE Computer Vision and Pattern Recognition Conference, 20, 373-378.
  • [39] Matus F., Flusser J.: Image representations via a finite radon transform. IEEE Trans. PAMI, 15, 996-1006. 1993.
  • [40] Meyer Y.: Wavelets: Algorithms and Applications. SIAM. 1993.
  • [41] Pal N. R., Pal S. K.: A review on image segmentation techniques. PR, 26, 1277-1294. 1993.
  • [42] Sahasrabudhe S. C., Gupta K. S. D.: A valley-seeking threshold selection technique. CVIP, 93, 55-65. 1993.
  • [43] Dubuisson M. P., Jain A. K., Jain M. K.: Segmentation and classification of bacterial culture. Journal of Microbiological Methods, 19, 279-295. 1994.
  • [44] Jain A. K., Dubuisson M. P.: Segmentation of X-ray and C-scan images of fiber reinforced composite materials. PR, 25, 257-270. 1994.
  • [45] Konstantinides K., Rasure J.: The Khoros software development for image and signal processing. IEEE Trans. Image Process, 3, 243-252. 1994.
  • [46] Olivo J. C.: Automatic threshold selection using the wavelet transform. CVIP, 56, 205-218. 1994.
  • [47] Zhang Y. J., Gerbrands J. J.: Objective and quantitative segmentation evaluation and comparison. SP, 39, 43-54. 1994.
  • [48] Ahuja N.: On detection and representation of multiscale low-level structure. ACM Computing Surveys, 27, 304-306. 1995.
  • [49] Latecki L.: Multicolor well-composed pictures. PRL, 16, 425-431. 1995.
  • [50] Latecki L., Eckhardt U., Rosenfeld A.: Well-composed sets. CVIU, 61, 70-83. 1995.
  • [51] Morel J. M., Solimini S.: Variational Methods in Image Segmentation. Birkhäuser, Boston, MA. 1995.
  • [52] Wang Y., Bhattacharya P.: A theory of parameter-dependent connected components of gray images and segmentation. Proc. lEEE Int. Conf. on Image Processing, 3, 69-72. 1995.
  • [53] Wang Y., Bhattacharya P.: On parameter-dependent connected components of gray images. PR, 29, 1359-1368. 1996.
  • [54] Wang Y., Bhattacharya P.: Image ana lysis and segmentation using gray connected components. Proc. IEEE Int. Conf. on SMC, 1, 444-449. 1996.
  • [55] Tieng Q. M., Boles W. W.: Recognition of 2D object contours using the wavelet transform zero-crossing representation. IEEE Trans. PAMI, 19, 910-915. 1997.
  • [56] Wang Y., Bhattacharya P.: Digital connectivity and extended well-composed sets for gray images. CVIU, 68, 330-345. 1997.
  • [57] Carpenter G. A., Grossberg S., Lesher G. W.: The what-and-w here filter. a spatial mapping neural network for object recognition and image understanding ,CVIU, 69, 1-22. 1998.
  • [58 ] Cheriet M., Said J. N., Suen C. Y.: A recursive thresholding technique for image segmentation. IEEE Trans. IP, 7, 918-921. 1998.
  • [59] Hojjatoleslami S. A., Kittler J.: Region growing: a new approach. IEEE Trans. IP, 7, 1079-1083. 1998.
  • [60] Banerjee A., Burlina P., Alajaji F.: Image segmentation and labeling using the polya urn model. IEEE Trans. IP, 8, 1243-1253. 1999.
  • [61] Jones R.: Connected filtering and segmentation using component trees. CVIU, 75, 215-228. 1999.
  • [62] Cheng H. D., Chen Y. H., Jiang X. H.: Thresholding using two-dimensional histogram and fuzzy entropy principle. IEEE Trans. IP, 9, 732-734. 2000.
  • [63] Saha P. K., Udupa J. K., Odhner D.: Scale-based fuzzy connected image segmentation: theory, algorithms, and validation. CVIU, 77, 145-174. 2000.
  • [64] Boccignone G., Ferraro M., Caelli T.: Encoding visual information using anisotropic transformations. IEEE Trans. PAMI, 23, 207-211. 2001.
  • [65] Fan J. P., Yau D. K. Y., Elmagarmid A. K., Aref W. G.: Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans. IP, 10, 1454-1466. 2001.
  • [66] Mohan A., Papageorgiou C., Poggio T.: Example-based object detection in images by components. IEEE Trans. PAMI, 23, 349-361. 2001.
  • [67] Saha P. K., Udupa J. K.: Relative fuzzy connectedness among multiple objects: theory, algorithms, and applications in image segmentation. CVIU, 82, 42-56. 2001.
  • [68] Saha P. K., Udupa J. K.: Optimum image thresholding via class uncertainty and region homogeneity. IEEE Trans. PAMI, 23, 689-706. 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA1-0003-0021
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