Wybrane zagadnienia przetwarzania obrazów barwnych

• Autorzy: Palus H.

Warianty tytułu

EN

Selected problems of colour image processing

• Języki publikacji: PL

Abstrakty

PL

Barwny obraz zapewnia bogatszą informację o obiektach w scenie i dlatego jego pozyskanie i przetwarzanie może znacznie uprościć rozpoznawanie i lokalizację tych obiektów. Występujące od niedawna: łatwy dostęp do urządzeń pozyskiwania obrazów barwnych, takich jak: skanery, cyfrowe aparaty fotograficzne i cyfrowe kamery oraz wzrost mocy obliczeniowej komputerów spowodowały, że to obrazy barwne stały się głównym obiektem przetwarzania i rozpoznawania, a obrazy binarne i monochromatyczne pozostały istotne w niektórych zastosowaniach widzenia maszynowego, przetwarzania obrazów medycznych itp. Chociaż przetwarzanie informacji barwnej jest niewątpliwie bardziej złożone, to jednocześnie informacja ta jest bardziej pełna. Cyfrowe przetwarzanie obrazów barwnych, oprócz stosowania metod znanych z przetwarzania obrazów monochromatycznych, korzysta z osiągnięć nauki o barwie, a szczególnie z techniki pomiarowej, jaką jest kolorymetria. Prezentowane w niniejszej rozprawie badania koncentrowały się wokół problemów niskopoziomowego przetwarzania, obrazu (ang. low level processing), począwszy od zagadnienia reprezentacji barwy w obrazach, poprzez pozyskiwanie obrazu barwnego, przetwarzanie wstępne (filtracja, kwantyzacja barwy), aż po segmentację obrazu barwnego i ocenę wyników tej segmentacji. W systemach wizyjnych przetwarzających obrazy barwne na tych właśnie etapach w najwyższym stopniu przejawia się specyfika barwy. Dla wysokopoziomowego przetwarzania obrazu (ang. high level processing), polegającego m.in. na ekstrakcji cech. rozpoznawaniu obiektów czy interpretacji sceny barwa obszaru może stanowić jedną z wielu wykorzystywanych cech, obok kształtu, rozmiaru i tekstury. W rozdziale 2 przeprowadzono przegląd kilkunastu najbardziej popularnych w dziedzinie przetwarzania obrazów przestrzeni barw. Uwagę skupiono na transformacjach z bazowej przestrzeni RGB i podstawowych właściwościach definiowanych w ten sposób przestrzeni. Szczególną uwagę zwrócono na liniowość transformacji, stabilność obliczeniową transformacji oraz ich równomierność percepcyjną. Wskazano również na możliwość sprzętowej implementacji takich transformacji oraz znajdujące coraz więcej zastosowań podejście kwaternionowe do reprezentacji barwy w obrazach. W rozdziale 3 przedstawiono cztery zagadnienia związane z pozyskiwaniem obrazów barwnych. Są one silnie związane z najważniejszymi urządzeniami wchodzącymi w skład systemu wizyjnego (kamery, framegrabbery, układy oświetleniowe itp.). Badania w tym zakresie wymagały konfigurowania systemów wizyjnych oraz stosowania próbek z atlasów barw i specjalnych wzorników barwy. Zaproponowano i sprawdzono procedurę kalibracji kolorymetrycznej systemu wizyjnego. Na przykładzie cyfrowego aparatu fotograficznego pokazano, jak standaryzacja przestrzeni RGB może poprawić jego wierność odwzorowania barw. Do oceny wierności stosowano najnowsze formuły różnicy barw proponowane przez Międzynarodową Komisję Oświetleniową. Dla kanałów jednoprzetwornikowej kamery CCD wyznaczono widmowe charakterystyki czułości, nie używając specjalizowanego sprzętu pomiarowego. Globalnym atrybutem pozyskiwanego obrazu barwnego jest jego barwność. Dlatego definicję tego ważnego pojęcia dołączono do tego "sprzętowego" rozdziału, a wśród jego badanych właściwości znalazły się zależności od lokalnych atrybutów percepcyjnych takich, jak odcień, nasycenie i jasność. Znajomość właściwości przestrzeni barw pozwala na odpowiedni dobór przestrzeni do określonego zadania przetwarzania obrazów. Rozdział 4 przybliża właściwości ważnej i często stosowanej klasy przestrzeni barw objętej wspólnym oznaczeniem HSx. Pokazano, że stosowanie kątowej skali odcieniowej może być źródłem problemów nawet podczas prostych działań arytmetycznych. Z drugiej strony niezmiennicze właściwości odcienia i nasycenia stanowią ważną zaletę tych przestrzeni. Przedstawiono badania doświadczalne, które pokazały przewagę wersji HSI nad 3 innymi wersjami przestrzeni percepcyjnych. Stosując stosunkowo proste przekształcenia algebraiczne wyprowadzono wzory na składowe HLS negatywu, co pozwala dokonywać tej operacji bez konieczności powrotu do przestrzeni RGB. Nierównomierny rozkład punktów-barw w bryłach barw przestrzeni HSx dopełnia właściwości tych przestrzeni. Drugą, przestrzenią barw omawianą w rozdziale 4 jest przestrzeń K1K2K3 będąca wynikiem transformacji Karhunena-Loevego na obrazie RGB. Transformacja KLT zapewnia dekorelację składowych barwy. Wiele uwagi poświęcono efektywnej realizacji tej złożonej obliczeniowo transformacji. Przetwarzając zbiór reprezentatywnych obrazów pokazano zjawisko skupienia energii w pierwszej składowej przestrzeni K1K2K3. Zauważono również, że w zmiennych warunkach oświetleniowych kontrast w obrazie K1 utrzymuje się znacznie dłużej niż w obrazie luminancyjnym Y. Rozdział 5, najobszerniejszy, poświęcono zagadnieniu segmentacji obrazów barwnych, które ma kluczowe znaczenie w zastosowaniach. Ogólny sens segmentacji obrazu to działanie na skończonym zbiorze pikseli w celu jego podziału na podzbiory (np. obszary) zawierające podobne elementy. Zakres rozpatrywanych w tym rozdziale technik ograniczono do technik pikselowych (progowanie i klasteryzacja) i obszarowych. Wiele miejsca poświęcono progowaniu obrazów barwnych w przestrzeni HSI, w szczególności z wykorzystaniem wiedzy o tle oraz w zastosowaniu do detekcji odblasków. Podobnie szeroko badano technikę klasteryzacyjną k-means zwracając uwagę na dobór parametrów. Omówiono dwie wersje techniki rozrostu obszaru: ziarnistego i nieziarnistego rozrostu. Ta ostatnia automatyczna technika segmentacji daje szczególnie dobre wyniki, zależne od stosowanego kryterium jednorodności wraz z progiem oraz od przestrzeni barw. Odpowiedni dobór kryterium, progu i przestrzeni pozwala ignorować cienie i odblaski w obrazie. Przetwarzanie końcowe, np. usuwanie małych obszarów, pozwala dodatkowo poprawić jakość segmentacji. Miarą jakości segmentacji mogą być specjalne funkcje oceny, których przydatność została potwierdzona w pracy. Wynik segmentacji w postaci obrazu binarnego może być punktem wyjścia do detekcji cech kształtowych (współczynniki kształtu, momenty itp.) i topologicznych (np. liczba Eulera), opisujących obiekty przedstawione w obrazie. Następny rozdział odnosi się do zagadnień poprzedzających segmentację, tzn. przetwarzania wstępnego, które ograniczono do kwantyzacji barwy i filtracji odszumiającej. Przeprowadzono badania 3 metod kwantyzacji: równomiernej w RGB oraz w HSV, a także kwantyzacji metodą k-means. Do oceny wyników kwantyzacji stosowano w rozprawie miarę teoriosygnałową PSNR, różnicę barw AE oraz dodatkowo zaproponowano różnicę barwności AM. W ramach badań nad filtracją poddano porównaniu kilka nieliniowych filtrów zachowujących krawędzie. Zaproponowano zastosowanie funkcji oceny segmentacji do zbadania wpływu filtracji odszumiającej na wyniki segmentacji. Wynikiem szczególnego zainteresowania autora barwnością obrazu było zastosowanie tego globalnego atrybutu percepcyjnego do porównania skalarnego i wektorowego filtru medianowego. W rozdziale 7 podsumowano najważniejsze wyniki i osiągnięcia rozprawy.

EN

Colour images are the sources of rich information on objects in the scene. Therefore, its acquisition and processing can significantly simplify both object recognition and location processes. Nowadays an easy access to the equipment for colour image acquisition e.g. scanners, digital still cameras etc. and still grow-up of computational power are the reasons of colour images use instead of the grey-level images. The binary and monochrome images are still important in some applications of the machine vision, medical image processing, etc. Although, the more complicated colour image processing the fuller colour information is. Apart from the use of the methods popular for the monochrome image processing, the digital colour image processing uses the results of colour science, especially the colour measurement technique (colorimetry). The research, presented in this monograph, was concentrated around the low level processing problems starting from the representation of colour in digital images, colour image acquisition, preprocessing (filtering, colour quantization), to continue with the problems of colour image segmentation and its evaluation. In the vision systems processing colour images, peculiarity of colour at these low level stages is revealed. In the high level processing (feature extraction, object recognition, scene interpretation etc.) the colour of region can be one of many used features as shape, size or texture. In Chapter 2 a review of the most popular colour spaces in the field of image processing has been presented. The attention has been given to on the transforms from the RGB colour space, defining these spaces and their fundamental properties. The colour spaces were compared on the basis of linearity of transform, their numerical stability and their perceptual uniformity. The possibility of hardware implementation of transforms is suggested. More and more applications of quaternion approach to the representation of colour in images have been noticed. In Chapter 3 four problems from the field of colour image acquisition were discussed. These problems are strongly related to the most important devices in the vision system e.g. cameras, framegrabbers, lighting systems etc. Research in this topic required the vision system configurating and applying special colour charts and colour atlases. The procedure of colorimetric calibration of vision system has been proposed and checked. The example of digital still camera has shown that standardization of RGB space can improve colour reproduction accuracy. New colour difference formulas, currently proposed by CIE, have been applied to evaluation of accuracy. The spectral sensitivity curves for channels of one-chip CCD camera have been determined without use of any special equipment. The global attribute of acquired colour image is its colourfulness. Therefore, the definition of this important concept has been attached to this "hardware-oriented" Chapter and the impact of such local perceptual attributes as the hue, saturation and lightness on image colourfulness has been studied. The knowledge of the colour space properties is very useful for the process of space selection in defined task of the colour image processing. Chapter 4 brings the properties of the important and frequently used class of colour spaces closer, which is named HSx. The use of angular scale for hue can be a source of problems even during the simple arithmetic operations. From the other hand the invariant of hue and saturation is important advantage of these spaces. Experimental research presented in this work confirmed that the HSI colour space has an advantage over the three other versions of perceptual colour spaces. Using relatively simple algebraic transformations the formulae for HLS components of negative image were derived, that makes it possible to do this operation without necessity to go back to the RGB space. Non-uniform distribution of points-colours in the HSx colour solids is also an important property of these spaces. The second colour space described in Chapter 4 is K1K2K3 space that is the result of Karhunen-Loeve transformation on the RGB image. This transform decorrelates the colour components. Much attention was devoted to the effective realization of this computationally complex transform. As the result of the representative images processing an effect of the energy compaction for the first component in the K1K2K3 space was shown. It was also observed that in the changeable lighting conditions the contrast in the image K1 remains significantly higher than the contrast in the luminance image Y. Chapter 5, the longest Chapter of this monograph, is devoted to the problem of colour image segmentation that is very important in many applications. In general sense the image segmentation is an action on limited set of pixels with the aim of dividing into subsets (e.g. regions) with similar elements. The range of the considered segmentation techniques has been limited to the pixel-based (thresholding, clustering) and region-based techniques. A lot of place is devoted to colour image thresholding in HSI colour space, in particular the application of knowledge on the background and application to highlight detection. Also the fc-means clustering technique with its parameters has been thoroughly tested. Two versions of region growing technique have been presented: seeded and unseeded versions. The latter automatic technique generates good results. Proper selection of homogeneity criterion, threshold and colour space allows to ignore shadows and highlights in the image. Postprocessing (e.g. small region removing) additionally improves the quality of the segmentation. Special evaluation functions, which usefulness has been confirmed in the research, can be used for the measurement of quality of the segmentation. The segmentation result, in the form of binary image, can be a good output to the detection of shape (shape factors, spatial moments etc.) and topological (e.g. the Euler number) features, describing objects in the image. The next Chapter concerns the stage preceding the image segmentation i.e., the preprocessing stage, in which the research has been restricted to the problems of colour quantization and denoising filtering. Three colour quantization methods: uniform quantization in RGB space, uniform quantization in HSV space and /c-means technique, have been investigated. The following measures for evaluation of the quantization results were used: the fundamental signal processing measure PSNR, the colour difference AE and additionally proposed difference of colourfulness AM. Within the framework of the filter testing a few nonlinear edge preserving filters have been compared. In this Chapter the use of evaluation function, originally proposed for the image segmentation, for denoising filtering, is also proposed. As a result of special interest in the image colourfulness was an application of this global perceptual attribute in comparison of scalar and vector median filters. In the last Chapter the most important results and achievements of this monograph have been summarized.

Słowa kluczowe

PL

obrazy barwne; przetwarzanie obrazów barwnych; przestrzeń barw

EN

low level processing; high low level processing; colour image processing

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Automatyka / Politechnika Śląska
• Rocznik: 2006
• Tom: z. 147
• Strony: 3--217
• Opis fizyczny: Bibliogr. 314 poz.

Twórcy

autor

Palus H.

• Instytut Automatyki Politechnika Śląska 44-100 Gliwice, ul.Akademicka 16 tel. [phone],

Bibliografia

• [1] R. Adams and L. Bischof. Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6):641-647, 1994.
• [2] H. Ailisto and T. Piironen. Evaluation of color representation methods in a practical vision system. In: Proceedings of the 5th Scandinavian Conference on Image Analysis, vol. 1, Stockholm, Sweden, 1987.
• [3] M. Ali, W. Martin and J. Aggarwal. Color-based computer analysis of aerial photographs. Computer Graphics and Image Processing, 9(3):282-283, 1979.
• [4] M. Anderberg. Cluster Analysis for Applications. Academic Press, New York, USA, 1973.
• [5] I. Andreadis, M. Browne and J. Swift. Image pixel classification by chromaticity analysis. Pattern Recognition Letters, ll(2):51-58, 1990.
• [6] I. Andreadis and P. Tsalides. Coloured object recognition using invariant spectral features. Journal of Intelligent and Robotic Systems, 13(1):93-106, 1995.
• [7] I. Andreadis, (ed.). Special issue on color imaging. Pattern Recognition. 35(8):1641-1806. 2002.
• [8] J. Astola, P. Haavisto and Y. Neuvo. Vector median filters. Proceedings of the IEEE, 78(4):678-689, 1990.
• [9] V. Athitsos, M. Swain and C. Frankel. Distinguishing photographs and graphics on the World Wide Web. In: Proceedings of Workshop on Content-Based Access of Image and Video Libraries (CBAIVL '97), 10-17, San Juan, Puerto Rico 1997.
• [10] A. Atsalakis, N. Papamarkos, N. Kroupis, D. Soudris and A. Thanailakis. Colour quantization technique based on image decomposition and its embedded system implementation. IEE Proc.-Vis. Image Signal Process., 151(6):511-524, 2004.
• [11] M. Babu, C.-H. Lee and A. Rosenfeld. Determining plane orientation from specular reflectance. Pattern Recognition, 18(l):53-62, 1985.
• [12] J. Bajon, M. Cattoen and S. Kim. Real-time colorimetric transformations using in robot vision system. In: Proceedings of MIC AD, 76-86, Paris, France 1985.
• [13] J. Bajon, M. Cattoen and L. Liang. Identification of multicoloured objects using a vision module. In: Proceedings of the 6 th RoViSeC, 21-30, Paris, France 1986.
• [14] D. Baker, S. Hwang and J. Aggarwal. Detection and segmentation of man-made objects in outdoor scenes: concrete bridges. Journal of Optical Society of America A, 6(6):938-950. 1989.
• [15] P. Bakker, L. van Fliet and P. Verbeek. Edge preserving orientation adaptive filtering. In: Proceedings of 5th Annual Conference of the Advanced School for Computing and Imaging, 207-213. Heijen, The Netherlands 1999.
• [16] A. Bal, H. Palus, P. Wołczyk. Selected properties of perceptual colour spaces. In: M. Ku-rzyński, E. Puchała, M. Woźniak, (eds.), Computer Recognition Systems, 125-130. Wroclaw University of Technology, Wroclaw 2001.
• [17] A. Bardos and S. Sangwine. Selective vector median filtering of colour images. In: Proceedings of 6th Int. Conf. on Image Processing and its Applications, 708-711, Dublin. Ireland 1997.
• [18] M. Barth, S. Parthasarathy, J. Wang and et al. A color vision system for microelectronics: applications to oxide thickness measurements. In: Proceedings of IEEE Int. Conf. on Robotics and Automation, vol. 2, 1242-1247, San Francisco, USA 1986.
• [19] K. B. Benson. Television Engineering Handbook. McGraw-Hill, New York 1986.
• [20] D. Bereska. Badania nad korelacją pomiędzy składowymi wektora barwy w cyfrowych obrazach barwnych. Praca doktorska, Instytut Automatyki Politechniki Śląskiej, Gliwice.
• [21] D. Bereska, H. Palus. Korelacja składowych sygnału wyjściowego jednoprzetwornikowej kamery kolorowej CCD. W: Materiały VI Konferencji Naukowej "Czujniki Optoelektroniczne i Elektroniczne" COE 2000, 487-492, Gliwice 2000.
• [22] D. Bereska and H. Palus. Correlation of colour components of camera output signal and decorrelation methods. In: Optoelectronic and Electronic Sensors IV. Proceedings of SPIE. vol. 4516, 299-306, Gliwice 2001.
• [23] D. Bereska, H. Palus. Wyznaczanie charakterystyk widmowych kamer kolorowych CCD. W: Materiały VII Konferencji Naukowej "Czujniki Optoelektroniczne i Elektroniczne" COE 2002, 231-236, Rzeszów 2002.
• [24] W. S. Berris and S. Sangwine. Automatic quantitative analysis of healing skin wounds using colour digital image processing. World Wide Wounds, The Electronic Journal of Wound Management Practice, SMTL, Brigend, Wales, Sept. 1997.
• [25] D. Berry. Colour recognition using spectral signatures. Pattern Recognition Letters, 6(l):69-75, 1987.
• [26] N. L. Bihan and S. Sangwine. Quaternion principal component analysis of color images. In: Proceedings of IEEE International Conference on Image Processing, vol. 1, 809-812, Barcelona, Spain 2003.
• [27] R. V. den Boomgaard. Decomposition of the Kuwahara-Nagao operator in terms of linear smoothing and morphological sharpening. In: Proceedings of the 6th International Symposium on Mathematical Morphology, 283-292, Sydney, Australia 2002.
• [28] M. Borsotti, P. Campadelli and R. Schettini. Quantitative evaluation of color image segmentation results. Pattern Recognition Letters, 19(8):741-747, 1998.
• [29] R. Boynton. Human color perception. In: K. Leibovic, (ed.), Science of Vision, 211-253. Springer-Verlag, Berlin, Germany 1990.
• [30] C. Brice and C. Fennema. Scene analysis using regions. Artificial Intelligence, 1(3):205-226, 1970.
• [31] S. Brock-Gunn and T. Ellis. Using colour templates for target identification and tracking. In: Proceedings of BMVC'92, 207-216, Leeds, UK 1992.
• [32] D. Brockelbank and Y. Yang. An experimental investigation in the use of color in computational stereopsis. IEEE Trans, on Systems. Man and Cybernetics. 19(6):1365-1383, 1989.
• [33] L. Brun and A. Tremeau. Color quantization. In: G. Sharma, (ed.), Digital Color Imaging Handbook, 589-637. CRC Press, Boca Raton, USA 2003.
• [34] Cambridge Research & Instrumentation, CRI, Inc. Woburn, MA, USA, http://www.cri-inc.com.
• [35] D. Cavagnino and A. Werbrouck. Color image compression by means of separable Karhunen-Loeve transform and vector quantization. In:Proceedings of the 5th Int. Workshop on Systems, Signals and Image Processing, 235-238, Zagreb, Croatia 1998.
• [36] M. Celenk. Analysis of color images of natural scenes. Journal of Electronic Imaging, 4(4):382-396, 1995.
• [37] J. Chamorro-Martinez, D. Sanchez and B. Prados-Suarez. A fuzzy color image segmentation applied to robot vision. In: J. M. Benitez, O. Cordon, F. Hoffman and R. Roy, (eds.), Advances in Soft Computing, Engineering, Design and Manufacturing, 129-138, Springer-Verlag, Berlin, Germany 2003.
• [38] T. Chen and Y. Lu. Color image segmentation - an innovative approach. Pattern Recognition, 35(2):395-405, 2002.
• [39] Y. Chen, P. Hao and A. Dang. Optimal transform in perceptually uniform color space and its application in image coding. In: Proceedings of I CI AR. 2004, 269-276, Porto, Portugal.
• [40] II. Cheng, X. Jiang, Y. Sun and J. Wang. Color image segmentation: advances and prospects. Pattern Recognition. 34 (12): 2259-2281, 2001.
• [41] H.-D. Cheng. A hierarchical approach to color image segmentation using homogeneity. IEEE Transactions on Image Processing, 9(12):2071-2082, 2000.
• [42] Y. Cheng. Mean shift, mode seeking and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(8): 790-799, 1995.
• [43] CIE Supplement No.2 to CIE Publication No.15, Recommendations on uniform colour spaces, colour-difference equations, psychometric colour terms, Bureau Central de la CIE, Paris 1978.
• [44] CIE Publication 116-1995, Industrial colour-difference evaluation, CIE Central Bureau. Vienna 1995.
• [45] CIE TC1-34 Final Report, The CIE 1997 Interim Colour Appearance Model (Simple Version) CIECAM97s, 1998.
• [46] CIE Publication 141-2001, Improvement to industrial colour difference evaluation, CIE Central Bureau, Vienna 2001.
• [47] Ph. Colantoni. Couleur.org, Personal webpage about colour spaces, 2003, http://www.couleur.org.
• [48] C. Connolly. The relationship between colour metrics and the appearance of three-dimensional coloured objects. Color Research and Application, 21(5):331-337, 1996.
• [49] C. Connolly and T. Fliess. A study of efficiency and accuracy in the transformation from RGB to CIELAB color space. IEEE Transactions on Image Processing, 6(7):1046-1048, 1997.
• [50] C. Connolly and H. Palus. Practical system considerations. In: S. Sangwine and R. Home, (eds.), The Colour Image Processing Handbook, 129-146. Chapman and Hall, London, UK 1998.
• [51] V. Coutance, T. Baron and M. Briot. Segmentation d'images couleur in robotique. In: Proceedings of 7th Congres AFCET, 1115-1122, Paris, France 1989.
• [52] D. Crevier. Computing statistical properties of hue distributions for image analysis. In: Intelligent Robots and Computer Vision XII, Proceedings of SPIE, vol. 2055, 613-623.
• [53] W. Cudny and L. Chmielewski. Light wave length measurement with a colour camera. Machine Graphics and Vision, 2(3):251-260, 1993.
• [54] F. Cutzu, R. Hammoud and A. Leykin. Estimating the photorealism of images: Distinguishing paintings from photographs. In: Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, 305-312, Madison, USA 2003.
• [55] Y. Dai and Y. Nakano. Face-texture model based on SGLD and its application in face detection in a color space. Pattern Recognition, 29(6):1007-1017, 1996.
• [56] C. Dalton. The measurement of the colorimetric fidelity of television cameras. Journal of the Institution of Electronic and Radio Engineers, 58(4):181-186, 1988.
• [57] M. Das, E. Riseman and B. Draper. Focus: Searching for multi-colored objects in a diverse image database. In: Proceedings of the IEEE Conference on Computer Vrision and Pattern Recognition '97, 756-761. San Juan, Puerto Rico 1997.
• [58] E. Davies. Principles and design graphs for obtaining uniform illumination in automated visual inspection. In: Proceedings of 6th IEE Conference on Image Processing and its Applications, 161-165, Dublin, Ireland 1997.
• [59] B. Deknuydt, J. Smolders, L. V. Eycken and A. Oosterlinck. Color space choice for nearly reversible image compression. In: Visual Communications and Image Processing. Proceedings of SPIE, vol. 1818, 1300-1311, 1992.
• [60] C.-H. Demarty and S. Beucher. Color segmentation using an HLS transformation. In: Proceedings of ISMM'98, 231-238, Amsterdam, The Netherlands 1998.
• [61] Y. Deng, C. Kenney, M. Moore and B. Manjunath. Peer group filtering and perceptual color image quantization. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS), vol. IV, 21-24, Orlando, USA 1999.
• [62] Y. Deng and B. Manjunath. Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence. 23(8):800-810, 2001.
• [63] A. De Rosa, A. M. Bonacchi, V. Cappellini and M. Barni. Image segmentation and region filling for virtual restoration of art-works. In: Proceedings of IEEE Int. Conference on Image Processing (ICIP'01), vol. I, 562-565, Thessaloniki, Greece 2001.
• [64] Digital camera product reviews/previews, Minolta, Dimage, 2002, http://www.dpreview.com/reviews/minoltadimage7.
• [65] M. Domański. Zaawansowane techniki kompresji obrazów i sekwencji wizyjnych. Wydawnictwa Politechniki Poznańskiej, Poznań 2000.
• [66] M. Domański and M. Bartkowiak. Compression. In: S. Sangwine and R. Horne, (eds.), The Colour Image Processing Handbook, 242-304. Chapman and Hall, London, UK 1998.
• [67] M. Domański and K. Rakowski. Lossless and near-lossless image compression with color transformations. In: Proceedings of International Conference on Image Processing, vol. 3, 454-457, Thessaloniki, Greece 2001.
• [68] R. Dony and S. Wesolkowski. Edge detection on color images using RGB vector angle. In: Proc. IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). 687-692, Edmonton, Canada 1999.
• [69] A. Empacher. Z. Sęp, A. Żakowska, W. Żakowski. Mały słownik matematyczny. Wiedza Powszechna, Warszawa 1970.
• [70] P. Engeldrum. Extending image quality models. In: Proceedings of PICS2002: IS&T PICS Conference, 65-69, Portland, USA 2002.
• [71] M. Fairchild. Color Appearance Models. Addison-Wesley, Reading, USA 1998.
• [72] J. Fan, D. Yau, A. Elmagarmid and W. Aref. Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Transactions on Image Processing, 10(10):1454-1466, 2001.
• [73] O. Faugeras. Digital color image processing within the framework of human visual model. IEEE Transactions on ASSP, 27(4):380-383, 1979.
• [74] E. Fedorovskaya, A. de Ridder and F. Blommaert. Chroma variations and perceived quality of color images of natural scenes. Color Research and Applications. 22(2):96-110, 1997.
• [75] F. Ferri and E. Vidal. Colour image segmentation and labeling through multiedit - condensing. Pattern Recognition Letters, 13(8):561-568, 1992.
• [76] S. Fischer, P. Schmid and J. Guillod. Analysis of skin lesions with pigmented networks. In: Proceedings of 3rd IEEE International Conference on Image Processing, vol. 1, 323-326, Lausanne, Switzerland 1996.
• [77] M. Fleck, D. Forsyth and C. Bregler. Finding nacked people. In: Proceedings of Ąth European Conference on Computer Vision, vol. 2, 592-602, Cambridge, UK 1996.
• [78] J. Foley and A. V. Dam. Fundamentals of Interactive Computer Graphics. Addison-Wesley, Reading, USA 1982.
• [79] D. Forsvth and J. Ponce. Computer Vision. Prentice Hall, Upper Saddle River, USA 2003.
• [80] Z. Fortuna Warszawa 1998.
• [81] J. Freixenet, X. Munot, D. Raba, J. Marti and X. Cuti. Yet another survey on image segmentation: region and boundary information integration. In: A. Heyden et al., (eds.), ECCV 2002, pages 408-422. Springer-Verlag, Berlin, Germany 2002.
• [82] H. Frey. Digitale Bildverarbeitung in Farbräumen, Dr.-Ing. Dissertation. TU München, München, Germany 1988.
• [83] H. Frey and H. Palus. Sensor calibration for video-colorimetry. In: Proceedings of Workshop on Design Methodologies for Microelectronics and Signal Processing, 109-113, Gliwice-Kraków 1993.
• [84] J. Fridrich, R. Du and L. Meng. Steganalysis of LSB encoding in color images. In: Proceedings of the IEEE Int. Conf. on Multimedia and Expo, vol. 3, 1279-1282, New York, USA 2000.
• [85] G. Gagliardi, G. Hatch and N. Sarkar. Machine vision applications in the food industry. In: Proceedings of Vision'85 Conference, 524-538, Detroit, USA 1985.
• [86] H. Gao, W.-C. Siu and C.-H. Hou. Improved techniques for automatic image segmentation. IEEE Transactions on Circuits and Systems for Video Technology, 11(12):1273-1280, 2001.
• [87] C. Garcia and G. Tziritas. Face detection using quantized skin color regions merging and wavelet packet analysis. IEEE Transactions on Multimedia, l(3):264-277, 1999.
• [88] J. Gauch. Segmentation and edge detection. In: S. Sangwine and R. Home, (eds.), The Colour Image Processing Handbook, 163-187. Chapman and Hall, London, UK 1998.
• [89] J. Gauch and C. Hsia. A comparison of three segmentation algorithms in four color spaces. In: Proceedings of SPIE, vol. 1818, 1168-1175, 1992.
• [90] L. Gericke, R. Schumitz, O. Richter and K. Schöne. Farbenkatalog für die Gestaltung. I. Ergänzungsteil. Zentralinstitut für die Gestaltung des DAMW, Berlin, Germany 1978.
• [91] R. Gershon. Aspects of perception and computation in color vision. CVGIP, 32(2):244-277, 1985.
• [92] Y. Gong and M. Sakauchi. Detection of regions matching specified chromatic features. Computer Vision and Image Understanding, 61(2):263-269, 1995.
• [93] Y. Gong, H. Zhang and H. Chua. An image database system with content capturing and fast image indexing abilities. In: Proceedings of the IEEE Int. Conf. on Multimedia Computing and Systems (ICMCS'9Ą), pages 121-130, Boston, USA 1994.
• [94] R. Gonzalez. Digital Image Processing. Addison-Wesley, Reading, USA 1977.
• [95] R. Gonzalez and R. Woods. Digital Image Processing. Addison-Wesley, Reading, USA 1992.
• [96] J. Gordillo. Colour representations for a vision machine. In: Proceedings of 2nd Int. Conf. on Machine Intelligence, 375-385, London, UK 1985.
• [97] P. Green and L. MacDonald. Colour Engineering, Achieving Device Independent Colour. John Wiley & Sons, Chichester, UK 2002.
• [98] I. Grinias, Y. Mavrikakis and G. Tziritas. Region growing colour image segmentation applied to face detection. In: Proc. of International Workshop Low Level Video Coding, Athens, Greece 2001.
• [99] A. Gunzinger, S. Mathis and W. Guggenbuehl. Real time color classification. In: V. Cap-pellini, (ed.), Time-Varying Image Processing and Moving Object Recognition, 82-87. Elsevier, Amsterdam, The Netherlands 1990.
• [100] R. Haralick and L. Shapiro. Glossary of computer vision terms. Pattern Recognition, 24(l):69-93, 1991.
• [101] R. Haralick and L. Shapiro. Computer and Robot Vision. Addison-Wesley, Reading, USA 1993.
• [102] J. Hardeberg. Desktop scanning to sRGB. In: Color Imaging: Device Independent Color, Color Hardcopy, and Graphic Arts V, Proceedings of SPIE, vol. 3963, 47-57, 2000.
• [103] J. Hardeberg, H. Brettel and F. Schmitt. Spectral characterisation of electronic cameras. In: Electronic Imaging: Processing, Printing and Publishing in Color. Proceedings of SPIE, vol. 3409, 100-109, 1998.
• [104] D. Hasler and S. Süsstrunk. Measuring colourfulness for natural images. In: Electronic Imaqinq 2003: Human Vision and Electronic Imaqinq VIII. Proceedinqs of SPIE. vol. 5007, 87-95, 2003.
• [105] G. Healey. Segmenting images using normalized color. IEEE Transactions on Systems, Man and Cybernetics, 22(l):64-73, 1992.
• [106] E. Hering. Outlines of a Theory of the Light Sense. Harvard University Press, Cambridge, USA 1964.
• [107] D. Hogg. Shape in machine vision. Image and Vision Computing, 11(6):309-316, 1993.
• [108] S. Hojjatoleslami and J. Kittler. Region growing: A new approach. IEEE Transactions on Image Processing, 7(7):1079-1084, 1998.
• [109] V. Hong, H. Palus and D. Paulus. Edge preserving filters on color images. Lecture Notes in Computer Science, 3039:35-42, 2004.
• [110] S. Horowitz and T. Pavlidis. Picture segmentation by a tree traversal algorithm. Journal of Association for Computing Machinery, 23(2):368-388, 1976.
• [111] M. Hu. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory, 8(2):179-187, 1962.
• [112] K. Huang, Q. Wang and Z. Wu. Color image enhancement and evaluation algorithm based on human visual system. In: Proceedings of ICASSP'2004, vol. 3, 721-724, Montreal, Canada 2004.
• [113] Q. Huang, B. Dom, D. Steele, J. Ashley and W. Niblack. Foreground/background segmentation of color images by integration of multiple cues. In: Proceedings of IEEE Int. Conf. on Image Processing, vol. 1, 246-249, Washington, D.C., USA 1995.
• [114] R. Hunt. The specification of colour appearance. Color Research and Applications, 2:55-69 and 109-120, 1977.
• [115] IEC Publ. 61966-2-1: Multimedia systems and equipment - Color measurement and management - Part 2-1: Default RGB colour space - sRGB, 1999.
• [116] N. Ikonomakis, K. Plataniotis and A. Venetsanopoulos. Color image segmentation for multimedia applications. Journal of Intelligent and Robotic Systems, 28(l-2):5-20, 2000.
• [117] N. Ikonomakis, K. Plataniotis and A. Venetsanopoulos. Unsupervised seed determination for a region-based color image segmentation scheme. In: Proceedings of IEEE Int. Conference on Image Processing (ICIP'00), vol. I, 537-540, Vancouver, Canada 2000.
• [118] ISO/IEC 13818-2/ITU-T Rec.262, Generic coding of moving pictures and associated audio, Pt.2: Video, 2000.
• [119] A. Jain and R. Dubes. Algorithms for Clustering Data. Prentice Hall. Englewood Cliffs, USA 1988.
• [120] M. Jamzad, B. Sadjad, V. Mirrokni, M. Kazemi and et al. A fast vision system for middle size robots in Robocup. Lecture Notes in Computer Science, 2377:71-80, 2002.
• [121] X. Jie and S. Peng-Fei. Natural color image segmentation. In: Proc. of IEEE Int. Conf. on Image Processing, (ICIP'03), vol. I, 973-976, Barcelona, Spain 2003.
• [122] J. R. Jordan III, W. Geisler and A. Bovik. Color as a source of information in the stereo correspondence process. Vision R.esearch, 30(12):1955-1970, 1990.
• [123] D. Judd and G. Wyszecki. Color in Business. Science, and Industry, 3rd ed. John Wiley & Sons, New York, USA 1975.
• [124] T. Kaczorek. Wektory i macierze w automatyce i elektrotechnice. WN-T, Warszawa 1998.
• [125] D. Karakos and P. Trahanias. Generalized multichannel image filtering structures. IEEE Transactions on Image Processing, 6(7):1038-1045, 1995.
• [126] A. Kasiński, R. Bączyk. Robust landmark recognition with application to navigation. In: M. Kurzyński, E. Puchała, M. Woźniak, (eds.), Computer Recognition Systems, 401-407. Wrocław University of Technology, Wroclaw 2001.
• [127] G. Kay and G. D. Jager. A versatile colour system capable of fruit sorting and accurate object classification. In: Proceedings of the COMSIG'92, 145-148, Capetown, RSA 1992.
• [128] N. Kehtarnavaz, N. Griswold and D. Kang. Stop-sign recognition based on color/shape processing. Machine Vision and Applications, 6(4):206-208, 1993.
• [129] J. Kender. Saturation, hue, and normalized color: calculation, digitization effects, and use, Technical Report. Carnegie-Mellon University, Pittsburgh, USA 1976.
• [130] J.-Y. Kim, J.-C. Shim and Y.-H. Ha. Color image enhancement based on modified IHS coordinate system. In: Intelligent Robots and Computer Vision XI, Proceedings of SPIE. vol. 1825. 366-377, 1992.
• [131] S. Ko and Y. Lee. Center weighted median filters and their application to image enhancement. IEEE Transactions on Circuits and Systems, 38(9):984-993, 1991.
• [132] Kodak images, Resources of Center for Image Processing Research, Rensselaer Polytechnic Institute, Troy, NY, USA 2002, http://www.cipr.rpi.edu/resource/stills/kodak.html.
• [133] A. Koschan. Dense stereo correspondence using polychromatic block matching. In: Proc. of 5th International Conference on Computer Analysis of Images and Patterns, 538-542, Budapest, Hungary 1993.
• [134] R. Kuehni. Color Space and Its Divisions, Color Order from Antiquity to the Present. John Wiley & Sons, Hoboken, USA 2003.
• [135] M. Kuwahara, K. Hachimura, S. Eiho and M. Kinoshita. Processing of ri-angiocardiographic images. In: K. Preston and M. Onoe, (eds.), Digital Processing of Biomedical Images, 187-202. Plenum Press, New York, USA 1976.
• [136] S. Ledley, M. Buas and T. Golab. Fundamentals of true-color image processing. In: Proceedings of 10th Int. Conf. on Pattern Recognition, 791-795, Atlantic City, USA 1990.
• [137] H. C. Lee. Method for computing the scene-illuminant chromaticity from specular highlights. Journal of Optical Society of America A, vol.3, no.10, 1694-1699, 1986.
• [138] R. Lee. Colorimetric calibration of a video digitizing system: algorithm and applications. Color R.esearch and Applications, 13(3):180-186, 1988.
• [139] H. Levkowitz and G. T Herman. GLHS: a generalized lightness, hue and saturation color model. CVGIP: Graphical Model and Image Processing, vol. 55, no.4, 271-285, 1993.
• [140] M. Li, I. Sethi, D. Li and N. Dimitrova. Region growing using online learning. In: Proceedings of International Conference on Imaging Science. Systems, and Technology. (CISST'03), vol. I, 73-76, Las Vegas, USA 2003.
• [141] Y. Lim and S. Lee. On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23(9):935-952, 1990.
• [142] X. Lin and S. Chen. Color image segmentation using a modified HSI system for road following. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, 1998-2003, Sacramento, USA 1991.
• [143] B. J. Lindbloom. Personal webpage, 2001, http://www.brucelindbloom.com.
• [144] J. Liu and Y.-H. Yang. Multiresolution color image segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no.7, 689-700, 1994.
• [145] L. Lucchese and S. Mitra. Advances in color image segmentation. In: Proceedings of Globecom'99, vol. IV, 2038-2044, Rio de Janeiro, Brazil 1999.
• [146] Machine vision illumination products, Siemens NERLITE, Weare, NH, USA, http://www.nerlite.com.
• [147] J. Mac Queen. Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematics. Statistics, and Probabilities, vol. I, 281-297, Berkeley and Los Angeles, USA 1967.
• [148] M. Mahy, L. V. Eyckden and A. Oosterlinck. Evaluation of uniform color spaces developed after the adoption of CIELAB and CIELUV. Color Research and Application. 19(2): 105-121, 1994.
• [149] S. Makrogiannis, G. Economou and S. Fotopoulos. A graph theory approach for automatic segmentation of color images. In: Proc. of International Workshop Low Level Video Coding, 162-166, Athens, Greece 2001.
• [150] W. Malina and M. Smiatacz. Metody cyfrowego przetwarzania obrazów. Akademicka Oficyna Wydawnicza EXIT, Warszawa 2005.
• [151] G. Marcu and S. Abe. Blue-print document analysis for color classification. In: Proceedings of 9th Scandinavian Conference on Image Analysis, 569-574, Uppsala, Sweden 1995.
• [152] K. Mardia and P. Jupp. Directional statistics. John Wiley & Sons, Chichester, UK 2000.
• [153] S. Marshall. Review of shape coding techniques. Image and Vision Computing, 7(4):281-294, 1989.
• [154] E. Marszalec. Database - Agfa IT8.7/2 set. University of Joensuu, Finlandia, http: //es. j oensuu. f i/~spectral/databases/download/agf ait872. htm.
• [155] E. Marszalec and M. Pietikäinen. On-line color camera calibration. In: Proceedings of 12th International Conference of Pattern Recognition, vol. 1, 232-237. Jerusalem, Israel 1994.
• [156] I. Masaki. Real-time multi-spectral visual processor. In: Proc. 1988 IEEE Int. Conf. on Robotics and Automation, vol. 3, 1554-1559, Philadelphia, USA 1988.
• [157] R. Massen. Color monitoring in the production lines: More than alternative to classical non-imaging colorimetrv. In: Polarization and Color Techniques in Industrial Inspection, Proceedings of SPIE, vol. 3826, 36-43, 1999.
• [158] R. Massen, P. Böttcher and U. Leisinger. Real-time grey level and colour image preprocessing for a vision guided biotechnology robot. In: Proceedings of 7th RoViSeC, 115-122, Zurich, Switzerland 1988.
• [159] C. McCamy, H. Marcus and J. Davidson. A color rendition chart. Journal of Applied Photographic Engineering, 2(3):95-99, 1976.
• [160] A. Mehnert and P. Jackway. An improved seeded region growing algorithm. Pattern Recognition Letters, 18(10):1065-1071, 1997.
• [161] F. Meyer. Color image segmentation. In: Proc. IEE Int. Con}. Image Processing and its Applications, 303-306. Maastricht, The Netherlands 1992.
• [162] O. Milvang and B. Olafsdottir. Discriminating crates from color images. In: Proceedings of 8th Scandinavian Conference on Image Analysis, vol. 1, 659-669, Tromso, Norway 1993.
• [163] D. Mital. G. Lee and T. Khwang. Color vision for industrial applications. In: Proceedings of 16th Annual Conference of IEEE Industrial Electronics Society, 548-551, Pacific Grove, USA 1990.
• [164] D. Monro and J. Nichols. Low bit rate color fractal video. In: Proceedings of IEEE Int. Conf. on Image Processing, vol. 3, 264-267, Washington, D.C., USA 1995.
• [165] R. Moorhead and Z. Zhu. Signal processing aspects of scientific visualization. IEEE Signal Processing Magazine, 12(5):20-41, 1995.
• [166] N. Moroney, M. Fairchild, R. Hunt, C. Li, M. Luo and T. Newman. The CIECAM02 color appearance model. In: Proceedings of IS&T/SID 10th Color Imaging Conference, 23-27, Scottsdale, USA 2002.
• [167] V. Müller. Elimination of specular surface-reflectance using polarized and unpolarized light. In: Proceedings of the Ąth European Conference on Computer Vision, vol. 2, 625-635, Cambridge, UK 1996.
• [168] M. Nagao and T. Matsuyama. Edge preserving smoothing. Computer Graphics and Image Processing, 9(4):374-407, 1979.
• [169] E. Navon, O. Miller and A. Averbuch. Color image segmentation based on adaptive local thresholds. Image and Vision Computing, 23(l):69-85, 2005.
• [170] R. Nevatia. A color edge detector and its use in scene segmentation. IEEE Trans. Systems. Man, and Cybernetics, 7(ll):820-826, 1977.
• [171] M. Nieniewski. Morfologia matematyczna w przetwarzaniu obrazów. Akademicka Oficyna Wydawnicza PLJ, Warszawa 1998.
• [172] Y. Ohta, T. Kanadę and T. Sakai. Color information for region segmentation. Computer Graphics and Image Processing, 13(3):222-241, 1980.
• [173] S. Ong and C. Hew. Segmentation of colour images based on iterative thresholding and merging. In: Proceedings of 2nd Int. Conf. on Image Processing, 721-725, Singapore 1992.
• [174] N. Otsu. A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics, 9(l):62-66, 1979.
• [175] N. Ouerhani, N. Archip, H. Huegli and P. Erard. Visual attention guided seed selection for color image segmentation. In: Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns (CAIP'01), 630-637, WTarsaw, Poland 2001.
• [176] L. Overturf, M. Comer and E. Delp. Color image coding using morphological pyramid decomposition. IEEE Trans, on Image Processing, 4(2):177-185, 1995.
• [177] N. Pal and S. Pal. Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9):1277-1293, 1993.
• [178] H. Palus. Model i badania barwoczułego systemu sensorycznego. Zeszyty Naukowe Politechniki Śląskiej, s. Automatyka, 101:193-204, Gliwice, 1990.
• [179] H. Palus. Cvetnoje mashinnoje zrenie. In: J. Kulikowski and J. Juravlev, (eds.). Lecture Notes of ICB Seminar, 205-215. International Center of Biocybernetics, Warsaw 1991.
• [180] H. Palus. Colour spaces in computer vision. Machine Graphics and Vision, l(3):543-554, 1992.
• [181] H. Palus. Regułowe systemy wizyjne. Zeszyty Naukowe Politechniki Śląskiej, s. Automatyka, 110:279-289, Gliwice 1992.
• [182] H. Palus. Systemy barwnej wizji komputerowej dla potrzeb robotyki. Prace Naukowe ICT Pol. Wrocl, 94:132-139. 1993.
• [183] H. Palus. Barwne stereowidzenie: przegląd metod i analiza możliwości. Prace IPI PAN, 747:1-19, 1994.
• [184] H. Palus. Kalibracja systemu barwnej wizji komputerowej, Raport z pracy BW-l/Raul/94 t.15. Instytut Automatyki Politechniki Śląskiej, Gliwice 1994. (praca niepublikowana).
• [185] H. Palus. Systemy wizji komputerowej w górnictwie? Mechanizacja i Automatyzacja Górnictwa, 287-288(5-6):114-116, 1994.
• [186] H. Palus. Wyznaczanie współczynnika kompaktowości w systemach wizyjnej identyfikacji obiektów. Zeszyty Naukowe Politechniki Śląskiej, s. Automatyka, 113:259-270, Gliwice.
• [187] H. Palus. Barwa w rozpoznawaniu obiektów przez system wizyjny robota przemysłowego. Zeszyty Naukowe Politechniki Śląskiej, s. Automatyka, 116:117-127, 1995.
• [188] H. Palus. Farbe in der Objekterkennung: Versuch der Systematisierung und ein Beispiel. Fachberichte Informatik, Universität Kobłenz-Landau, (15):17-20, 1995.
• [189] H. Palus. Der IHS Farbraum: Eigenschaften und Modifikationen. Schriftenreihe des ZBS, Ilmenau, (l):73-77, 1996.
• [190] H. Palus. Przestrzeń barw IHS w zastosowaniu do rozpoznawania obiektów. Zeszyty Naukowe Politechniki Śląskiej, s. Automatyka, 119:181-191, Gliwice 1996.
• [191] H. Palus. Nutzung des Wissens über die Farbe des Bildhintergrundes zur Segmentierung von Farbbildern. In: D. Paulus and T. Wagner, (eds.), Dritter Workshop Farbbildverarbeitung, 39-43. IR.B-Verlag, Stuttgart, Germany 1997.
• [192] H. Palus. Homogeneity criteria for region-growing image segmentation in IHS colour space. In: Proceedings of 5th Int. Workshop on Systems, Signals and Image Processing, 227-230, Zagreb, Croatia 1998.
• [193] H. Palus. Representations of colour images in different colour spaces. In: S. J. Sangwine and R. E. N. Home, (eds.), The Colour Image Processing Handbook, 67-90. Chapman and Hall, London, UK 1998.
• [194] H. Palus. Wykorzystanie odblasków w obrazach barwnych do zliczania obiektów w scenie. Zeszyty Naukowe Politechniki Śląskiej, s. Automatyka, 125:191-199, 1998.
• [195] H. Palus. Counting of colored objects using highlights. In: Polarization and Color Techniques in Industrial Inspection, Proceedings of SPIE, vol. 3826, 44-51, 1999.
• [196] H. Palus. Remarks on the using colour in computer vision. In: Proceedings of AIC Midterm. Meeting, 327-334, Warsaw 1999.
• [197] H. Palus. Segmentacja obrazów barwnych techniką podziału i łączenia. W: Materiały International Conference on Management Systems, 51-59, Bielsko-Biała 1999.
• [198] H. Palus. Barwa w obrazach cyfrowych. W: Materiały Seminarium "Edukacyjne Systemy Internetowe", 99-105, Bielsko-Biała 2001.
• [199] H. Palus. Latest results in color image processing and its applications. Machine Graphics and Vision, 11 (2/3): 135-137, 2002.
• [200] H. Palus. Region-based colour image segmentation: control parameters and evaluation functions. In: Proceedings of the First European Conference on Cołor in Graphics, Imaging and Vision, (CGIV'02), 259-262, Poitiers, France 2002.
• [201] H. Palus. Estimating the usefulness of preprocessing in colour image segmentation. In: Proc. of 2nd European Conference on Colour in Graphics, Imaging, and Vision (CGIV2004), 197-200, Aachen, Germany 2004.
• [202] H. Palus. Application of colourfulness of the image in colour image quantization. In: Computer Methods and Systems, Proceedings of Conference, vol. 2, 205-210, Kraków 2005.
• [203] H. Palus. Colourfulness of the image and its application in image filtering. In: Proceedings of 5th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT'2005), 884-889, Athens, Greece 2005.
• [204] H. Palus. Performance evaluation of preprocessing in colour image segmentation. Journal of Imaging Science and Technology, 49(6):583-587, 2005.
• [205] H. Palus. Region-based colour image segmentation technique and its properties. Przegląd Elektrotechniczny, (3):41-44, 2005.
• [206] H. Palus. Region growing technique for colour image segmentation. In: Computer Methods and Systems, Proceedings of Conference, vol. 2, 199-204. Kraków 2005.
• [207] H. Palus. Color image segmentation: selected techniques. In: R. Lukac and K. Plataniotis, (eds.), Color Image Processing: Methods and Applications, 103-128. CRC Press, Boca Raton, USA 2006.
• [208] H. Palus. Colorfulness of the image: definition, computation and properties. In: Proceedings ofSPIE, vol. 6158, 05-1-05-6, 2006.
• [209] H. Palus and D. Bereska. The comparison between transformations from RGB colour space to IHS colour space, used for object recognition. In: Proceedings of the 5th Int. Conf. on Image Processing and Its Applications, 825-827, Edinburgh, UK 1995.
• [210] H. Palus, D. Bereska. Segmentacja obrazów w przestrzeni IHS: problem przenoszenia informacji kształtowej. Prace IPI PAN, 772:1-19, 1995.
• [211] H. Palus and D. Bereska. IHS colour space for use in object recognition. In: Proceedings of the Ąth International Symposium on Methods and Models in Automation and Robotics, vol. 3, 979-984, Międzyzdroje 1997.
• [212] H. Palus and D. Bereska. Single-coloured object recognition in scenes with a known background. In: Proceedings of the Ąth Int. Workshop on Systems, Signals and Image Processing, 145-148, Poznań 1997.
• [213] H. Palus and D. Bereska. Colour image segmentation using region growing in IHS colour space. In: Proceedings of the 6th Int. Workshop on Systems, Signals and Image Processing, 53-57, Bratislava, Slovakia 1999.
• [214] H. Palus and D. Bereska. Region-based colour image segmentation. In: Proc. of 5th Workshop "Farbbildverarbeitung", 67-74, Ilmenau, Germany 1999.
• [215] H. Palus, D. Bereska. Wierna prezentacja barwnych obrazów cyfrowych w Internecie. W: L. Kiełtyka, (ed.), Multimedia w biznesie, 353-364. Kantor Wydawniczv Zakamycze, Kraków 2003.
• [216] H. Palus and D. Bereska. On color image quantization by the k-means algorithm. In: D. Droege and D. Paulus, (eds.), 10. Workshop Farbbildverarbeitung, 58-65. Der Andere Verlag, Töenning, Germany 2004.
• [217] H. Palus and D. Bereska. Wierność odwzorowania barw w systemach wizyjnych. In: R. Gessing, T. Szkodny, (eds.), Automatyzacja procesów dyskretnych, Optymalizacja dyskretna, Robotyka i sterowniki programowalne, 285-294. WN-T, Warszawa 2004.
• [218] H. Palus and D. Bereska. Colour reproduction accuracy in vision systems. In: K. Wojciechowski, B. Smolka, H. Palus and et al., (eds.), Computer Vision and Graphics, 279-286. Springer, Dordrecht, The Netherlands 2006.
• [219] H. Palus, D. Bereska, S. Grela. Transformacja Karhunena-Loevego dla obrazów barwnych. Zeszyty Naukowe Politechniki Śląskiej, s. Automatyka, 131:185-195, Gliwice 2000.
• [220] H. Palus and M. Bogdański. Clustering techniques in colour image segmentation. In: Proceedings of AIMETH International Conference, 103-104, Gliwice 2003.
• [221] H. Palus and T. Kotyczka. Evaluation of colour image segmentation results. Arbeitsberichte des Instituts fuer Informatik Friedrich-Alexander- Universitaet Erlang en-Nuernberg. 34(15):41-46, 2001.
• [222] N. Papamarkos, A. Atsalakis and C. Strouthopoulos. Adaptive color reduction. IEEE Trans, on SMC - Part B: Cybernetics, 32(l):44-56, 2002.
• [223] H. Park and J. Ra. Homogeneous region merging approach for image segmentation preserving semantic object contours. In: Proc. International Workshop on Very Low Bitrate Video Coding, 149-152, Chicago, USA 1998.
• [224] D. Paulus, J. Hornegger and L. Csink. Linear approximation of sensitivity curve calibration. Schriftenreihe des ZBS Ilmenau, (1):3-10, 2002.
• [225] T. Pavlidis. Structural Pattern Recognition. Springer, New York, USA 1977.
• [226] S.-C. Pei and C.-M. Cheng. A novel block truncation coding of color images by using quaternion-moment-preserving principle. In: Proceedings of IEEE Int. Symp. on Circuits and Systems, vol. 2, 684-687, Atlanta, USA 1996.
• [227] E. Peli. Contrast in complex images. Journal of Optical Society of America A, 7(10):2032-2040, 1990.
• [228] F. Perez and C. Koch. Toward color image segmentation in analog VLSI: algorithm and hardware. International Journal of Computer Vision, 12(l):17-42, 1994.
• [229] B. Phong. Illumination for computer generated pictures. Comm. ACM. 18(6):311-317, 1975.
• [230] M. Pietikainen and D. Harwood. Segmentation of color images using edge-preserving filters. In: V. Cappellini and R. Marconi, (eds.), Advances in Image Processing and Pattern Recognition, 94-99. Elsevier, Amsterdam, The Netherlands 1986.
• [231] K. Plataniotis and A. Venetsanopoulos. Color Image Processing and Applications. Springer-Verlag, Berlin, Germany 2000.
• [232] T. Pomierski and H.-M. Gross. Biological neural architectures for chromatic adaptation resulting in constant color sensations. In: Proceedings of IEEE Int. Conf. on Neural Networks ICNN'96, vol. 2, 734-739, Washington, D.C., USA 1996.
• [233] W. Pratt. Spatial transform coding of color images. IEEE Transactions on Communication Technology, 19(6):980-992, 1971.
• [234] W. Pratt. Digital Image Processing. John Wiley & Sons, New York, USA 1978.
• [235] W. Pratt. Digital Image Processing, 2nd ed. John Wiley & Sons, New York, USA 1991.
• [236] L. Priese and V. Rehrmann. A fast hybrid color segmentation method. In: S. Pöppl and H. Handels, (Hrsg.), Mustererkennung 1993. 297-304. Springer-Verlag. Berlin, Germany 1993.
• [237] A. Pritchard. Object characterisation and image segmentation using a modified HSI colour space, Ph.D. Thesis. University of Reading, Reading, UK 1995.
• [238] P. Pujas and M.-J. Aldon. Robust colour image segmentation. In: Proceedings of the 7th Int. Conf. on Advanced Robotics (ICAR), vol. 1, 145-155, San Feliu de Guixola, Spain 1995.
• [239] P. Ranefall, B. Nordin and E. Bengtsson. Finding facial features using an HLS colour space. In: C. Braccini, L. DeFloriani and G. Vernazza, (eds.), Image Analysis and Processing, 191-196. Springer-Verlag, Berlin, Germany 1995.
• [240] Recommendation BT.601, Encoding parameters of digital television for studios, ITU, Geneva 1994.
• [241] P. Reitan. Hybrid approaches to color image quantization, Ph.D. Thesis. University of Maryland, Baltimore, USA 1999.
• [242] C. Ridder, O. Munkelt and H. Kirchner. Adaptive background estimation and foreground detection using Kalman-filtering. In: Proceedings of ICRAM'95, 193-195, Istanbul, Turkey.
• [243] P. Robertson. Visualizing color gamuts: A user interface for the effective use of perceptual color spaces in data displays. IEEE Comp. Graph, and Appl., 8(5):50-64, 1988.
• [244] A. Rodriguez and O. Mitchel. Image segmentation by succesive background extraction. Pattern Recognition, 24(5):409-420, 1991.
• [245] D. Rogers and R. Earnshaw. Techniques for Computer Graphics. Springer-Verlag, Berlin, Germany 1986.
• [246] G. Sandini, F. Buemi, M. Massa and M. Zucchini. Visually guided operations in greenhouses. In: Proceedings of IEEE International Workshop on Intelligent Robots and Systems, IROS'90, vol. 1, 279-285, New York, USA 1990.
• [247] S. Sangwine. Fourier transforms of colour images using quaternions, or hypercomplex numbers. Electronics Letters, 32(21):1979-1980, 1996.
• [248] S. Sangwine and R. Home. The Colour Image Processing Handbook. Chapman and Hall. London, UK 1998.
• [249] M. Sarifuddin and R. Missaoui. A new perceptually uniform color space with associated color similarity measure for content-based image and video retrieval. In: Proceedings of ACM SIGIR Workshop on Multimedia Information Retrieval, Salvador, Brazil 2005.
• [250] C. Scheering and A. Knoll. Fast colour image segmentation using pre-clustered chromaticity-plane. In: Proceedings of ICASSP-97, vol. 4, 3145-3147. Munich, Germany 1997.
• [251] R. Schettini. A segmentation algorithm for color images. Pattern Recognition Letters, 14(6):499-506, 1993.
• [252] R. Schettini. Multicolored object recognition and location. Pattern Recognition Letters, 15(11):1089-1097, 1994.
• [253] T. Schindewolf, R. Albert, W. Stolz, W. Abmayr and H. Harms. Klassifikation mela-nozytärer Hautveränderungen anhand makroskopischer Farbaufnahmen. In: S. J. Pöppl, H. Handels, (Hrsg.), Mustererkennung 1993, 436-443, Springer-Verlag, Berlin 1993.
• [254] S. Selim and M. Ismail. K-means-type algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(l):81-87, 1984.
• [255] S. Shafer. Using color to separate reflection components. Color Research and Application, 10(4):210-218, 1985.
• [256] L. Shapiro and G. Stockman. Computer Vision. Prentice Hall. Upper Saddle River. USA 2003.
• [257] G. Sharma, (ed.) Digital Color Imaging Handbook, CRC Press, Boca Raton, FL, USA 2003.
• [258] G. Sharma and H. J. Trussell, (eds.). Special issue on colour imaging. IEEE Transactions on Image Processing, 6(7):901-932, 1997.
• [259] G. Sharma, W. Wu and E. Dalai. The CIEDE2000 color-difference formula: implementation notes, supplementary test data, and mathematical observations. Color Research and Application, 30(l):21-30, 2005.
• [260] D. Sinclair. Voronoi seeded colour image segmentation. Technical Report TR.99-4, AT&T Laboratories, Cambridge, UK 1999.
• [261] W. Skarbek. Metody reprezentacji obrazów cyfrowych. Akademicka Oficyna Wydawnicza PLJ, Warszawa 1993.
• [262] W. Skarbek and A. Koschan. Colour image segmentation - a survey. Technical Report 94-32, Tech. Univ. of Berlin, Berlin, Germany, October 1994.
• [263] J. Slater. Modern Televisions Systems. Pitman, London, UK 1991.
• [264] D. Slaughter and R. Harrell. Color vision in robotic fruit harvesting. Trans, of the ASAE, 30(4):1144-1148, 1987.
• [265] P. D. Smet, R. Pires and D. D. Vleeschauwer. The activity image in image enhancement and segmentation. In: Proceedings of IEEE Benelux Signal Processing Symposium, 79-82, Leuven, Belgium 1998.
• [266] J. Smith and S. Chang. Visualseek: a fully automated content-based image query system. In: Proceedings of ACM Multimedia Conference, 87-98, Boston, USA 1996.
• [267] B. Smolka. Nonlinear Techniques of Noise Reduction in Digital Color Images. Wydawnictwo Politechniki Śląskiej, Gliwice 2004.
• [268] B. Smolka, M. Szczepański, A. Świerniak, H. Palus and K. Plataniotis. On the segmentation of the comet assay images. Journal of Medical Informatics and Technologies, 2:29-39,
• [269] J. Solinsky. The use of color in machine edge detection. In: Proceedings of Vision'85, 4-34-4-52, Detroit, USA 1985.
• [270] K. Stapor. Automatyczna klasyfikacja obiektów. Akademicka Oficyna Wydawnicza EXIT, Warszawa 2005.
• [271] N. Strachan, P. Nesvadba and A. Allen. Calibration of a video camera digitising system in the CIE L*U*V* colour space. Pattern Recognition Letters, ll(ll):771-777, 1990.
• [272] Y.-N. Sun, C.-S. Wu, X.-Z. Lin and N.-H. Chou. Color image analysis for liver tissue classification. Optical Engineering, 32(7):1609-1615, 1993.
• [273] S. Süsstrunk, R. Buckley and S. Swen. Standard RGB color spaces. In: Proceedings of 7th Color Imaging Conference: Color Science, Systems, and Applications, 127-134, Scotts Valley, USA 1999.
• [274] S. Süsstrunk and S. Winkler. Color image quality on the Internet. In: Electronic Imaging 2004, Internet Imaging V, Proceedings of SPIE, vol. 5304, 118-131, 2004.
• [275] M. Swain. Color Indexing, Ph.D. Thesis. University of Rochester, Rochester, USA 1990.
• [276] M. Swain and D. Ballard. Color indexing. International Journal of Computer Vision, 7(l):ll-32, 1991.
• [277] M. Szczepański, B. Smolka, D. Ślusarczyk, K. Plataniotis and A. Venetsanopoulos. Geodesic paths approach to color image enhancement. Electronic Notes in Theoretical Computer Science, 46, 2001.
• [278] J. Śmieja, H. Palus. Zastosowanie cech topologicznych w rozpoznawaniu obiektów. Zeszyty Naukowe Politechniki Śląskiej, s. Automatyka, 115:125-135, Gliwice 1994.
• [279] R.Tadeusiewicz. Systemy wizyjne robotów przemysłowych. WN-T, Warszawa 1992.
• [280] R. Tadeusiewicz, P. Korohoda. Komputerowa analiza i przetwarzanie obrazów. Wydawnictwo Fundacji Postępu Telekomunikacji, Kraków 1997.
• [281] R. Taylor and P. Lewis. Colour image segmentation using boundary relaxation. In: Proceedings of 11th IAPR Int. Conf. on Pattern Recognition, vol. 3, 721-724, The Hague, The Netherlands 1992.
• [282] J. M. Tenenbaum, T. D. Garvey, S. Weyl, and H. C. Wolf. An interactive facility for scene analysis research, Technical Report TN 87. SRI International, Menlo Park, California, USA 1974.
• [283] P. Thrift and C. Lee. Using highlights to constrain object size and location. IEEE Transactions on Systems, Man, and Cybernetics, 13(3):426-443, 1983.
• [284] A. Toet. Multiscale colour image enhancement. Pattern Recognition Letters, 13(3):164-174, 1992.
• [285] S. Tominaga. Color classification of natural color images. Color Research and Application, 17(4):230-239, 1992.
• [286] F. Torres, J. Angulo, and F. Ortiz. Automatic detection of specular reflectance in colour images using the MS diagram. Lecture Notes in Computer Science, 2756:132-139, 2003.
• [287] A. Tremeau and P. Colantoni. Region adjacency graph applied to color image segmentation,. IEEE Transactions on Image Processing, 9(4):735-744, 2000.
• [288] A. Tremeau, V. Lozano, and B. Lager. How to optimize the use of the L*H*C* color space in color image analysis processes. Acta Stereol., 14(2):223-228, 1995.
• [289] R. Turi and S. Ray. Clustering-based colour image segmentation. In: Proceedings of Australia Pattern Recognition Society Student Conference, 76-88, Melbourne, Australia 1996.
• [290] R. Turi and S. Ray. An application of clustering in colour image segmentation. In: Proceedings of 6th International Conference on Control, Automation, Robotics and Vision (ICARCV'OO), Singapore 2000.
• [291] T. Uchiyama and M. Arbib. Color image segmentation using competitive learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(12):1197-1206, 1994.
• [292] S. Umbaugh, R. Moss and W. Stoecker. Automatic color segmentation of image with application to detection of variegated coloring in skin tumors. IEEE Enginering in Medicine and Biology Magazine, 8(4):43-52, 1989.
• [293] R. D. Valois and K. D. Valois. A multi-stage color model. Vision Research, 33(8):1053-1065, 1993.
• [294] N. Vandenbroucke, L. Macaire and L.-G. Postaire. Color pixels classification in an hybrid color space. In: Proceedings of International Conference on Image Processing, vol. 1, 176-180, Chicago, USA 1998.
• [295] A. D. Ventura and R. Schettini. Computer-aided color coding for data display. In: Proceedings of 11th IAPR Int. Conf. on Pattern Recognition, vol. 3, 29-32, The Hague, The Netherlands 1992.
• [296] A. Watson and C. Tiana. Color motion video coded by perceptual components. SID Digest of Technical Papers, (23):314-317, 1992.
• [297] S. Westland and C. Ripamonti. Computational Colour Science Using Matlab. John Wiley & Sons, Chichester, UK 2004.
• [298] S. Winkler. Visual fidelity and perceived quality: towards comprehensive metrics. In: Human Vision and Electronic Imaging VI, Proceedings of SPIE, vol. 4299, 114-125, 2001.
• [299] L. Wixson and D. Ballard. Real-time detection of multicolored objects. In: Proceedings of SPIE, vol. 1198, 435-446, 1989.
• [300] S. Wolf, R. Ginosar and Y. Zeevi. Spatio-chromatic image enhancement based on a model of human visual information processing. Journal of Visual Communication and Image Representation, 9(l):25-37, 1998.
• [301] G. Wyszecki and W. Stiles. Color Science: Concepts and Methods, Quantitative Data and Formulae. John Wiley & Sons, New York, USA 1982.
• [302] M. Yachida and S. Tsuji. Application of colour information to visual perception. Pattern Recognition, 3(3):307-323, 1971.
• [303] D. Yagi, K. Abe and H. Nakatani. Segmentation of color aerial photographs using HSV color models. In: Proc. IAPR Workshop on Machine Vision Applications, (MVA '92), 367-370, Tokyo, Japan 1992.
• [304] K. Yamaba and Y. Miyake. Color character recognition method based on human perception. Optical Engineering, 32(l):33-40, 1993.
• [305] C. Yang and J. Rodriguez. Saturation clipping in the LHS and YIQ color spaces. In Proceedings of IS&T/SPIE Int. Symp. on Electronic Imaging, 297-307, San JOse, USA1996.
• [306] S. Yendrikhovskij, F. Blommaert and H. de Ridder. Optimizing colour reproduction of natural images. In: Proceedings of IS&T/SID 6th Color Imaging Conference, 140-145, Scottsdale, USA 1998.
• [307] J. Zabrodzki. Grafika komputerowa. Metody i narzędzia. WNT, Warszawa 1994.
• [308] E. Zarakov. Direct image sensor tackles color concerns. Photonics Spectra, 37(11):99-100, 2003.
• [309] L. Zhang, F. Lin and B. Zhang. A CBIR method based on color-spatial feature. In: Proceedings of the IEEE Region 10th Ann. Int. Conf. TENCON'99, 166-169, Cheju, Korea.
• [310] X. Zhang and B. Wandell. A spatial extension to CIELAB for digital color image reproduction. Journal of the Society for Information Display, 5(l):61-63, 1997.
• [311] Y. Zhang. A survey on evaluation methods for image segmentation. Pattern Recognition, 29(8):1335-1346, 1996.
• [312] J. Zheng, K. Valavanis and J. Gauch. Noise removal from color images. Journal of Intelligent and Robotic Systems, 7(3):257-285, 1993.
• [313] S. Zhu and A. Yuille. Region competition: unifying snakes, region growing and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(9):884-900, 1996.
• [314] K. Zieliński, M. Strzelecki. Komputerowa analiza obrazu medycznego, Wstęp do morfometrii i patologii ilościowej. PWN, Warszawa 2001.