Spatch Based Active Partitions with Linguistically Formulated Energy

• Autorzy: Tomczyk A. , Pryczek M. , Walczak S. , Jojczyk K. , Szczepaniak P. S.
• Języki publikacji: EN

Abstrakty

EN

The present paper shows the method of cognitive hierarchical active partitions that can be applied to creation of automatic image understanding systems. The approach, which stems from active contours techniques, allows one to use not only the knowledge contained in an image, but also any additional expert knowledge. Special emphasis is put on the effcient way of knowledge retrieval, which could minimise the necessity to render information expressed in a natural language into a description convenient for recognition algorithms and machine learning.

Słowa kluczowe

EN

image segmentation; image understanding; active partitions; linguistic

PL

segmantacja obrazów; rozumienie obrazów

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2010
• Tom: Vol. 18, nr 1
• Strony: 87--115
• Opis fizyczny: Bibliogr. 39 poz.

Twórcy

autor

Tomczyk A.

autor

Pryczek M.

autor

Walczak S.

autor

Jojczyk K.

autor

Szczepaniak P. S.

• Institute of Information Technology Technical University of Lodz Wólczańska 215, 90-924 Łódź, Poland,

Bibliografia

• [1] Gonzalez, R. and Woods, R., Digital Image Processing, Prentice-Hall Inc., New Jersey, 2002.
• [2] Sonka, M., Hlavec, V., and Boyle, R., Image Processing, Analysis and Machine Vision, Chapman and Hall, Cambridge, 1994.
• [3] Davies, E. R., Machine Vision, Theory, Algorithms, Practicalities, Elsevier, Morgan Kaufmann, San Francisco, 2005.
• [4] Tadeusiewicz, R. and Ogiela, M., Medical Image Understanding Technology, Vol. 156 of Studies in Fuzziness and Soft Computing, Springer-Verlag, Berlin, Heidelberg, New York, 2004.
• [5] Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active Contour Models, International Journal of Computer Vision, 1988, pp. 321-331.
• [6] Cohen, L. D., On active contour models and balloons, Computer Vision, Graphics, and Image Processing. Image Understanding, Vol. 53, No. 2, 1991, pp. 211-218.
• [7] Ivins, J. and Porrill, J., Active Region Models for Segmenting Medical Images, In: IEEE International Conference ICIP, 1994, pp. 227-231.
• [8] Xu, C. and Prince, J., Snakes, Shapes, and Gradient Vector Flow, 1998.
• [9] Cohen, L. D. and Cohen, I., Finite Element Methods for Active Contour Models and Balloons for 2D and 3D Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No. 11, November 1991, pp. 1131-1147.
• [10] Amini, A. A., Weymouth, T. E., and Jain, R. C., Using Dynamic Programming for Solving Variatioal Problems in Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 9, 1990, pp. 855-867.
• [11] McInerney, T. and Terzopoulos, D., Topologically Adaptable Snakes, In: ICCV, 1995, pp. 840-845.
• [12] Delingette, H. and Montagnat, J., New Algorithms for Controlling Active Contours Shape and Topology, In: ECCV (2), 2000, pp. 381-395.
• [13] Casseles, V., Catte, F., Coll, T., and Dibos, F., A geometric model for active contours in image processing, Numerische Mathematic, Vol. 66, 1993, pp. 1-31.
• [14] Malladi, R., Sethian, J. A., and Vemuri, B. C., Shape Modeling with Front Propagation: A Level Set Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 2, 1995, pp. 158-175.
• [15] Osher, S. and Sethian, J. A., Fronts Propagating with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations, Journal of Computational Physics, Vol. 79, 1988, pp. 12-49.
• [16] Caselles, V., Kimmel, R., and Sapiro, G., Geodesic Active Contours, International Journal of Computer Vision, Vol. 22, No. 1, 1997, pp. 61-79.
• [17] Yezzi, A., Kichenassamy, S., Kumar, A., Olver, P., and Tannenbaum, A., A Geometric Snake Model for Segmentation of Medical Imagery, IEEE Transactions on Medical Imaging, Vol. 16, No. 2, 1997.
• [18] Kichenassamy, S., Kumar, A., Olver, P. J., Tannenbaum, A., and Yezzi, A. J., Gradient Flows and Geometric Active Contour Models, In: ICCV, 1995, pp. 810-815.
• [19] Xu, C., Yezzi, A., and Prince, J., On the Relationship between Parametric and Geometric Active Contours, In: 34th Asilomar Conference on Signals, Systems and Computers, 2000, pp. 483-489.
• [20] Xu, C., Hopkins, J., Yezzi, A., and Prince, J. L., A Summary of Geometric Level-Set Analogues for a General Class of Parametric Active Contour and Surface Models, In: Proc. of 1st IEEE Workshop on Variational and Level Set Methods in Computer Vision, July 2001, pp. 104-111.
• [21] Cootes, T. and Taylor, C. J., Active shape models - 'smart snakes', In: Proceedings of 3rd British Machine Vision Conference, Springer-Verlag, 1992, pp. 266-275.
• [22] Cootes, T., Taylor, C., Cooper, D., and Graham, J., Active Shape Model - Their Training and Application, CVGIP Image Understanding, Vol. 61, No. 1, 1994, pp. 38-59.
• [23] Grzeszczuk, R. and Levin, D., Brownian Strings: Segmenting Images with Stochastically Deformable Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 10, 1997, pp. 1100-1113.
• [24] Jacob, M., Blu, T., and Unser, M., A unifying approach and interface for spline-based snakes, In: in Proc. SPIE Med. Imaging, l. 4322, 2001, pp. 340-347.
• [25] Schnabel, J. and Arridge, S., Active contour models for shape description using multiscale differential invariants, In: In Proceedings of British Machine Vision Conference, edited by D. Pycock, 1995, pp. 197-206.
• [26] Staib, L. H. and Duncan, J. S., Parametrically Deformable Contour Models, In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1989, pp. 98-103.
• [27] Denzler, J. and Niemann, H., Active rays: A new approach to contour tracking, International Journal of Computing and Information Technology, Vol. 4, 1996, pp. 9-16.
• [28] Tomczyk, A. and Szczepaniak, P. S., On the Relationship Between Active Contours and Contextual Classification, In: CORES, edited by M. Kurzyński, E. Puchała, M. Woźniak, and A. ˙ Zołnierek, Vol. 30 of Advances in Soft Computing, Springer, 2005, pp. 303-310.
• [29] Tomczyk, A., Szczepaniak, P. S., and Pryczek, M., Active Contours as Knowledge Discovery Methods, In: Discovery Science, edited by V. Corruble, M. Takeda, and E. Suzuki, Vol. 4755 of Lecture Notes in Computer Science, Springer, 2007, pp. 209-218.
• [30] Tomczyk, A. and Szczepaniak, P. S., Contribution of Active Contour Approach to Image Understanding, In: IEEE International Workshop on Imaging Systems and Techniques, 2007.
• [31] Tadeusiewicz, R., Ogiela, M. R., and Szczepaniak, P. S., Notes on a Linguistic Description as the Basis for Automatic Image Understanding, Applied Mathematics and Computer Science, Vol. 19, No. 1, 2009, pp. 143-150.
• [32] Tomczyk, A., Image Segmentation Using Adaptive Potential Active Contours, In: Computer Recognition Systems 2, edited by M. Kurzyński, E. Puchała, M. Woźniak, and A. ˙ Zołnierek, Vol. 45 of Advances in Soft Computing, Springer, 2007, pp. 148-155.
• [33] Tomczyk, A.,Wolski, C., Szczepaniak, P. S., and Rotkiewicz, A., Analysis of Changes in Heart Ventricle Shape Using Contextual Potential Active Contours, In: Computer Recognition Systems 3, edited by M. Kurzyński and M. Woźniak, Vol. 57 of Advances in Soft Computing, Springer, 2009, pp. 397-405.
• [34] Tomczyk, A. and Szczepaniak, P. S., Segmentation of Heart Image Sequences Based on Human Way of Recognition, In: Brain Informatics, edited by N. Zhong, K. Li, S. Lu, and L. Chen, Vol. 5819 of Lecture Notes in Computer Science, Springer, 2009, pp. 225-235.
• [35] Rutkowska, D., Piliński, M., and Rutkowski, L., Sieci neuronowe, algorytmy genetyczne i systemy rozmyte, Wydawnictwo Naukowe PWN, Warszawa, 1997.
• [36] Rutkowska, D., Inteligentne systemy obliczeniowe, algorytmy genetyczne i sieci neuronowe w systemach rozmytych, Akademicka Oficyna Wydawnicza PLJ, Warszawa, 1997.
• [37] Strehl, A., Relationship-based Clustering and Cluster Ensembles for Highdimensional Data Mining, Ph.D. thesis, The University of Texas at Austin, May 2002.
• [38] Rocha, J. and Pavlidis, T., A Shape Analysis Model with Applications to a Character Recognition System, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 16, No. 4, 1994, pp. 393-404.
• [39] Rocha, J. and Pavlidis, T., Character Recognition Without Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, 1995, pp. 903-909.