Unsupervised segmentation incorporating colour, texture, and motion

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2756, Issue , 2003, Pages 353-360

  Brox, Thomas ^a   Rousson, Mikaël ^b   Deriche, Rachid ^b   Weickert, Joachim ^a  

^a SAARLAND UNIVERSITY   (Germany)

^b INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE; COMPUTERS;

LEVEL SET APPROACH; MULTIPLE OBJECTS; NONLINEAR DIFFUSION; OBJECT SEGMENTATION; SEGMENTATION PROCESS; STATISTICAL MODELING; UNSUPERVISED SEGMENTATION; VARIATIONAL FRAMEWORK;

IMAGE SEGMENTATION;

EID: 35248892929     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-45179-2_44     Document Type: Article

References (23)

1. F. Andreu, C. Ballester, V. Caselles, and J. M. Mazón. Minimizing total variation flow. Differential and Integral Equations, 14(3):321-360, Mar. 2001.
2. J. Bigün, G. H. Granlund, and J. Wiklund. Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(8):775-790, Aug. 1991.
3. T. Brox, M. Rousson, R. Deriche, and J. Weickert. Unsupervised segmentation incorporating colour, texture, and motion. Technical Report 4760, INRIA Sophia-Antipolis, France, 2003.
4. T. Brox and J. Weickert. Nonlinear matrix diffusion for optic flow estimation. In L. Van Gool, editor, Pattern Recognition, volume 2449 of Lecture Notes in Computer Science, pages 446-453. Springer, Berlin, 2002.
5. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B, 39:1-38, 1977.
6. W. Förstner and E. Gülch. A fast operator for detection and precise location of distinct points, corners and centres of circular features. In Proc. ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, pages 281-305, Interlaken, Switzerland, June 1987.
7. G. Gerig, O. Kübler, R. Kikinis, and F. A. Jolesz. Nonlinear anisotropic filtering of MRI data. IEEE Transactions on Medical Imaging, 11:221-232, 1992.
8. S. Keeling and R. Stollberger. Nonlinear anisotropic diffusion filters for wide range edge sharpening. Inverse Problems, 18:175-190, Jan. 2002.
9. J. Kim, J. Fisher, A. Yezzi, M. Cetin, and A. Willsky. Nonparametric methods for image segmentation using information theory and curve evolution. In IEEE International Conference on Image Processing, Rochester, NY, Sept. 2002.
10. J. Malik, S. Belongie, T. Leung, and J. Shi. Contour and texture analysis for image segmentation. International Journal of Computer Vision, 43(1):7-27, 2001.
11. H.-H. Nagel and A. Gehrke. Spatiotemporally adaptive estimation and segmentation of OF-fields. In H. Burkhardt and B. Neumann, editors, Computer Vision - ECCV '9S, volume 1407 of Lecture Notes in Computer Science, pages 86-102. Springer, Berlin, 1998.
12. S. Osher and J. A. Sethian. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, 79:12-49, 1988.
13. N. Paragios and R. Deriche. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(3), Mar. 2000.
14. N. Paragios and R. Deriche. Geodesic active regions: A new paradigm to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation, pages 249-268, March/June 2002.
15. P. Perona and J. Malik. Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:629-639, 1990.
16. C. Peterson and B. Söderberg. A new method for mapping optimization problems onto neural networks. International Journal of Neural Systems, 1(1):3-22, 1989.
17. J. Puzicha, T. Hofmann, and J. Buhmann. Deterministic annealing: fast physical heuristics for real-time optimization of large systems. In Proc. 15th IMACS World Conference on Scientific Computation, Modelling and Applied Mathematics, Berlin, 1997.
18. M. Rousson, T. Brox, and R. Deriche. Active unsupervised texture segmentation on a diffusion based feature space. In Proc. 2003 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Madison, WI, June 2003. IEEE Computer Society Press. To appear.
19. M. Rousson and R. Deriche. A variational framework for active and adaptive segmentation of vector valued images. In Proc. IEEE Workshop on Motion and Video Computing, Orlando, Florida, Dec. 2002.
20. L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992.
21. J. Weickert and B. Benhamouda. A semidiscrete nonlinear scale-space theory and its relation to the Perona-Malik paradox. In F. Solina, W. G. Kropatsch, R. Klette, and R. Bajcsy, editors, Advances in Computer Vision, pages 1-10. Springer, Wien, 1997.
22. J. Weickert and T. Brox. Diffusion and regularization of vector- and matrix-valued images. In M. Z. Nashed and O. Scherzer, editors, Inverse Problems, Image Analysis, and Medical Imaging, volume 313 of Contemporary Mathematics. AMS, Providence, 2002.
23. H. Zhao, T. Chan, B. Merriman, and S. Osher. A variational level set approach to multiphase motion. Journal of Computational Physics, 127:179-195, 1996.