Nonlinear shape statistics via kernel spaces

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

Volumn 2191, Issue , 2001, Pages 269-276

  Cremers, Daniel ^a   Kohlberger, Timo ^a   Schnörr, Christoph ^a  

^a UNIVERSITY OF MANNHEIM   (Germany)

Author keywords

Kernel PCA; Mercer kernels; Nonlinear density estimation; Nonlinear shape statistics; Shape learning; Variational methods

Indexed keywords

OBJECT RECOGNITION;

DENSITY ESTIMATION; KERNEL PCA; MERCER KERNEL; SHAPE LEARNING; SHAPE STATISTICS; VARIATIONAL METHODS;

VECTOR SPACES;

EID: 84945269261     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45404-7_36     Document Type: Conference Paper

References (13)

1. B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, Proc. of the 5th Annual ACM Workshop on Computational Learning Theory, pages 144–152, Pittsburgh, PA, 1992. ACM Press.
2. B. Chalmond and S. C. Girard. Nonlinear modeling of scattered multivariate data and its application to shape change. IEEE Trans. Patt. Anal. Mach. Intell., 21(5):422–432, 1999.
3. T.F. Cootes and C.J. Taylor. A mixture model for representing shape variation. Image and Vis. Comp., 17(8):567–574, 1999.
4. T.F. Cootes, C.J. Taylor, D.M. Cooper, and J. Graham. Active shape models – their training and application. Comp. Vision Image Underst., 61(1):38–59, 1995.
5. V. Cortes, C. and Vapnik. Support vector networks. Machine Learning, 20:273– 297, 1995.
6. D. Cremers, C. Schnörr, and J. Weickert. Diffusion–snakes: Combining statistical shape knowledge and image information in a variational framework. In IEEE Workshop on Variational and Level Set Methods, Vancouver, Canada, Jul. 13, 2001. To appear.
7. D. Cremers, C. Schnörr, J. Weickert, and C. Schellewald. Diffusion–snakes using statistical shape knowledge. In G. Sommer and Y.Y. Zeevi, editors, Algebraic Frames for the Perception-Action Cycle, volume 1888 of Lect. Not. Comp. Sci., pages 164–174, Kiel, Germany, Sept. 10–11, 2000. Springer.
8. T. Heap and D. Hogg. Automated pivot location for the cartesian-polar hybrid point distribution model. In Brit. Machine Vision Conference, pages 97–106, Edinburgh, UK, Sept. 1996.
9. C. Kervrann and F. Heitz. A hierarchical markov modeling approach for the segmentation and tracking of deformable shapes. Graphical Models and Image Processing, 60:173–195, 5 1998.
10. M.E. Leventon, W.E.L. Grimson, and O. Faugeras. Statistical shape influence in geodesic active contours. In Proc. Conf. Computer Vis. and Pattern Recog., volume 1, pages 316–323, Hilton Head Island, South Carolina, June 13–15, 2000.
11. B. Moghaddam and A. Pentland. Probabilistic visual learning for object representation. IEEE Trans. Patt. Anal. Mach. Intell., 19(7):696–710, 1997.
12. B. Schölkopf, S. Mika, Smola A., G. Rätsch, and Müller K.-R. Kernel PCA pattern reconstruction via approximate pre-images. In L. Niklasson, M. Boden, and T. Ziemke, editors, Internat. Conf. on Art. Neural Networks ICANN, pages 147– 152, Berlin, Germany, 1998. Springer.
13. B. Schölkopf, A. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10:1299–1319, 1998.