DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
UNIVERSITY OF CALIFORNIA, SAN DIEGO

CSE 291: Seminar on Vision & Learning

List of Suggested Papers


Most of these papers can be located on the web, e.g. through CiteSeer, Google, or INSPEC (UC only). If the links provided below don't work, please try one of these three options before giving up. (Or, you can get some fresh air and walk to the actual library...) Participants in the seminar should feel free to propose worthwhile papers that don't appear here.

Jianbo Shi; Malik, J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Aug. 2000, vol.22, (no.8):888-905.

Meila, M. and Shi, J. Learning Segmentation with Random Walks. Advances in Neural Information Processing Systems 13 (NIPS 2000).

Meila, M. and Shi, J. A Random Walks View of Spectral Segmentation. AI and STATISTICS 2001(AISTATS) 2001.

Christoph Bregler, Learning and Recognizing Human Dynamics in Video Sequences IEEE Conf. on Computer Vision and Pattern Recognition (1997)

Schoelkopf, B.; Smola, A.; Mueller, K.-R. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 1 July 1998, vol.10, (no.5):1299-319.

Weiss, Y. Smoothness in layers: Motion segmentation using nonparametric mixture estimation. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 17-19 June 1997.

Christopher K. I. Williams On a Connection between Kernel PCA and Metric Multidimensional Scaling Advances in Neural Information Processing Systems 13 eds. T. K. Leen, T. G. Diettrich, V. Tresp. MIT Press (2001)

Bookstein, F.L. Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, June 1989, vol.11, (no.6):567-85.

Girosi, F.; Jones, M.; Poggio, T. Regularization theory and neural networks architectures. Neural Computation, March 1995, vol.7, (no.2):219-69.

Poggio, T., Torre, V., Koch, C. Computational vision and regularization theory. Nature, vol.317, (no.6035), 26 Sept. 1985. p.314-19.

Scott, G.L.; Longuet-Higgins, H.C. Feature grouping by 'relocalisation' of eigenvectors of the proximity matrix. IN: BMVC90 Proceedings of the British Machine Vision Conference. (BMVC90 Proceedings of the British Machine Vision Conference, Oxford, UK, 24-27 Sept. 1990). Oxford, UK: BMVC 90, 1990. p. 103-8.

G. Scott and H. Longuet-Higgins. An Algorithm for Associating the Features of Two Images. In Proc. Royal Society London, volume B244, pages 21-26, 1991.

Pilu, M. A direct method for stereo correspondence based on singular value decomposition. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 17-19 June 1997.

Shapiro, L.S.; Brady, J.M. Feature-based correspondence: an eigenvector approach. Image and Vision Computing, June 1992, vol.10, (no.5):283-8.

Umeyama, S. An eigendecomposition approach to weighted graph matching problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, Sept. 1988, vol.10, (no.5):695-703.

S. Sclaroff and A. Pentland Modal Matching for Correspondence and Recognition IEEE Trans. Pattern Analysis and Machine Intelligence 17(6), June 1995.

Pietro Perona and William Freeman A factorization approach to grouping Proc. 5th European Conference of Computer Vision (ECCV98). Freiburg, Germany. 1998. Pages 655-670.

Haili Chui; Rangarajan, A. A new algorithm for non-rigid point matching, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000, Hilton Head Island, SC, USA, 13-15 June 2000.

Dellaert, F.; Seitz, S.M.; Thorpe, C.E.; Thrun, S. Structure from motion without correspondence. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000, Hilton Head Island, SC, USA, 13-15 June 2000.

F. Dellaert, S. Seitz, C. Thorpe, and S. Thrun, Feature Correspondence: A Markov Chain Monte Carlo Approach, Advances in Neural Information Processing Systems 13 (NIPS 2000).

Z.W. Tu, S. C. Zhu, "Image Segmentation by Data Driven Markov Chain Monte Carlo", Submitted to PAMI. A short version appeared in ICCV 2001. (co-authored with H. Shum)

Girosi, F. An equivalence between sparse approximation and support vector machines. Neural Computation, 15 Aug. 1998, vol.10, (no.6):1455-80.

Puzicha, J.; Buhmann, J.M.; Rubner, Y.; Tomasi, C. Empirical evaluation of dissimilarity measures for color and texture. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 20-27 Sept. 1999.

Puzicha, J.; Hofmann, T.; Buhmann, J.M. Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 17-19 June 1997.

Paul Viola and Mike Jones, Robust Real Time Object Detection, Second International Workshop on Statistical and Computational Theories of Vision Vancouver, Canada, July 13, 2001.

Amit, Y.; Geman, D.; Wilder, K. Joint induction of shape features and tree classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, Nov. 1997, vol.19, (no.11):1300-5.

Amit, Y.; Geman, D. Shape quantization and recognition with randomized trees. Neural Computation, 1 Oct. 1997, vol.9, (no.7):1545-88.

S. Soatto, G. Doretto, Y.Wu, Dynamic Textures. Intl. Conf. on Computer Vision, pages 439-446, July 2001.

Cootes, T.F.; Edwards, G.J.; Taylor, C.J. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, June 2001, vol.23, (no.6):681-5.

Efros, A.A.; Leung, T.K. Texture synthesis by non-parametric sampling. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 20-27 Sept. 1999.

Alexei A. Efros and William T. Freeman ``Image Quilting for Texture Synthesis and Transfer'' To be published in SIGGRAPH 2001.

D. Comaniciu, P. Meer: Mean shift analysis and applications. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 20-27 Sept. 1999.

Wong, Y.-F.I. Nonlinear scale-space filtering and multiresolution system. IEEE Transactions on Image Processing, June 1995, vol.4, (no.6):774-87.

Y. Wong : "Clustering Data by Melting," Neural Computation, vol. 5, pp. 89--104, 1993.

Roweis, S.T.; Saul, L.K. Nonlinear dimensionality reduction by locally linear embedding. Science, 22 Dec. 2000, vol.290, (no.5500):2323-6.

Tenenbaum, J.B.; de Silva, V.; Langford, J.C. A global geometric framework for nonlinear dimensionality reduction. Science, 22 Dec. 2000, vol.290, (no.5500):2319-23.

Weiss, Y. Segmentation using eigenvectors: a unifying view. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 20-27 Sept. 1999.

Michael Isard and Andrew Blake CONDENSATION -- conditional density propagation for visual tracking Int. J. Computer Vision, 29, 1, 5--28, (1998)

S. Mika, B. Schoelkopf, A.J. Smola, K.-R. Mueller, M. Scholz, and G. Raetsch. Kernel PCA and de-noising in feature spaces. In M.S. Kearns, S.A. Solla, and D.A. Cohn, editors, Advances in Neural Information Processing Systems 11, pages 536-542. MIT Press, 1999.

B. Schoelkopf, S. Mika, C.J.C. Burges, P. Knirsch, K.-R. Mueller, G. Raetsch, and A.J. Smola. Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5):1000-1017, September 1999.

Lee, D.D.; Seung, H.S. Learning the parts of objects by non-negative matrix factorization. Nature, vol.401, (no.6755), Macmillan Magazines, 21 Oct. 1999. p.788-91.

Anthony J. Bell and Terrence J. Sejnowski, The "Independent Components" of Natural Scenes are Edge Filters, Vision Research, Volume 37, Issue 23, December 1997, Pages 3327-3338.

J.H. van Hateren, D.L. Ruderman, Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex. Proc.R.Soc.Lond. B, 265:2315-2320 (1998).

J.H. van Hateren, A. van der Schaaf, Independent component filters of natural images compared with simple cells in primary visual cortex. Proc.R.Soc.Lond. B 265:359-366 (1998).

A. Hyvärinen and P. O. Hoyer. A Two-Layer Sparse Coding Model Learns Simple and Complex Cell Receptive Fields and Topography from Natural Images. Vision Research, 41(18):2413-2423, 2001 (in press).

T-W. Lee, T. Wachtler and T.J. Sejnowski. Color Opponency Constitutes A Sparse Representation For the Chromatic Structure of Natural Scenes, Advances in Neural Information Processing Systems 13, 2001, MIT Press, Cambridge MA.

Lucas, B. D. and Kanade, T. An iterative image registration technique with an application to stereo vision, Proceedings of the 7th International Joint Conference on Artificial Intelligence, Vancouver, pp. 674--679, 1981.

B. K. P. Horn and B. G. Schunck. Determining optical flow. Artificial Intelligence, 17:185-203, 1981. (click here for chapter on optical flow from Horn's book)

Yoav Freund, Robert E. Schapire Experiments with a New Boosting Algorithm Proc. 13th International Conference on Machine Learning, 1996.

Robert E. Schapire, Yoram Singer Improved Boosting Algorithms Using Confidence-rated Predictions COLT: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann Publishers, 1999.

H.C. Longuet-Higgins. A computer algorithm for reconstructing a scene from two projections. Nature, 293:133--135, 1981.

George Tzanetakis, Georg Essl, Perry Cook. Automatic Musical Genre Classification of Audio Signals, In. Proc. Int. Symposium on Music Information Retrieval (ISMIR), Bloomington, Indiana, 2001


Most recently updated on Sept. 26, 2001 by Serge Belongie.