List of Suggested Papers 1. Adelson & Bergen, The Plenoptic Function and the Elements of Early Vision http://web.mit.edu/persci/people/adelson/pub_pdfs/elements91.pdf 2.Cavanagh, P. (1996). Vision is getting easier every day. Perception, 24, 1227-1232. http://visionlab.harvard.edu/Members/Patrick/PDF.files/2002%20pdfs/easy27.pdf 3.Cavanagh, P. (1991). What's up in top-down processing? In A. Gorea (ed.) Representations of Vision: Trends and Tacit Assumptions in Vision Research, Cambridge, UK: Cambridge University Press, 295-304. http://visionlab.harvard.edu/Members/Patrick/PDF.files/2002%20pdfs/what.pdf 4. Cavanagh, P. (1999). Pictorial art and vision. In Robert A. Wilson and Frank C. Keil (Eds.), MIT Encyclopedia of Cognitive Science, (pp. 648-651) Cambridge, MA: MIT Press. http://www.visionlab.harvard.edu/members/Patrick/PDF.files/artMITECS.pdf Part I: Low-level Vision (images as texture) 5. Olshausen & field, mergence of simple-cell receptive field properties by learning a sparse code for natural images, (1996) Nature, 381: 607-609. http://www.ai.mit.edu/courses/6.899/papers/sparse-coding.pdf (code available: http://redwood.berkeley.edu/bruno/sparsenet/) 6. Y. Rubner and C. Tomasi and L. J. Guibas. The Earth Mover's Distance as a Metric for Image Retrieval. International Journal of Computer Vision, 40(2) November 2000, pages 99--121. http://vision.stanford.edu/public/publication/rubner/rubnerTr98.pdf (code available: http://www.ofai.at/~elias.pampalk/ma/emd.zip) 7. Y. Rubner,J. Puzicha, C. Tomasi, and J. M. Buhmann. Empirical Evaluation of Dissimilarity Measures for Color and Texture. Computer Vision and Image Understanding Journal, 84(1):25-43, October 2001. http://www.cs.duke.edu/~tomasi/papers/rubner/rubnerCviu01.pdf 8. Martin, Fowlkes, Malik, Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5):530-549, May 2004. http://www.cs.bc.edu/~dmartin/papers/tpami2004.pdf http://www.cs.bc.edu/~dmartin/papers/nips02.pdf (short version) (code and data available: http://www.cs.berkeley.edu/projects/vision/grouping/segbench) 9. Renninger, L.W. & Malik, J. (2004). When is scene recognition just texture recognition? Vision Research, 44, 2301-2311 http://www.ski.org/Verghese_Lab/laura/pubs/manuscripts/scenes.pdf Data available: http://www.ski.org/Verghese_Lab/laura/pubs/scenes.Zip 10. Csurka et al http://www.cs.huji.ac.il/~daphna/cbvis-papers/Csurka.pdf 11. J. Winn, A. Criminisi and T. Minka. Object Categorization by Learned Universal Visual Dictionary Proc. IEEE Intl. Conf. on Computer Vision (ICCV), Beijing 2005. 12. A. Torralba and A. Oliva. (2003) Statistics of Natural Image Categories Network: Computation in Neural Systems. Vol. 14, 391-412. http://web.mit.edu/torralba/www/ne3302.pdf 13. A. Torralba, A. Oliva. Depth estimation from image structure (2002) IEEE Transactions on Pattern Analysis and Machine Intelligence. 24(9): 1226-1238. September. http://cvcl.mit.edu/Papers/Torralba-Oliva02.pdf 14. A. Oliva, A. Torralba (2001). Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision, Vol. 42(3): 145-175. http://cvcl.mit.edu/Papers/IJCV01-Oliva-Torralba.pdf Part II: Mid-level Vision (Image Segmentation) 15. Max Wertheimer, Laws of Organization in Perceptual Forms (1923) http://psy.ed.asu.edu/~classics/Wertheimer/Forms/forms.htm 16. 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. http://www.cs.berkeley.edu/~malik/papers/SM-ncut.pdf (code available: http://www.cis.upenn.edu/~jshi/software/) 17. Meila, M. and Shi, J. Learning Segmentation with Random Walks. Advances in Neural Information Processing Systems 13 (NIPS 2000). 18. Weiss, Y. Segmentation using eigenvectors: a unifying view. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, 20-27 Sept. 1999. 19. Andrew Y. Ng, Michael I. Jordan, Yair Weiss, On Spectral Clustering: Analysis and an algorithm (2001) NIPS http://citeseer.ist.psu.edu/rd/50624373%2C541173%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/26676/http:zSzzSzwww-2.cs.cmu.eduzSzGroupszSzNIPSzSzNIPS2001zSzpaperszSzpsgzzSzAA35.pdf/ng01spectral.pdf 20. Xiaofeng Ren and Jitendra Malik, Learning a Classification Model for Segmentation. in ICCV '03 (superpixel code: http://www.cs.sfu.ca/~mori/research/superpixels/) 21. Tu & Zhu, Image Segmentation by Data-Driven Markov Chain Monte Carlo, PAMI (2002) http://www.cnbc.cmu.edu/cns/papers/DDMCMC.pdf 22. D. Comaniciu, P. Meer: Mean Shift: A Robust Approach toward Feature Space Analysis, IEEE Trans. Pattern Analysis Machine Intell., Vol. 24, No. 5, 603-619, 2002 http://www.caip.rutgers.edu/~comanici/Papers/MsRobustApproach.pdf 23. Boykov & Jolly, Interactive Graph. Cuts. for Optimal Boundary & Region Segmentation of. Objects in ND Images. ICCV 01 http://www.cse.ucsd.edu/classes/fa04/cse252c/tedeschi.pdf 24. Yin Li; Jian Sun; Chi-Keung Tang; Heung-Yeung Shum, Lazy Snapping, SIGGRAPH 04 http://www.research.microsoft.com/asia/dload_files/group/vc/2004/LazySnapping_SIGGRAPH04.pdf Part III: 2D Recognition Schniderman & Kanade Viola & Jones Vidal-Naquet, Ullman (2003) Torralba, Sharing Features * Segmentation + Recognition Ulman's horses xren's horses Schiele * Correspondences Distance Transform, Schanfer dist Shape Context, Geometric Blur Berg & Marius ?? * Dealing with data Isomap LLE Style vs. Content AAM (Cootes & Taylor) + Blinz & Vetter NMF * Intrinsic images Adelson, Pentland, theatre workshop Sinha, Adelson: World of painted polyhedra Finlayson, ECCV 04 Weiss intrinsic Freeman intrinsic Hoiem, Efros, Hebert Andrew Ng (3D) * Tracking Toyama & Blake Particle Filtering ? Condensation (Isard & Blake) Larry Zitnik's superpixels Ramanan, Strike a Pose Torr, ICCV '05 * image + words Kobus Barnard Berg & Berg (Names and Faces) * Object Category Discovery LSA, pLSA, LDA FeiFei (scenes) Josef Fergus (google)