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) Scene Models 9. 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 10. 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 11. 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 12. 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 "Bag of Words" Models 13. G. Csurka, C. Bray, C. Dance, and L. Fan. Visual categorization with bags of keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV, pages 1-22, 2004. http://www.cs.huji.ac.il/~daphna/cbvis-papers/Csurka.pdf 14. J. Winn, A. Criminisi and T. Minka. Object Categorization by Learned Universal Visual Dictionary Proc. IEEE Intl. Conf. on Computer Vision (ICCV), Beijing 2005. 15. Ullman, S., Vidal-Naquet, M. , and Sali, E. (2002) Visual features of intermediate complexity and their use in classification. Nature Neuroscience, 5(7), 1-6 http://www.wisdom.weizmann.ac.il/~shimon/papers/NNeuroscience5_02.pdf 16. Michel Vidal-Naquet, Shimon Ullman: Object Recognition with Informative Features and Linear Classification. ICCV 2003 http://www.cs.huji.ac.il/~daphna/cbvis-papers/vidal.pdf 17. Fei-Fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, volume 2, pages 524-531, June 2005. http://www.vision.caltech.edu/feifeili/Fei-Fei_CVPR2005.pdf (code available: http://people.csail.mit.edu/fergus/iccv2005/bagwords.html) 18. Josef Sivic, Bryan Russell, Alexei A. Efros, Andrew Zisserman, Bill Freeman, Discovering Objects and thier Location in Images, ICCV 2005 http://people.csail.mit.edu/brussell/research/SREZF05.pdf (code available: http://people.csail.mit.edu/fergus/iccv2005/bagwords.html) 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 Window Scanning 25. H. Schneiderman and T. Kanade "Object Detection Using the Statistics of Parts" International Journal of Computer Vision, 2004 http://scholar.google.com/url?sa=U&q=http://www.springerlink.com/index/M801592435802212.pdf (demo available: http://demo.pittpatt.com/) 26. Viola, Jones, Robust Real-time Object Detection http://research.microsoft.com/~viola/Pubs/Detect/violaJones_IJCV.pdf http://research.microsoft.com/~viola/Pubs/Detect/violaJones_CVPR2001.pdf (shorter) 27. Dalal, Triggs, Histograms of Oriented Gradients for Human Detection, CVPR 2005 http://lear.inrialpes.fr/pubs/2005/DT05/hog_cvpr2005.pdf (code might be available) Correspondence Matching 28. Gavrila & Philomin, "Real-time Object Detection for Smart Vehicles", 1999 http://www.cs.berkeley.edu/~malik/cs294/gavrila99.pdf 29. Olson & Huttenlocher, Automatic Target Recognition by Matching Oriented Edge Pixels, 1997 http://www.cs.berkeley.edu/~malik/cs294/olsonhuttenlocher97.pdf 30. Belongie, Malik, Puzicha, Shape Matching and Object Recognition Using Shape Contexts (2002) http://www.cs.berkeley.edu/~malik/papers/BMP-shape.pdf 31. A Berg, T Berg, J Malik, Shape Matching and Object Recognition using Low Distortion Correspondences, CVPR 2005 http://www.cnbc.cmu.edu/cns/papers/berg-cvpr05.pdf 32. M. Leordeanu and M. Hebert, A Spectral Technique for Correspondence Problems using Pairwise Constraints, ICCV 2005 http://www.ri.cmu.edu/pub_files/pub4/leordeanu_marius_2005_1/leordeanu_marius_2005_1.pdf 33. David G. Lowe, Object Recognition from Local Scale-Invariant Features, ICCV 1999 http://www.cs.ubc.ca/spider/lowe/papers/iccv99.pdf 34. Fitzgibbon, A. W. and Zisserman, A. On Affine Invariant Clustering and Automatic Cast Listing in Movies, ECCV 2002 http://www.robots.ox.ac.uk/~vgg/publications/papers/fitzgibbon02.pdf 35. TF Cootes, GJ Edwards, CJ Taylor, Active Appearance Models, PAMI 2001 http://www.cs.ucsb.edu/~cs281b/winter2002/papers/ActiveAppearance.pdf Recognition with Segmentation 36. Eran Borenstein, Shimon Ullman: Class-Specific, Top-Down Segmentation. ECCV (2) 2002 http://courses.ece.uiuc.edu/ece598/ffl/papers_EE598/BorensteinUllman2002.pdf 37. Eran Borenstein, Shimon Ullman: Learning to Segment. ECCV (3) 2004 http://courses.ece.uiuc.edu/ece598/ffl/papers_EE598/BorensteinUllman2001.pdf 38. E. Borenstein, E. Sharon, S. Ullman, Combining Top-Down and Bottom-Up Segmentation, Proceedings IEEE workshop on Perceptual Organization in Computer Vision, IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, June 2004. http://www.dam.brown.edu/people/eitans/publications/BorensteinSharonUllman-TDBUseg.pdf 39. Xiaofeng Ren, Charless Fowlkes and Jitendra Malik, in NIPS '05, http://www.cs.berkeley.edu/~xren/publication/xren_nips05_grouping.pdf 40. Stella X. Yu and Jianbo Shi, Object-Specific Figure-Ground Segregation, CVPR 2003 http://cs.bc.edu/~syu/doc/yus_object.pdf 41. B Leibe, E Seemann, B Schiele, Pedestrian Detection in Crowded Scenes, CVPR 2005 http://www.wisdom.weizmann.ac.il/~boiman/reading/categorization_segmentation/leibe-crowdedscenes-cvpr05.pdf 42. J. Winn and N. Jojic, LOCUS: Learning Object Classes with Unsupervised Segmentation, To appear Proc. IEEE Intl. Conf. on Computer Vision (ICCV), Beijing 2005. http://courses.ece.uiuc.edu/ece598/ffl/papers_EE598/WinnJojic2005.pdf 43. Image Parsing: Unifying Segmentation, Detection, and Recognition Z Tu, X Chen, AL Yuille, SC Zhu - International Journal of Computer Vision, 2005 http://scholar.google.com/url?sa=U&q=http://www.springerlink.com/index/G1886T6163340314.pdf Words and Pictures 44. Tamara L. Berg, Alexander C. Berg, Jaety Edwards, Michael Maire, Ryan White, Yee Whye Teh, Erik Learned-Miller, David A. Forsyth, Names and Faces http://www.cs.berkeley.edu/%7Eaberg/papers/journal_berg.pdf 45. Pinar Duygulu, Kobus Barnard, Nando de Freitas, and David Forsyth " Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary," ECCV 2002. http://kobus.ca/research/publications/ECCV-02-1/ECCV-02-1.pdf 46. Kobus Barnard, Pinar Duygulu, Nando de Freitas, David Forsyth, David Blei, and Michael I. Jordan, "Matching Words and Pictures," Journal of Machine Learning Research, 2003. http://kobus.ca/research/publications/JMLR-03/JMLR-03.pdf Part IV: Intrinsic Images 47. HG Barrow, JM Tenenbaum, Recovering Intrinsic Scene Characteristics from Images, 1978 (classic paper!) (barrow78.pdf) 48. Adelson & Pentland, The Perception of Shading and Reflectance, 1996 http://web.mit.edu/persci/people/adelson/pub_pdfs/shading96.pdf 49. Sinha & Adelson: Recovering Reflectance in a World of Painted Polyhedra, ICCV 1993 50. Yair Weiss, Deriving intrinsic images from image sequences, ICCV 2001 http://www.cs.huji.ac.il/~yweiss/iccv01.pdf (code available: http://www.cs.huji.ac.il/~yweiss/intrinsic.tar) 51. GD Finlayson, MS Drew, C Lu, Intrinsic Images by Entropy Minimization, ECCV 04 http://www2.cmp.uea.ac.uk/~pm/cp_web/intrinsicfromentropy.pdf 52. Marshall F Tappen, William T Freeman, Edward H Adelson, Recovering Intrinsic Images from a Single Image (2002) http://citeseer.ist.psu.edu/cache/papers/cs/27823/http:zSzzSzwww.ai.mit.eduzSz~mtappenzSznips02_final.pdf/tappen02recovering.pdf 53. Hoiem, Efros, Hebert, Geometric Context from a Single Image, ICCV 2005 http://www.cs.cmu.edu/~dhoiem/projects/context/index.html 54. Ashutosh Saxena, Sung Chung, and Andrew Y. Ng. Learning Depth from Single Monocular Images, NIPS 2005. http://books.nips.cc/papers/files/nips18/NIPS2005_0684.pdf 55. Tenenbaum, & Freeman, Separating Style and Content with Bilinear Models (1997) http://web.mit.edu/cocosci/Papers/NC120601.pdf Part V: Dealing with Data 56. A global geometric framework for nonlinear dimensionality reduction J. B. Tenenbaum, V. De Silva, J. C. Langford Science 290 (5500): 22 December 2000. http://www.sciencemag.org/cgi/content/full/290/5500/2319 (code available: http://isomap.stanford.edu/) 57. Nonlinear dimensionality reduction by locally linear embedding. Sam Roweis & Lawrence Saul. Science v.290 no.5500, Dec.22, 2000. http://www.sciencemag.org/cgi/content/full/290/5500/2323 (code available: http://www.cs.toronto.edu/~roweis/lle/) 58. Learning the parts of objects by non-negative matrix factorization, D. D. Lee and H. S. Seung, Nature 401, 788-791 (1999). http://hebb.mit.edu/people/seung/papers/ls-lponm-99.pdf (code available) Part VI: Tracking & Motion Segmentation 59. Isard & Blake, CONDENSATION conditional density propagation for visual tracking (1998) http://www.cs.ucsb.edu/~290stat/Papers/Condensation.pdf 60. Toyama & Blake, Probabilistic Tracking with Exemplars in a Metric Space, 2002 http://research.microsoft.com/vision/cambridge/papers/toyama_ijcv02.pdf 61. Consistent segmentation for optical flow estimation C. L. Zitnick, N. Jojic, S. B. Kang IEEE Int'l Conf. on Computer Vision, 2005. http://research.microsoft.com/users/larryz/ZitnickICCV05.pdf 62. Ramanan, Forsyth, Zisserman. Strike a Pose: Tracking People by Finding Stylized Poses, CVPR 2005 http://ttic.uchicago.edu/~ramanan/papers/pose/ 63. MP Kumar, PHS Torr, A Zisserman, Learning Layered Motion Segmentations of Video, ICCV '05 http://www.robots.ox.ac.uk/~vgg/publications/papers/kumar05b.pdf