The 2006 edition of this course offers an advanced survey of selected topics of current interest in the neural and computational modeling of mammalian vision. This year's topics include perceptual consequences of eye movements, visual search, object recognition, and perceptual learning. Some classes will be held at laboratories of nearby institutions. Students are expected to have a sufficient interdisciplinary grounding in the fundamentals of computational modeling of mammalian vision to read primary research sources extensively. A term project that combines a problem statement, literature review, and either (1) simulation of a model or (2) a design for a psychophysical experiment is required.
Answers to FREQUENTLY-ASKED QUESTIONS about CN 730
Information for GUEST SPEAKERS
Dates of DELIVERABLES for student research reports
Discussion board (internal)
Click
on a date to go directly to a summary of that week's class, including assigned
readings. Links to guest speakers' home pages, weekly topics, and a list of
readings will also be found there, though these will be updated in real time
in the course of the semester.
Jan 19 Jeremy Wolfe
Jan 26 Michele Rucci
Feb 2 Heiko Neumann
Feb 9 Student presentations
Feb 16 Antonio Torralba
Feb 23 Rhea Eskew
Mar 2 Erik Blaser
Mar 9 Spring break
Mar 16 Rob Fergus and Bill Freeman
Mar 23 Aaron Seitz
Mar 30 Arash Yazdanbakhsh -- field trip
Apr 6 Patrick Cavanagh -- field trip
Apr 13 Helen Barbas
Apr 20 Adam Reeves
Apr 27 Student presentations
Readings
Wolfe JM. Moving towards solutions to some enduring controversies in visual search. Trends Cogn Sci. 2003 Feb;7(2):70-76. pdf
Wolfe JM, Horowitz TS. What attributes guide the deployment of visual attention and how do they do it? Nat Rev Neurosci. 2004 Jun;5(6):495-501. pdf
Background
Martinez-Conde S, Macknik SL, and Hubel DH (2004) The role of fixational eye
movements in visual perception. Nature Rev Neurosci 5:229-240. pdf
Steinman RM, Haddad GM, Skavenski AA, and Wyman D (1973) Miniature eye movement. Science 181:810-819. pdf
M. Rucci and A. Casile (2005), Fixational instability and natural image statistics: Implications for early visual representation, Network: Computation in Neural Systems, 16, 2/3, 121-138, 2005. pdf
M. Rucci and G. Desbordes (2003), Contributions of fixational eye movements to the discrimination of briefly presented stimuli, Journal of Vision 3(11), 852-864. link
Atick JJ and Redlich A (1992) What does the retina know about natural scenes? Neural Comp 4:449-572.
Olveczky B, Baccus S, and Meister M (2003) Segregation of object and background motion in the retina. Nature 423:401-408.
A. Casile and M. Rucci (2006), A theoretical analysis of the influence of fixational instability on the development of thalamocortical connectivity, Neural Computation, 18, 3.
M. Rucci and A. Casile (2004) “Decorrelation of neural activity during fixational eye movements: Possible implications for the refinement of V1 receptive fields”, Visual Neuroscience,21, 725-738.
M. Rucci, G.M. Edelman and J. Wray (2000) “Modeling LGN responses during free-viewing: A possible role of microscopic eye movements in the refinement of cortical orientation selectivity”, Journal of Neuroscience, 20, 12, 4708-4720. pdf
Snodderly DM, Kagan I, and Gur M (2001) Selective activation of visual cortex neurons by fixational eye movements: Implications for neural coding. Vis Neurosci 18:259-277. pdf
Background
Pack CC, Born RT (2001) Temporal dynamics of a neural solution to the
aperture problem in cortical area MT. Nature 409, 1040-1042
Hupe JM, James AC, Girard P, Lomber SG, Payne BR, Bullier J (2001)
Feedback connections act on the early part of the responses in monkey
visual cortex. Journal of Neurophysiology 85, 134-145
Core
Bayerl P, Neumann H (2004) Disambiguating visual motion through
contextual feedback modulation. Neural Computation 16, 2041-2066 pdf
Hansen T, Neumann H (2004) Neural mechanism for the robust representation
of junctions. Neural Computation 16, 1013-1037 pdf
J. M. Hupe´ , A. C. James, B. R. Payne, P. Girard & J. Bullier.
Cortical feedback improves discrimination between figure and background
by V1, V2 andV3 neurons NATURE |VOL 394 | 20 AUGUST 1998. pdf
Supplementary
Neumann H, Sepp W (1999) Recurrent V1-V2 interaction in early visual
boundary processing. Biological Cybernetics 81, 425-444
Rajzada R, Grossberg S (2001) Context-sensitive binding by the laminar circuits
of V1 and V2: A unified model of perceptual grouping, attention, and orientation
contrast. Visual Cognition 8, 431-466
Grossberg S, Mingolla E, Viswanathan L (2001) Neural dynamics of motion
integration and segmentation within and across apertures. Vision Research 41,
2521-2553
Background
Li Fei-Fei, Rob Fergus, Antonio Torralba. Recognizing and Learning Object Categories. ICCV 2005 short courses.
Slides and code available at http://people.csail.mit.edu/torralba/iccv2005/
Core readings
1) A. Torralba, K. P. Murphy, W. T. Freeman and M. A. Rubin (2003). Context-based vision system for place and object recognition, IEEE Intl. Conference on Computer Vision (ICCV), Nice, France, October.
ftp://publications.ai.mit.edu/ai-publications/2003/AIM-2003-005.pdf
2) A. Torralba (2003). Contextual priming for object detection. International Journal of Computer Vision. Vol. 53(2), 169-191.
http://people.csail.mit.edu/torralba/IJCVobj.pdf
3) A. Torralba, K. P. Murphy and W. T. Freeman. (2004). Sharing features: efficient boosting procedures for multiclass object detection. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). pp 762- 769.
http://web.mit.edu/torralba/www/cvpr2004.pdf
For students that need annotated image databases, you can find a resource here:
http://people.csail.mit.edu/brussell/research/LabelMe/intro.html
4) B. Russell, A. Torralba, K. Murphy and W. T. Freeman. LabelMe: a database and web-based tool for image annotation. AI-Memo http://people.csail.mit.edu/torralba/publications/LabelMe.pdf
Supplementary articles
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
K. Murphy, A. Torralba, D. Eaton, W. T. Freeman. Object detection and localization using local and global features. Sicily workshop on object recognition, 2005. Lecture Notes in Computer Science http://people.csail.mit.edu/torralba/publications/localAndGlobal.pdf
A. Torralba, K. P. Murphy and W. T. Freeman (2004). Contextual Models for Object Detection using Boosted Random Fields. Neural Information Processing Systems (NIPS) ftp://publications.ai.mit.edu/ai-publications/2004/AIM-2004-013.pdf
E. Sudderth, A. Torralba, W. T. Freeman, and A. Wilsky. (2005). Describing Visual Scenes using Transformed Dirichlet Processes . NIPS 2005. http://ssg.mit.edu/~esuddert/papers/nips05.pdf
E. Sudderth, A. Torralba, W. T. Freeman, and A. Wilsky. (2005). Learning Hierarchical Models of Scenes, Objects, and Parts. ICCV 2005. http://ssg.mit.edu/~esuddert/papers/iccv05.pdf
Derek Hoiem, Alexei A. Efros, Martial Hebert. (2005). Geometric Context from a Single Image. ICCV 2005
L. Fei-Fei and P. Perona. A Bayesian hierarchical model for learning natural scene categories. In CVPR, volume 2, pages 524–531, 2005.
K. Barnard et al. Matching words and pictures. JMLR, 3:1107–1135, 2003.
P. Viola and M. J. Jones. Robust real–time face detection. IJCV, 57(2):137–154, 2004.
J. Sivic, B. C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman. Discovering objects and their location in images. ICCV, 2005.
Background
Wandell, B.A. (1995). Foundations of vision. Sunderland, Mass.: Sinauer Associates. Chapters 4 and 9
Kaiser, P.K., & Boynton, R.M. (1996). Human color vision, 2nd Ed. (Washington, D.C.: Optical Society of America. Chapter 7
Core
Eskew, R.T., Jr., McLellan, J.S., & Giulianini, F. (1999). Chromatic detection and discrimination. In: K. Gegenfurtner, & L.T. Sharpe (Eds.), Color vision: from genes to perception (pp. 345-368). Cambridge: Cambridge University Press. pdf
Krauskopf, J. (1999). Higher order color mechanisms. In: K.R. Gegenfurtner, & L.T. Sharpe (Eds.), Color vision: From genes to perception (Cambridge: Cambridge University Press. pdf
Supplementary
Eskew, R.T., Jr., Newton, J.R., & Giulianini, F. (2001). Chromatic detection and discrimination analyzed by a Bayesian classifier. Vision Research, 41, 893-909. pdf
Newton, J.R., & Eskew, R.T., Jr. (2003). Chromatic detection and discrimination in the periphery: a post-receptoral loss of color sensitivity. Visual Neuroscience, 20, 511-521. pdf
Deep background
Knoblauch, K. (1995). Dual bases in dichromatic color space. In: B. Drum (Ed.) Colour vision deficiences Xii. Dordrecht: Kluwer Academic.
Krantz, D.H. (1975). Color measurement and color theory: I. Representation theorem for Grassman structures. Journal of Mathematical Psychology, 12, 283-303.
Krantz, D.H. (1975). Color measurement and color theory: II. Opponent-colors theory. Journal of Mathematical Psychology, 12, 304-327.
Part I: Object-based attention
Blaser E, Pylyshyn ZW, Holcombe AO. Tracking an object through feature space. Nature. 2000 Nov 9;408(6809):196-9. pdf
O'Craven KM, Downing PE, Kanwisher N. fMRI evidence for objects as the units of attentional selection. Nature. 1999 Oct 7;401(6753):584-7. pdf
Sperling G, Melchner MJ. The attention operating characteristic: examples from visual search. Science. 1978 Oct 20;202(4365):315-8. pdf
Braun J. Intimate attention. Nature. 2000 Nov 9;408(6809):154-5. pdf
Part II: The hidden scale of natural forms: a new cue to depth?
Blaser, E. VSS 2006 abstract. pdf
Simoncelli EP, Olshausen BA. Natural image statistics and neural representation. Annu Rev Neurosci. 2001;24:1193-216. pdf
J. Sivic, B. Russell, A. A. Efros, A. Zisserman, W. T. Freeman, Discovering Objects and their Location in Images International Conference on Computer Vision (ICCV), Beijing, China, Oct. 2005. http://people.csail.mit.edu/billf/papers/iccv05SivicEtAl.pdf
Learning Object Categories from Google's Image Search
Fergus, R. , Fei-Fei L. , Perona, P. and Zisserman, A.
Proc. of the 10th Inter. Conf. on Computer Vision, ICCV 2005.
http://people.csail.mit.edu/fergus/papers/fergus_google.pdf
A Visual Category Filter for Google Images
Fergus, R. , Perona, P. and Zisserman, A.
Proc. of the 8th European Conf. on Computer Vision, ECCV 2004.
http://people.csail.mit.edu/fergus/papers/Fergus_ECCV4.pdf
Seitz, Yamagishi, Werner, Goda, Kawato, Watanabe (2005). "Task specific
disruption of perceptual learning" PNAS, Oct 3; 10.1073/pnas.0505765102 pdf
Seitz, Lefebvre, Watanabe, Jolicoeur (2005). "The requirement of high-level
processing in subliminal learning" Current Biology, Sept 20;18(15):R753-5 pdf
Seitz and Watanabe (2003). "Is subliminal learning really passive?" Nature,
Mar 6 (Vol 422(6927): 36). pdf
Seitz and Watanabe (2005). "A unified model for perceptual learning" Trends
in Cognitive Science, Jul (Vol 9(7) 329-334). pdf
Livingstone MS, Pack CC, Born RT. Two-dimensional substructure of MT receptive fields. Neuron. 2001 Jun;30(3):781-93. pdf
Pack CC, Livingstone MS, Duffy KR, Born RT. End-stopping and the aperture problem: two-dimensional motion signals in macaque V1. Neuron. 2003 Aug 14;39(4):671-80. pdf
Pack CC, Born RT, Livingstone MS. Two-dimensional substructure of stereo and motion interactions in macaque visual cortex.Neuron. 2003 Feb 6;37(3):525-35. pdf
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Please direct questions to: ennio @ cns.bu.edu