Psych 465, Spring 2009: High Level Vision — readings

Perception

  1. Biederman, I., Rabinowitz, J.CV., Glass, A.L., & Stacy, E.W. (1974). On the information extracted from a glance at a scene. Journal of experimental psychology, 103, 597-600.
  2. Brady, T.F., Konkle, T., Alvarez, G.A., & Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. Proceedings of the National Academy of Sciences, USA, vol 105 (38), 14325-14329.
  3. Standing, L. (1973). Learning 10,000 pictures. Quartely journal of experimental psychology, 25, 207-222.
  4. Oliva, A., & Schyns, P.G. (2000). Diagnostic colors mediate scene recognition. Cognitive Psychology, 41,176-210.
  5. Rousselet, G.A, Joubert, O.R., Fabre-Thorpe, M. (2005). How long to get to the "gist" of real world natural scenes? Visual Cognition, 12, 852-877.
  6. Luo, J., Boutell, M., & Brown, C. (2006). Pictures are not taken in a vacuum: An overview of exploiting context for scene content understanding. IEEE Signal Processing Magazine, March 2006, pp 101-114.
  7. Oliva, A. & Torralba, A. (2006). Building the Gist of a Scene: The Role of Global Image Features in Recognition. Progress in Brain Research.
  8. Thorpe, S. J., Fize, D., & Marlot, C. (1997). Speed of processing in the human vision system. Nature, 381, 520-522.
  9. Kirchner, H., & Thorpe, S.J. (2006). Ultra-rapid object detection with saccadic eye movements: visual processing speed revisited. Vision Research, 46, 1762-1776.
  10. Serre, T., Oliva, A., & Poggio, T. A. (2007). A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences.
  11. Simonic, T. (2003). Preference and perceived naturalness in visual perception of naturalistic landscapes.
  12. Kravitz, D.J., Vinson, L.D., & Baker, C.I. (2008). How position dependent is visual object recognition? TICS.
  13. Fiona N. Newell, Dianne M. Sheppard, Shimon Edelman, and Kimron L. Shapiro, The interaction of shape- and location-based priming in object categorisation: Evidence for a hybrid "what+where" representation stage, Vision Research 45:2065-2080 (2005).
  14. D. Pelli, Crowding: a cortical constraint on object recognition, Current Opinion in Neurobiology 18:445-451(2008).

The machinery of vision

V1

  1. Field DJ. (1993). "Scale-invariance and Self-similar 'Wavelet' Transforms: an Analysis of Natural Scenes and Mammalian Visual Systems." In: "Wavelets, Fractals and Fourier Transforms: New Developments and New Applications." Oxford University Press.
  2. Olshausen, BA & Field, DJ. (1996). Emergence of simple-cell receptive fields properties by learning a sparse code for natural images. Nature, 381, 607-609.
  3. Olshausen, B.A, & Field, D.J. (2005). What is the other 85% of V1 doing? In Problems in Systems Neuroscience. T.J. Sejnowski, L. van Hemmen, eds. Oxford University Press.

V2

  1. Willmore, B., Prenger, R. J., & Gallant, J. L. (2005). Principles of neural shape coding in area V2 [Abstract]. Journal of Vision, 5(8):82, 82a, http://journalofvision.org/5/8/82/
  2. Felsen, G., & Dan, Y. (2005). A natural approach to studying vision. Nature Neuroscience, 8-12.

V4 and the rest

  1. JL Gallant, CE Connor, S Rakshit, JW Lewis, Journal of Neurophysiology, 1996 Neural responses to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey
  2. Charles E Connor, Scott L Brincata and Anitha Pasupathy. Transformation of shape information in the ventral pathway Current Opinion in Neurobiology. Volume 17, Issue 2, April 2007, Pages 140-147
  3. Waydo, Kraskov, Quiroga, Fried, and Koch. (2006). Sparse Representation in the Human Medial Temporal Lobe. Journal of Neuroscience, 26(40), 10232-4.
  4. Rolls ET, Aggelopoulos NC, Zheng F. (2003). The receptive fields of inferior temporal cortex neurons in natural scenes. J Neuroscience, 23(1):339-48.
  5. Aggelopoulos, N.C. et al (2004). Object Perception in Natural Scenes: encoding by inferior temporal cortex simultaneously recorded neurons. Journal of Neurophysiology, 93,1342-1357.
  6. Bar, M. (2004). Visual Object in Context. Nature Neuroscience Review, 617-628.
  7. Bar, M. and E. Aminoff (2003). Cortical analysis of visual context. Neuron, 38, 347-358.
  8. Bar, M. et al (2006). Top-down facilitation of visual recognition. PNAS, 103(2), 449-454.
  9. S. Edelman, K. Grill-Spector, T. Kushnir, and R. Malach, Towards direct visualization of the internal shape representation space by fMRI, Psychobiology (special issue on Cognitive Neuroscience of Object Representation and Recognition), 26, 309-321, 1998.
  10. K. Tsunoda, Y. Yamane, M. Nishizaki, and M. Tanifuji, Complex objects are represented in macaque inferotemporal cortex by the combination of feature columns, Nature Neuroscience 4:832-838 (2001).
  11. J. S. Bowers, On the Biological Plausibility of Grandmother Cells: Implications for Neural Network Theories in Psychology and Neuroscience, Psychological Review 116:220-251 (2009).
  12. DiCarlo, J. J., and D. D. Cox, Untangling invariant object recognition, Trends in Cognitive Sciences 11:333-341 (2007).

Modeling

  1. Charles Cadieu, Minjoon Kouh, Anitha Pasupathy, Charles E. Connor, Maximilian Riesenhuber and Tomaso Poggio A Model of V4 Shape Selectivity and Invariance J Neurophysiol 98: 1733-1750, 2007.
  2. Yuille, A., & Kersten, D. (2006). Vision as Bayesian inference: analysis by synthesis? TICS, 10(7).
  3. Fei Fei, L., & Perona, P. (2005). A bayesian hierarchical model for learning natural scene categories. IEEE Proceedings in Computer Vision and Pattern Recognition, 2, 524-531.
  4. Oliva, A. & Torralba, A. (2001). Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope. International Journal of Computer Vision, 42(3), 145-175.
  5. Ariely, D. (2001). Seeing sets: Representation by statistical properties. Psychological Science, 12(2), 157-162.
  6. Chong, S.C., and Treisman, A. (2003). Representation of statistical properties. Vision Res., 43, 393-404.
  7. Epstein, R.A. (2005). The cortical basis of scene processing. Visual Cognition, 12, 954-978.
  8. Ullman, (2007). Object recognition and segmentation by a fragment-based hierarchy. TICS, 11(2),
  9. Duvdevani-Bar, S., and S. Edelman, Visual recognition and categorization on the basis of similarities to multiple class prototypes, Intl. J. of Computer Vision, 33:201-228 (1999).
  10. Shimon Edelman and Nathan Intrator, Visual Processing of Object Structure, in The Handbook of Brain Theory and Neural Networks (2nd ed.), M. A. Arbib, ed., MIT Press, 2002.
  11. Shimon Edelman and Nathan Intrator, Towards structural systematicity in distributed, statically bound visual representations, Cognitive Science, 27:73-110 (2003) [see abstract, and also our response to John Hummel's comments on this article, both published in Cognitive Science].
  12. Torralba, A., Murphy, K. P., Freeman, W. T., and Rubin, M. A. (2003). Context-based vision system for place and object recognition. In Proc. IEEE Intl. Conference on Computer Vision (ICCV), pages 273-281, Nice, France.
  13. Image interpretation by a single bottom-up top-down cycle, Boris Epshtein, Ita Lifshitz, and Shimon Ullman, PNAS (2008).

Image statistics

  1. Torralba, A., Oliva, A. (2003). Statistics of Natural Images Categories. Network: Computation in Neural Systems, 14, 391-412.
  2. Attewell, D., & Baddeley, R.J. (2007). The distribution of reflectances within the visual environment. Vision Research, 47, 548-554.
  3. Field DJ. (1994). "What is the Goal of Sensory Coding?" Neural Computation Vol 6: 559-601
  4. The role of context in object recognition, Oliva and Torralba, TiCS (2007).
  5. Statistical Learning Using Real-World Scenes Extracting Categorical Regularities Without Conscious Intent, Timothy F. Brady and Aude Oliva (2008).

Philosophy

  1. Reitman, W., Nado, R., and Wilcox, B. (1978). Machine perception: what makes it so hard for computers to see? In Savage, C. W., editor, Percep- tion and cognition: issues in the foundations of psychology, volume IX of Minnesota studies in the philosophy of science, pages 65-87. University of Minnesota Press, Minneapolis, MN.
  2. Shimon Edelman, Constraining the neural representation of the visual world, Trends in Cognitive Sciences 6:125-131, 2002.
  3. Sloman, A. (2006). Aiming for more realistic vision systems? COSY-TR 0603, University of Birmingham, School of Computer Science.
  4. Shimon Edelman, On what it means to see, and what we can do about it, in Object Categorization: Computer and Human Vision Perspectives, S. Dickinson, A. Leonardis, B. Schiele, and M. J. Tarr, eds. (Cambridge University Press, 2009, in press).
Shimon Edelman <se37 at cornell.edu>
Last modified on Tue Feb 3 11:55:29 2009