Psych 465, Spring 2009: High Level Vision readings
Perception
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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.
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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.
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Standing, L. (1973). Learning 10,000 pictures. Quartely journal of
experimental psychology, 25, 207-222.
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Oliva, A., & Schyns, P.G. (2000). Diagnostic colors mediate scene
recognition. Cognitive Psychology, 41,176-210.
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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.
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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.
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Oliva, A. & Torralba, A. (2006). Building the Gist of a Scene: The Role of
Global Image Features in Recognition. Progress in Brain Research.
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Thorpe, S. J., Fize, D., & Marlot, C. (1997). Speed of processing in the
human vision system. Nature, 381, 520-522.
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Kirchner, H., & Thorpe, S.J. (2006). Ultra-rapid object detection with
saccadic eye movements: visual processing speed revisited. Vision Research,
46, 1762-1776.
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Serre, T., Oliva, A., & Poggio, T. A. (2007). A feedforward architecture
accounts for rapid categorization. Proceedings of the National Academy of
Sciences.
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Simonic, T. (2003). Preference and perceived naturalness in visual
perception of naturalistic landscapes.
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Kravitz, D.J., Vinson, L.D., & Baker, C.I. (2008). How position dependent is
visual object recognition? TICS.
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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).
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D. Pelli, Crowding: a cortical constraint on object recognition, Current
Opinion in Neurobiology 18:445-451(2008).
The machinery of vision
V1
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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.
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Olshausen, BA & Field, DJ. (1996). Emergence of simple-cell receptive fields
properties by learning a sparse code for natural images. Nature, 381,
607-609.
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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
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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/
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Felsen, G., & Dan, Y. (2005). A natural approach to studying vision. Nature
Neuroscience, 8-12.
V4 and the rest
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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
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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
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Waydo, Kraskov, Quiroga, Fried, and Koch. (2006). Sparse Representation in
the Human Medial Temporal Lobe. Journal of Neuroscience, 26(40), 10232-4.
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Rolls ET, Aggelopoulos NC, Zheng F. (2003). The receptive fields of inferior
temporal cortex neurons in natural scenes. J Neuroscience, 23(1):339-48.
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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.
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Bar, M. (2004). Visual Object in Context. Nature Neuroscience Review,
617-628.
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Bar, M. and E. Aminoff (2003). Cortical analysis of visual context. Neuron,
38, 347-358.
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Bar, M. et al (2006). Top-down facilitation of visual recognition. PNAS,
103(2), 449-454.
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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.
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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).
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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).
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DiCarlo, J. J., and D. D. Cox, Untangling invariant object
recognition, Trends in Cognitive Sciences 11:333-341 (2007).
Modeling
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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.
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Yuille, A., & Kersten, D. (2006). Vision as Bayesian inference: analysis by
synthesis? TICS, 10(7).
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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.
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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.
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Ariely, D. (2001). Seeing sets: Representation by statistical
properties. Psychological Science, 12(2), 157-162.
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Chong, S.C., and Treisman, A. (2003). Representation of statistical
properties. Vision Res., 43, 393-404.
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Epstein, R.A. (2005). The cortical basis of scene processing. Visual
Cognition, 12, 954-978.
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Ullman, (2007). Object recognition and segmentation by a fragment-based
hierarchy. TICS, 11(2),
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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).
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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.
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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].
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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.
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Image interpretation by a single bottom-up top-down cycle,
Boris Epshtein, Ita Lifshitz, and Shimon Ullman, PNAS (2008).
Image statistics
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Torralba, A., Oliva, A. (2003). Statistics of Natural Images
Categories. Network: Computation in Neural Systems, 14, 391-412.
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Attewell, D., & Baddeley, R.J. (2007). The distribution of reflectances
within the visual environment. Vision Research, 47, 548-554.
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Field DJ. (1994). "What is the Goal of Sensory Coding?" Neural Computation
Vol 6: 559-601
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The role of context in object recognition, Oliva and Torralba, TiCS
(2007).
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Statistical Learning Using Real-World Scenes Extracting Categorical
Regularities Without Conscious Intent, Timothy F. Brady and Aude Oliva (2008).
Philosophy
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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.
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Shimon Edelman, Constraining the neural representation of the visual
world, Trends in Cognitive Sciences 6:125-131, 2002.
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Sloman, A. (2006). Aiming for more realistic vision systems? COSY-TR 0603,
University of Birmingham, School of Computer Science.
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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