Abstract
Brain imaging studies suggest that expert object recognition is a distinct visual skill, implemented by a dedicated anatomic pathway. Like all visual pathways, the expert recognition pathway begins with the early visual system (retina, LGN/SC, striate cortex). It is defined, however, by subsequent diffuse activation in the lateral occipital cortex (LOC), and sharp foci of activation in the fusiform gyrus and right inferior frontal gyrus. This pathway recognizes familiar objects from familiar viewpoints under familiar illumination. Significantly, it identifies objects at both the categorical and instance (subcategorical) levels, and these processes cannot be disassociated. This paper presents a four-stage functional model of the expert object recognition pathway, where each stage models one area of anatomic activation. It implements this model in an end-to-end computer vision system, and tests it on real images to provide feedback for the cognitive science and computer vision communities.
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References
I. Biederman, “Recognition-by-Components: A Theory of Human Image Understanding,” Psychological Review, vol. 94, pp. 115–147, 1987.
V. Bruce and A. Young, In the Eye of the Beholder: The Science of Face Perception. New York: Oxford University Press, 1998.
H. H. Bülthoff and S. Edelman, “Psychophysical Support for a 2-D View Interpolation Theory of Object Recognition,” Proceedings of the National Academy of Science, vol. 89, pp. 60–64, 1992.
H. H. Bülthoff, C. Wallraven, and A. Graf, “View-based Dynamic Object Recognition based on Human Perception,” International Conference on Pattern Recognition, Quebec City, 2002.
L. L. Chao, A. Martin, and J. V. Haxby, “Are face-responsive regions selective only for faces?,” NeuroReport, vol. 10, pp. 2945–2950, 1999.
V. P. Clark, K. Keil, J. M. Maisog, S. Courtney, L. G. Ungeleider, and J. V. Haxby, “Functional Magnetic Resonance Imaging of Human Visual Cortex during Face Matching: A Comparison with Positron Emission Tomography, ” NeuroImage, vol. 4, pp. 1–15, 1996.
B. J. Frey, A. Colmenarez, and T. S. Huang, “Mixtures of Local Linear Subspaces for Face Recognition, ” IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, CA, 1998.
I. Gauthier and N. K. Logothetis, “Is Face Recognition Not So Unique After All?,” Cognitive Neuropsychology, vol. 17, pp. 125–142, 2000.
I. Gauthier, M. J. Tarr, A. W. Anderson, P. Skudlarski, and J. C. Gore, “Behavioral and Neural Changes Following Expertise Training,” Meeting of the Psychonomic Society, Philadelphia, 1997.
J. V. Haxby, L. G. Ungerleider, V. P. Clark, J. L. Schouten, E. A. Hoffman, and A. Martin, “The Effect of Face Inversion on Activity in Human Neural Systems for Face and Object Recognition,” Neuron, vol. 22, pp. 189–199, 199.
A. Ishai, L. G. Ungerleider, A. Martin, J. L. Schouten, and J. V. Haxby, “Distributed representation of objects in the human ventral visual pathway,” Science, vol. 96, pp. 9379–9384, 1999.
L. Itti, “Modeling Primate Visual Attention,” in Computational Neuroscience: A Comprehensive Approach, J. Feng, Ed. Boca Raton, FL: CRC Press, 2002.
N. Kambhatla and T. K. Leen, “Dimension Reduction by Local PCA,” Neural Computation, vol. 9, pp. 1493–1516, 1997.
N. Kanwisher, M. Chun, J. McDermott, and P. Ledden, “Functional Imaging of Human Visual Recognition,” Cognitive Brain Research, vol. 5, pp. 55–67, 1996.
S. M. Kosslyn, Image and Brain. Cambridge, MA: MIT Press, 1994.
S. M. Kosslyn, A. Pascual-Leone, O. Felician, S. Camposano, J. P. Keenan, W. L. Thompson, G. Ganis, K. E. Sukel, and N. M. Alpert, “The Role of Area 17 in Visual Imagery: Convergent Evidence from PET and rTMS,” Science, vol. 284, pp. 167–170, 1999.
Z. Kourtzi and N. Kanwisher, “Cortical Regions Involved in Perceiving Object Shape,” The Journal of Neuroscience, vol. 20, pp. 3310–3318, 2000.
T. S. Lee, D. Mumford, R. Romero, and V. A. F. Lamme, “The role of the primary visual cortex in higher level vision,” Vision Research, vol. 38, pp. 2429–2454, 1998.
D. G. Lowe, Perceptual Organization And Visual Recognition. Boston: Kluwer, 1985.
E. Maguire, C. D. Frith, and L. Cipolotti, “Distinct Neural Systems for the Encoding and Recognition of Topography and Faces,” NeuroImage, vol. 13, pp. 743–750, 2001.
A. Maki, P. Nordlund, and J.-O. Eklundh, “Attentional Scene Segmentation: Integrating Depth and Motion from Phase,” Computer Vision and Image Understanding, vol. 78, pp. 351–373, 2000.
D. Marr, Vision. Cambridge, MA: Freeman, 1982.
K. Nakamura, R. Kawashima, N. Sata, A. Nakamura, M. Sugiura, T. Kato, K. Hatano, K. Ito, H. Fukuda, T. Schormann, and K. Zilles, “Functional delineation of the human occipito-temporal areas related to face and scene processing: a PET study,” Brain, vol. 123, pp. 1903–1912, 2000.
A. Oliva and P. G. Schyns, “Coarse Blobs or Fine Edges? Evidence That Information Diagnoticity Changes the Perception of Complex Visual Stimuli,” Cognitive Psychology, vol. 34, pp. 72–107, 1997.
A. J. O’Toole, K. A. Deffenbacher, D. Valentin, and H. Abdi, “Structural Aspects of Face Recognition and the Other Race Effect,” Memory and Cognition, vol. 22, pp. 208–224, 1994.
S. E. Palmer, Vision Science: Photons to Phenomenology. Cambridge, MA: MIT Press, 1999.
D. Parkhurst, K. Law, and E. Neibur, “Modeling the role of salience in the allocation of overt visual attention,” Vision Research, vol. 42, pp. 107–123, 2002.
D. A. Pollen, J. P. Gaska, and L. D. Jacobson, “Physiological Constraints on Models of Visual Cortical Function,” in Models of Brain Functions, M. Rodney and J. Cotterill, Eds. New York: Cambridge University Press, 1989, pp. 115–135.
A. Puce, T. Allison, J. C. Gore, and G. McCarthy, “Face-sensitive regions in human extrastriate cortex studied by functional MRI,” Journal of Neurophysiology, vol. 74, pp. 1192–1199, 1995.
J. W. Tanaka and T. Curran, “A Neural Basis for Expert Object Recognition,” Psychological Science, vol. 12, pp. 43–47, 2001.
M. J. Tarr and I. Gauthier, “FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise,” Neuroscience, vol. 3, pp. 764–769, 2000.
M. E. Tipping and C. M. Bishop, “Mixtures of Probabilistic Principal Component Analysers,” Neural Computation, vol. 11, pp. 443–482, 1999.
F. Tong, K. Nakayama, M. Moscovitch, O. Weinrib, and N. Kanwisher, “Response Properties of the Human Fusiform Face Area,” Cognitive Neuropsychology, vol. 17, pp. 257–279, 2000.
L. G. Ungeleider and M. Mishkin, “Two cortical visual systems,” in Analysis of visual behavior, D. J. Ingle, M. A. Goodale, and R. J. W. Mansfield, Eds. Cambridge, MA: MIT Press, 1982, pp. 549–586.
K. Zipser, V. A. F. Lamme, and P. H. Schiller, “Contextual Modulation in Primary Visual Cortex,” Neuroscience, vol. 16, pp. 7376–7389, 1996.
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Draper, B.A., Baek, K., Boody, J. (2003). Implementing the Expert Object Recognition Pathway. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds) Computer Vision Systems. ICVS 2003. Lecture Notes in Computer Science, vol 2626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36592-3_1
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DOI: https://doi.org/10.1007/3-540-36592-3_1
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