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| Object Recognition Models Based on Primate Visual Cortices: A Review |
| YAO Xing-Zhong1,2, LU Tong-Wei1,3, HU Han-Ping1 |
1.State Education Commission Key Laboratory for Image Processing and Intelligent Control, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074
2.Laboratory of Precision-Guided Technology, Institute for Military Theory, Second Artillery Command College, Wuhan 430012
3.School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan 430074 |
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Abstract In the past decade, a large number of experimental data have been accumulated in the primate visual cortices. Many visual system models have been developed based on the experimental data of the primates, and they have been used for computer object recognitions. In this paper, the main experimental conclusions on the primate visual system are summarized. Then, the frequently used models based on the mechanism of the primate visual system are reviewed. The MIT cortex hierarchical model is mainly discussed.
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Received: 18 September 2008
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[1] Riesenhuber M, Poggio T. Hierarchical Models of Object Recognition in Cortex. Nature Neuroscience, 1999, 2: 1019-1025
[2] Mutch J, Lowe D G. Multiclass Object Recognition with Sparse, Localized Features // Proc of the Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA, 2006: 11-18
[3] Fukushima K. Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position. Biological Cybernetics, 1980, 36(4): 193-202
[4]Hubel D H, Wiesel T N. Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex. The Journal of Physiology, 1962, 160(1): 106-154
[5] Fukushima K, Wake N. Handwritten Alphanumeric Character Recognition by the Neocognitron. IEEE Trans on Neural Networks, 1991, 2(3): 355-364
[6] Tsunoda K, Yamane Y, Nishizaki M, et al. Complex Objects Are Represented in Macaque Inferotemporal Cortex by the Combination of Feature Columns. Nature Neuroscience, 2001, 4(8): 832-838
[7] Hubel D H, Wiesel T N. Functional Architecture of Macaque Monkey Visual Cortex(Ferrier Lecture). Proc of the Royal of Society of London B: Biological Sciences, 1977, 198: 1-59
[8] Rolls E T, Milward T. A Model of Invariant Object Recognition in the Visual System: Learning Rules, Activation Functions, Lateral Inhibition, and Information-Based Performance Measures. Neural Computation, 2000, 12(1): 2547-2572
[9]Wallis G, Rolls E. A Model of Invariant Object Recognition in the Visual System. Progress in Neurobiology, 1997, 51(1): 167-194
[10] Mel B W. SEEMORE: Combining Color, Shape and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition. Neural Computation, 1997, 9(4): 777-804
[11]Serre T, Oliva A, Poggio T. A Feedforward Architecture Accounts for Rapid Categorization. Proc of the National Academy of Sciences of the United States of America, 2007, 104(15): 6424-6429
[12]Schneider R, Riesenhuber M. A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition [EB/OL]. [2002-08-01]. http://dspace.mit.edu/bitstream/handle/1721.1/7178/AIM-2002-01.pdf?sequence=2
[13]Hochstein S, Ahissar M. View from the Top: Hierarchies and Reverse Hierarchies in the Visual System. Neuron, 2002, 36(5): 791-804
[14]Yao Xingzhong. Study on Mechanism of Primate Visual Object Recognition and Its Application to Object Recognition. Ph. D Dissertation. Wuhan, China: Huazhong University of Science and Technology. Institute for Pattern Recognition and Artificial Intelligence, 2008 (in Chinese) |
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2009, Vol. 22 
