National Laboratory of Pattern Recognition and Centre for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing 100080
Abstract The computational models for vision have the characteristics of complex and diversity, as they come from many subjects such as cognition science and information science. In this paper, the computational models for vision are investigated from the biological visual mechanism and computational vision theory systematically. Some points of view about the prospects of the computational model are presented. The development of the computational model will build the bridge for the computational vision and biological visual mechanism.
[1] Borji A, Itti L. State-of-the-Art in Visual Attention Modeling. IEEE Trans on Pattern Analysis and Machine Intelligence, 2013, 35(1): 185-207 [2] Tenenbaum J B, Griffiths T L, Kemp C. Theory-Based Bayesian Models of Inductive Learning and Reasoning. Trends in Cognitive Sciences, 2006, 10(7): 309-318 [3] Roberts L. Machine Perception of Three-Dimensional Solids. Ph. D Dissertation. Cambridge, USA: Massachusetts Institute of Technology, 1963 [4] Marr D. Vision: A Computational Investigation into the Human Re-presentation and Processing of Visual Information. San Francisco, USA: Freeman, 1982 [5] Aloimonos Y. What I Have Learned. CVGIP: Image Understanding, 1994, 60(1): 74-85 [6] Neisser U. Cognition and Reality. San Francisco, USA: Freeman, 1976 [7] Shou Tiande. Brain Mechanism of Visual Information Processing. 2nd Edition. Hefei, China: University of Science and Technology of China Press, 2010 (in Chinese) (寿天德.视觉信息处理的脑机制.第2版.合肥:中国科学技术大学出版社, 2010) [8] Wandell B A. Foundations of Vision. Sunderland, USA: Sinauer Associates, 1995 [9] Gonzalez R C, Woods R E. Digital Image Processing. 2nd Edition. Upper Saddle River, USA: Prentice Hall, 2002 [10] Huang Kaiqi, Wang Liangsheng, Tan Tieniu, et al. A Real-Time Object Detecting and Tracking System for Outdoor Night Surveillance. Pattern Recognition, 2008, 41(1): 432-444 [11] Lachman R, Butterfield E C, Lachman J. Cognitive Psychology and Information Processing: An Introduction. Hillsdale, USA: Lawrence Erlbaum Associates, 1979 [12] Land E H. The Retinex Theory of Color Vision. Scientific American, 1977, 237(6): 108-128 [13] Huang Kaiqi, Wu Zhenyang, Wang Qiao. The Application of Color Constancy to Color Image Enhancement. Journal of Applied Sciences, 2004, 22(3): 322-326 (in Chinese) (黄凯奇,吴镇扬,王 桥.色彩恒常性在彩色图像增强中的应用.应用科学学报, 2004, 22(3): 322-326) [14] Zaghloul K A, Boachen K. Optical Nerve Signals in a Neuromorphic Chip I: Outer and Inner Retina Model. IEEE Trans on Biomedical Engineering, 2004, 51(4): 657-666 [15] Hubel D H, Wiesel T N. Receptive Fields, Binocular Interaction and Functional Architecture in the Cats Visual Cortex. Journal of Physiology, 1962, 160(1): 106-154 [16] Michison G. The Organization of Sequential Memory: Sparse Representations and the Targeting Problem // von Seelen W, Leinhos U, Shaw G, eds. Organization of Neural Networks. Weinheim, Germany: VCH Verlags-Gesellschaft, 1988: 347-367 [17] Wright J, Yang A, Ganesh A, et al. Robust Face Recognition via Sparse Representation. IEEE Trans on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227 [18] Gao Shenghua, Tsang I W H, Chia L T. Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications. IEEE Trans on Pattern Analysis and Machine Intelligence, 2013, 35(1): 92-104 [19] Rodieck R W, Stone J J. Analysis of Receptive Fields of Cat Retina Ganglion Cell. Journal of Neurophysiology, 1965, 28(5): 833-849 [20] Wang Yunjiu, Qi Xianglin. The Development of Gabor Function Model in Primary Vision. Acta Biophysica Sinica, 1993, 9(3): 508-513 (in Chinese) (汪云九,齐翔林.初级视觉的Gabor函数模型的研究进展.生物物理学报, 1993, 9(3): 508-513) [21] Daugman J G. Two-Dimensional Spectral Analysis of Cortical Receptive Field Profiles. Vision Research, 1980, 20(10): 847-856 [22] Campell F W, Robson J G. Application of Fourier Analysis to the Visibility of Gratings. The Journal of Physiology, 1968, 197(3): 551-556 [23] Pattanaik S N, Ferwerda J A. A Multiscale Model of Adaptation and Spatial Vision for Realistic Image Display // Proc of the 25th Annual Conference on Computer Graphics and Interactive Techniques. Orlando, USA, 1998: 152-172 [24] Huang Kaiqi, Wang Qiao, Wu Zhenyang. Natural Color Image Enhancement Evaluation Algorithm Based on Human Visual System. Computer Vision and Image Understanding, 2006, 103(1): 52-63 [25] Lowe D G. Towards a Computational Model for Object Recognition in IT Cortex // Proc of the 1st IEEE International Workshop on Biologically Motivated Computer Vision. Seoul, Republic of Korea, 2000: 20-31 [26] Riesenhuber M, Poggio T. Hierarchical Models of Object Recognition in Cortex. Nature Neuroscience, 1999, 2(11): 1019-1025 [27] Serre T, Wolf L, Bileschi S, et al. Robust Object Recognition with Cortex-Like Mechanisms. IEEE Trans on Pattern Analysis and Machine Intelligence, 2007, 29(3): 411-426 [28] Huang Kaiqi, Tao Dacheng, Yuan Yuan, et al. Biologically Inspired Features for Scene Classification in Video Surveillance. IEEE Trans on Systems, Man and Cybernetics, 2011, 41(1): 307-313 [29] Huang Yongzhen, Huang Kaiqi, Tao Dacheng, et al. Enhanced Biologically Inspired Model for Object Recognition. IEEE Trans on Systems, Man and Cybernetics, 2011, 41(6): 1668-1678 [30] Bednar J A. Building a Mechanistic Model of the Development and Function of the Primary Visual Cortex. Journal of Physiology(Paris), 2012, 106(5): 194-211 [31] Bileschi S, Wolf L. Image Representations beyond Histograms of Gradients[EB/OL]. [2013-04-15] . http://www.cs.tau.ac.il/~wolf/papers/gestalt.pdf [32] Zhu Songchun. Embedding Gestalt Laws in Markov Random Fields[EB/OL]. [2013-04-15]. http://www.cnbc.cmu.edu/~tai/readings/texture/Gestalt_pami.pdf [33] Gibson J J. The Senses Considered as Perceptual Systems. Boston, USA: Houghton Mifflin, 1966 [34] Horn B K P, Schunck B G. Determining Optical Flow. Artificial Intelligence, 1980, 17(1/2/3): 185-203 [35] Shimshoni I, Ponce J. Probabilistic 3D Object Recognition. International Journal of Computer Vision, 2000, 36(1): 51-70 [36] Marr D.Representing Visual Information:A Computational Approach // Hanson A R, Riseman E M, eds. Computer Vision Systems. New York, USA: Academic Press, 1978: 61-80 [37] Biederman I. Recognition-by-Components: A Theory of Human Image Understanding. Psychological Review, 1987, 94(2): 115-147 [38] Li Feifei, Fergue R, Torralba A. Recognizing and Learning Object Categories [EB/OL].[2007-06-17]. http://cs.haifa.ac.il/~dkeren/recognition/categories.pdf [39] Treisman A, Gelade G. A Feature-Integration Theory of Attention. Cognitive Psychology, 1980, 12(1): 97-136 [40] Koch C, llman S. Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry. Human Neurobiology, 1985, 4(4): 219-227 [41] Itti L, Koch C, Niebur E. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis. IEEE Trans on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254-1259 [42] Long Fuhui, Zheng Nanning. A Visual Computing Model Based on Attention Mechanism. Journal of Image and Graphics, 1998, 3(7): 592-595 (in Chinese) (龙甫荟,郑南宁.一种引入注意机制的视觉计算模型.中国图象图形学报, 1998, 3(7): 592-595) [43] McClelland J L, Rumelhart D E. An Interactive Activation Model of Context Effects in Letter Perception: Part I. An Account of Basic Findings. Psychological Review, 1981, 88(5): 375-407 [44] Mcclelland J L, Rumelhard D E. Exploration in Parallel Distributed Processing : A Handbook of Models, Programs, and Exercises. Cambridge, USA: MIT Press, 1986 [45] LeCun Y, Bottou L, Bengio Y, et al. Gradient-Based Learning Applied to Document Recognition. Proc of the IEEE, 1998, 86(11): 2278-2324 [46] Quoc V L, Ranzato M A, Monga R, et al. Building High-Level Features Using Large Scale Unsupervised Learning[EB/OL]. [2013-04-15]. http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/unsupervised_icml2012_slides.pdf [47] Dean J, Corrado G S, Monga R, et al. Large Scale Distributed Deep Networks [EB/OL].[2013-04-15]. http://www.cs.toronto.edu/~ranzato/publications/DistBeliefNIPS2012_with Appendix.pdf [48] Chen L. Topological Structure in Visual Perception[EB/OL]. [2013-04-10]. http://paper.sciencenet.cn/upload/news/file/2012/11/201211131357396010.pdf [49] Huang Yongzhen, Huang Kaiqi, Tan Tieniu, et al. A Novel Visual Organization Based on Topological Perception // Proc of the 9th Asian Conference on Computer Vision. Xian, China, 2009: 180-189 [50] Millan J R. On the Need for Online Learning in Brain-Computer Interfaces // Proc of the IEEE International Joint Conference on Neural Networks. Martigny, Switzerland, 2004, IV: 2877-2882 [51] Reber P. What Is the Memory Capacity of the Human Brain[EB/OL]. [2013-04-10]. http://www.scientificamerican.com/article.cfm?id=what-is-the-memory-capacity [52] Brige R. Human Brain[EB/OL]. [2013-04-10]. http://www.sizes.com/people/brain.htm [53] Linkenkaer-Hansen K, Palva J M, Sams M, et al. Face-Selective Processing in Human Extrastriate Cortex around 120ms after Stimulus Onset Revealed by Magneto- and Electroencephalography. Neuroscience Letters, 1998, 253(3): 147-150 [54] Chikkerur S, Serre T, Tan C, et al. What and Where: A Bayesian Inference Theory of Attention. Vision Research, 2010, 50(22): 2233-2247 [55] Argyriou A, Evgeniou T, Pontil M. Multi-Task Feature Learning[EB/OL]. [2013-04-15]. http://books.nips.cc/papers/files/nips19/NIPS2006_0251.pdf [56] Hawkins J, Blakeslee S. On Intelligence. New York, USA: Times Books, 2004
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