A neuromorphic approach to computer vision

Neuroscience is beginning to inspire a new generation of seeing machines.

[1]  Geoffrey E. Hinton Learning multiple layers of representation , 2007, Trends in Cognitive Sciences.

[2]  S. Grossberg Towards a unified theory of neocortex: laminar cortical circuits for vision and cognition. , 2007, Progress in brain research.

[3]  Denise C. Park,et al.  Nature versus Nurture in Ventral Visual Cortex: A Functional Magnetic Resonance Imaging Study of Twins , 2007, The Journal of Neuroscience.

[4]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[5]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  A. Borst Seeing smells: imaging olfactory learning in bees , 1999, Nature Neuroscience.

[7]  Y. Amit,et al.  An integrated network for invariant visual detection and recognition , 2003, Vision Research.

[8]  Eero P. Simoncelli,et al.  Spatiotemporal Elements of Macaque V1 Receptive Fields , 2005, Neuron.

[9]  R. Desimone,et al.  Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4 , 1999, The Journal of Neuroscience.

[10]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[11]  S. Hochstein,et al.  View from the Top Hierarchies and Reverse Hierarchies in the Visual System , 2002, Neuron.

[12]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[13]  S. Gerber,et al.  Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex , 2008 .

[14]  Thomas Serre,et al.  An integrated model of visual attention using shape-based features , 2009 .

[15]  Thomas Serre,et al.  A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex , 2005 .

[16]  Thomas Dean,et al.  A Computational Model of the Cerebral Cortex , 2005, AAAI.

[17]  Yoshua Bengio,et al.  Scaling learning algorithms towards AI , 2007 .

[18]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[19]  Peter Földiák,et al.  Learning Invariance from Transformation Sequences , 1991, Neural Comput..

[20]  Shimon Ullman,et al.  Image interpretation by a single bottom-up top-down cycle , 2008, Proceedings of the National Academy of Sciences.

[21]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[22]  A. Yuille,et al.  Opinion TRENDS in Cognitive Sciences Vol.10 No.7 July 2006 Special Issue: Probabilistic models of cognition Vision as Bayesian inference: analysis by synthesis? , 2022 .

[23]  N. Logothetis,et al.  Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.

[24]  T. Poggio,et al.  What and where: A Bayesian inference theory of attention , 2010, Vision Research.

[25]  D. George,et al.  A hierarchical Bayesian model of invariant pattern recognition in the visual cortex , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[26]  D. Wilkin,et al.  Neuron , 2001, Brain Research.

[27]  Michel Vidal-Naquet,et al.  Visual features of intermediate complexity and their use in classification , 2002, Nature Neuroscience.

[28]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[29]  Nicolas Pinto,et al.  Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..

[30]  Thomas Serre,et al.  A Component-based Framework for Face Detection and Identification , 2007, International Journal of Computer Vision.

[31]  David G. Lowe,et al.  Multiclass Object Recognition with Sparse, Localized Features , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[32]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[33]  Gustavo Deco,et al.  Computational neuroscience of vision , 2002 .

[34]  David D. Cox,et al.  Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition , 2022 .

[35]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[36]  R. K. Simpson Nature Neuroscience , 2022 .

[37]  Thomas Serre,et al.  A quantitative theory of immediate visual recognition. , 2007, Progress in brain research.

[38]  David I. Perrett,et al.  Neurophysiology of shape processing , 1993, Image Vis. Comput..

[39]  Thomas Serre,et al.  A Biologically Inspired System for Action Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[40]  Thomas Serre,et al.  Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  H E M Journal of Neurophysiology , 1938, Nature.

[42]  Thomas Serre,et al.  Learning complex cell invariance from natural videos: A plausibility proof , 2007 .

[43]  Bartlett W. Mel SEEMORE: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition , 1997, Neural Computation.

[44]  Jay Hegdé,et al.  Reappraising the Functional Implications of the Primate Visual Anatomical Hierarchy , 2007, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[45]  Heiko Wersing,et al.  Learning Optimized Features for Hierarchical Models of Invariant Object Recognition , 2003, Neural Computation.

[46]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .

[47]  Simon J. Thorpe,et al.  Ultra-Rapid Scene Categorization with a Wave of Spikes , 2002, Biologically Motivated Computer Vision.