Throwing Down the Visual Intelligence Gauntlet

In recent years, scientific and technological advances have produced artificial systems that have matched or surpassed human capabilities in narrow domains such as face detection and optical character recognition. However, the problem of producing truly intelligent machines still remains far from being solved. In this chapter, we first describe some of these recent advances, and then review one approach to moving beyond these limited successes – the neuromorphic approach of studying and reverse-engineering the networks of neurons in the human brain (specifically, the visual system). Finally, we discuss several possible future directions in the quest for visual intelligence.

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

[2]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[3]  Leslie G. Ungerleider,et al.  Object vision and spatial vision: two cortical pathways , 1983, Trends in Neurosciences.

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

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

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

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

[8]  Edmund T. Rolls,et al.  A Model of Invariant Object Recognition in the Visual System: Learning Rules, Activation Functions, Lateral Inhibition, and Information-Based Performance Measures , 2000, Neural Computation.

[9]  S. Thorpe,et al.  Seeking Categories in the Brain , 2001, Science.

[10]  P. Fldik,et al.  The Speed of Sight , 2001, Journal of Cognitive Neuroscience.

[11]  P. Perona,et al.  Rapid natural scene categorization in the near absence of attention , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[12]  T. Gawne,et al.  Responses of primate visual cortical V4 neurons to simultaneously presented stimuli. , 2002, Journal of neurophysiology.

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

[14]  C. Koch,et al.  Visual Selective Behavior Can Be Triggered by a Feed-Forward Process , 2003, Journal of Cognitive Neuroscience.

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

[16]  Tomaso Poggio,et al.  Intracellular measurements of spatial integration and the MAX operation in complex cells of the cat primary visual cortex. , 2004, Journal of neurophysiology.

[17]  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.

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

[19]  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 .

[20]  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).

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

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

[23]  T. Poggio,et al.  A model of V4 shape selectivity and invariance. , 2007, Journal of neurophysiology.

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

[25]  D. C. Essen,et al.  Neurons in monkey visual area V2 encode combinations of orientations , 2007, Nature Neuroscience.

[26]  J. Movshon,et al.  Structure and Function Come Unglued in the Visual Cortex , 2008, Neuron.

[27]  Nicolas Pinto,et al.  Establishing Good Benchmarks and Baselines for Face Recognition , 2008 .

[28]  Nicolas Pinto,et al.  How far can you get with a modern face recognition test set using only simple features? , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Thomas Serre,et al.  A neuromorphic approach to computer vision , 2010, Commun. ACM.

[30]  Thomas Serre,et al.  Automated home-cage behavioural phenotyping of mice. , 2010, Nature communications.