Models of object recognition

Understanding how biological visual systems recognize objects is one of the ultimate goals in computational neuroscience. From the computational viewpoint of learning, different recognition tasks, such as categorization and identification, are similar, representing different trade-offs between specificity and invariance. Thus, the different tasks do not require different classes of models. We briefly review some recent trends in computational vision and then focus on feedforward, view-based models that are supported by psychophysical and physiological data.

[1]  Wayne D. Gray,et al.  Basic objects in natural categories , 1976, Cognitive Psychology.

[2]  D. Marr,et al.  Representation and recognition of the spatial organization of three-dimensional shapes , 1978, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[3]  D C Van Essen,et al.  Shifter circuits: a computational strategy for dynamic aspects of visual processing. , 1987, Proceedings of the National Academy of Sciences of the United States of America.

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

[5]  T. Poggio,et al.  A network that learns to recognize three-dimensional objects , 1990, Nature.

[6]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

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

[8]  D I Perrett,et al.  Organization and functions of cells responsive to faces in the temporal cortex. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[9]  I. Biederman,et al.  Dynamic binding in a neural network for shape recognition. , 1992, Psychological review.

[10]  M. Young,et al.  Sparse population coding of faces in the inferotemporal cortex. , 1992, Science.

[11]  H H Bülthoff,et al.  Psychophysical support for a two-dimensional view interpolation theory of object recognition. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[12]  D Mumford,et al.  On the computational architecture of the neocortex. II. The role of cortico-cortical loops. , 1992, Biological cybernetics.

[13]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  I. Biederman,et al.  Recognizing depth-rotated objects: evidence and conditions for three-dimensional viewpoint invariance. , 1993, Journal of experimental psychology. Human perception and performance.

[15]  D. V. van Essen,et al.  A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

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

[17]  P M Gochin.  Properties of simulated neurons from a model of primate inferior temporal cortex. , 1994, Cerebral cortex.

[18]  Keiji Tanaka,et al.  Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.

[19]  N. Logothetis,et al.  View-dependent object recognition by monkeys , 1994, Current Biology.

[20]  B. C. Motter,et al.  Neural correlates of feature selective memory and pop-out in extrastriate area V4 , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[21]  Leslie G. Ungerleider,et al.  ‘What’ and ‘where’ in the human brain , 1994, Current Opinion in Neurobiology.

[22]  M J Tarr,et al.  Is human object recognition better described by geon structural descriptions or by multiple views? Comment on Biederman and Gerhardstein (1993). , 1995, Journal of experimental psychology. Human perception and performance.

[23]  Geoffrey E. Hinton,et al.  The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.

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

[25]  Keiji Tanaka,et al.  Optical Imaging of Functional Organization in the Monkey Inferotemporal Cortex , 1996, Science.

[26]  S. Ullman,et al.  Generalization to Novel Images in Upright and Inverted Faces , 1993, Perception.

[27]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[28]  Keiji Tanaka,et al.  Inferotemporal cortex and object vision. , 1996, Annual review of neuroscience.

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

[30]  S. Ullman.  High-Level Vision: Object Recognition and Visual Cognition , 1996 .

[31]  R. Desimone,et al.  Neural Mechanisms of Visual Working Memory in Prefrontal Cortex of the Macaque , 1996, The Journal of Neuroscience.

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

[33]  E. Rolls,et al.  INVARIANT FACE AND OBJECT RECOGNITION IN THE VISUAL SYSTEM , 1997, Progress in Neurobiology.

[34]  Tomaso A. Poggio,et al.  Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[35]  Rajesh P. N. Rao,et al.  Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex , 1997, Neural Computation.

[36]  R. Desimone,et al.  Responses of Neurons in Inferior Temporal Cortex during Memory- Guided Visual Search , 1998 .

[37]  Keiji Tanaka,et al.  Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. , 1998, Journal of neurophysiology.

[38]  Isabel Gauthier,et al.  Three-dimensional object recognition is viewpoint dependent , 1998, Nature Neuroscience.

[39]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[40]  E. Rolls,et al.  View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex. , 1998, Cerebral cortex.

[41]  Heinrich H Bülthoff,et al.  Image-based object recognition in man, monkey and machine , 1998, Cognition.

[42]  T. Poggio,et al.  Are Cortical Models Really Bound by the “Binding Problem”? , 1999, Neuron.

[43]  Shimon Edelman,et al.  Representation and recognition in vision , 1999 .

[44]  Yali Amit,et al.  A Computational Model for Visual Selection , 1999, Neural Computation.

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

[46]  Anuj Mohan.  Object Detection in Images by Components , 1999 .

[47]  Tomaso Poggio,et al.  A Note on Object Class Representation and Categorical Perception , 1999 .

[48]  Tomaso Poggio,et al.  The Individual is Nothing, the Class Everything: Psychophysics and Modeling of Recognition in Obect Classes , 2000 .

[49]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[50]  Pietro Perona,et al.  Towards automatic discovery of object categories , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[51]  IEEE conference on computer vision and pattern recognition , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[52]  Massimiliano Pontil,et al.  Face Detection in Still Gray Images , 2000 .

[53]  E. Miller,et al.  THE PREFRONTAL CORTEX AND COGNITIVE CONTROL , 2000 .

[54]  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.

[55]  E. Miller,et al.  The prefontral cortex and cognitive control , 2000, Nature Reviews Neuroscience.

[56]  Aapo Hyvärinen,et al.  Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.

[57]  M. Tarr,et al.  Visual Object Recognition , 1996, ISTCS.