Complex cells and Object Recognition

Nearest-neighbor correlation-based similarity computation in the space of outputs of complex-type receptive fields can support robust recognition of 3D objects. Our experiments with four collections of objects resulted in mean recognition rates between 84% (for subordinate-level discrimination among 15 quadruped animal shapes) and 94% (for basic-level recognition of 20 everyday objects), over a 40deg X 40deg range of viewpoints, centered on a stored canonical view and related to it by rotations in depth (comparable figures were obtained for image-plane translations). This result has interesting implications for the design of a front end to an artificial object recognition system, and for the understanding of the faculty of object recognition in primate vision.

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

[2]  Kunihiko Fukushima,et al.  Neocognitron: A hierarchical neural network capable of visual pattern recognition , 1988, Neural Networks.

[3]  H. Spitzer,et al.  Complex-cell receptive field models , 1988, Progress in Neurobiology.

[4]  Heinrich H. Bülthoff,et al.  Psychophysical support for a 2D view interpolation theory of object recognition , 1991 .

[5]  D. Heeger Normalization of cell responses in cat striate cortex , 1992, Visual Neuroscience.

[6]  I. Ohzawa,et al.  Local intracortical connections in the cat's visual cortex: postnatal development and plasticity. , 1994, Journal of neurophysiology.

[7]  S. Edelman Receptive Fields for Vision: from Hyperacuity to Object Recognition , 1995 .

[8]  F. Girosi,et al.  Regularization Theory and Neural Networks , 1995 .

[9]  Shimon Edelman,et al.  Receptive field spaces and class-based generalization from a single view in face recognition , 1995 .

[10]  Tomaso A. Poggio,et al.  Regularization Theory and Neural Networks Architectures , 1995, Neural Computation.

[11]  Tomaso A. Poggio,et al.  Image Synthesis from a Single Example Image , 1996, ECCV.

[12]  Edmund T. Rolls,et al.  Visual Processing in the Temporal Lobe for Invariant Object Recognition , 1996 .

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

[14]  Ning Qian,et al.  Physiological computation of binocular disparity , 1997, Vision Research.

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

[16]  Nathan Intrator,et al.  Learning as Extraction of Low-Dimensional Representations , 1997 .