Journal ArticleDOI
A network that learns to recognize three-dimensional objects.
Tomaso Poggio,Shimon Edelman +1 more
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TLDR
In this article, a method based on the theory of approximation of multivariate functions is proposed to learn from a small set of perspective views a function mapping any viewpoint to a standard view.Abstract:
THE visual recognition of three-dimensional (3-D) objects on the basis of their shape poses at least two difficult problems. First, there is the problem of variable illumination, which can be addressed by working with relatively stable features such as intensity edges rather than the raw intensity images1,2. Second, there is the problem of the initially unknown pose of the object relative to the viewer. In one approach to this problem, a hypothesis is first made about the viewpoint, then the appearance of a model object from such a viewpoint is computed and compared with the actual image3–7. Such recognition schemes generally employ 3-D models of objects, but the automatic learning of 3-D models is itself a difficult problem8,9. To address this problem in computational vision, we have developed a scheme, based on the theory of approximation of multivariate functions, that learns from a small set of perspective views a function mapping any viewpoint to a standard view. A network equivalent to this scheme will thus 'recognize' the object on which it was trained from any viewpoint.read more
Citations
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Journal ArticleDOI
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.
Rajesh P. N. Rao,Dana H. Ballard +1 more
TL;DR: Results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.
Journal ArticleDOI
Networks for approximation and learning
Tomaso Poggio,Federico Girosi +1 more
TL;DR: Regularization networks are mathematically related to the radial basis functions, mainly used for strict interpolation tasks as mentioned in this paper, and two extensions of the regularization approach are presented, along with the approach's corrections to splines, regularization, Bayes formulation, and clustering.
Journal ArticleDOI
Hierarchical models of object recognition in cortex
TL;DR: A new hierarchical model consistent with physiological data from inferotemporal cortex that accounts for this complex visual task and makes testable predictions is described.
Journal ArticleDOI
Face recognition: features versus templates
Roberto Brunelli,Tomaso Poggio +1 more
TL;DR: Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching are presented.
References
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Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Journal ArticleDOI
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Journal Article
Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks
David S. Broomhead,David Lowe +1 more
TL;DR: The relationship between 'learning' in adaptive layered networks and the fitting of data with high dimensional surfaces is discussed, leading naturally to a picture of 'generalization in terms of interpolation between known data points and suggests a rational approach to the theory of such networks.
Journal ArticleDOI
A computer algorithm for reconstructing a scene from two projections
TL;DR: A simple algorithm for computing the three-dimensional structure of a scene from a correlated pair of perspective projections is described here, when the spatial relationship between the two projections is unknown.
Book
The Interpretation of Visual Motion
TL;DR: In this paper, the authors used the methodology of artificial intelligence to investigate the phenomena of visual motion perception: how the visual system constructs descriptions of the environment in terms of objects, their three-dimensional shape, and their motion through space, on the basis of the changing image that reaches the eye.
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