Projective distortion of surface texture observed in a perspective image can provide direct information about the shape of the underlying surface. Previous theories have generally concerned planar surfaces;this paper ...
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Projective distortion of surface texture observed in a perspective image can provide direct information about the shape of the underlying surface. Previous theories have generally concerned planar surfaces;in this pap...
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In this paper we study small depth circuits that contain threshold gates (with or without weights) and parity gates. All circuits we consider are of polynomial size. We prove several results which complete the work on...
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A highly simplified network model of cortical associative memory, based on Hebb's theory of cell assemblies, has been developed and simulated. The network comprises realistically modelled pyramidal-type cells and ...
A highly simplified network model of cortical associative memory, based on Hebb's theory of cell assemblies, has been developed and simulated. The network comprises realistically modelled pyramidal-type cells and inhibitory fast-spiking interneurons and its connectivity is adopted from a trained recurrent artificial neural network. After-activity, pattern completion and competition between cell assemblies is readily produced. If, instead of pyramidal cells, motor neuron type cells are used, network behaviour changes drastically. For instance, spike synchronization can be observed but after-activity is hard to produce. The authors results support the biological feasibility of Hebb's cell assembly theory. The analogy between this theory and recurrent artificial neural network models is discussed.
The paper outlines a method for designing near optimal nonlinear classifiers based on a self-organizing technique for estimating probability density functions when only weak assumptions are made about the densities. T...
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The paper outlines a method for designing near optimal nonlinear classifiers based on a self-organizing technique for estimating probability density functions when only weak assumptions are made about the densities. The classical parametric and nonparametric methods for estimating density functions have a number of drawbacks;parametric methods give weak results on unknown distributions, while nonparametric methods require extensive amounts of design samples, storage capacity, and computing power. The present method avoids these disadvantages by parameterizing a set of component densities from which the actual densities are constructed. The parameters of the component densities are optimized by a self-organizing algorithm, reducing to a minimum the labeling of design samples. All the required computations are realized with the simple "sum of product" units commonly used in connectionist models. The density approximations produced by the method are illustrated in two dimensions for a multispectral image classification task. The practical use of the method is illustrated by a small speech recognition problem, that of recognizing 18 Swedish consonants. Related issues of invariant projections, cross-class pooling of data, and subspace partitioning are also discussed.
We investigate the power of threshold circuits of small depth. In particular, we give functions that require exponential size unweighted threshold circuits of depth 3 when we restrict the bottom fanin. We also prove t...
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Focus-of-attention is extremely important in human visual perception. If computer vision systems are to perform tasks in a complex, dynamic world they will have to be able to control processing in a way that is analog...
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Focus-of-attention is extremely important in human visual perception. If computer vision systems are to perform tasks in a complex, dynamic world they will have to be able to control processing in a way that is analogous to visual attention in humans. Problems connected to foveation (examination of selected regions of the world at high resolution) are examined. In particular, the problem of finding and classifying junctions from this aspect is considered. It is shown that foveation as simulated by controlled, active zooming in conjunction with scale-space techniques allows for robust detection and classification of junctions.
An efficient parallel algorithm for the tree-decomposition problem for fixed width w is presented. The algorithm runs in time O(log/sup 3/ n) and uses O(n) processors on a concurrent-read, concurrent-write parallel ra...
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An efficient parallel algorithm for the tree-decomposition problem for fixed width w is presented. The algorithm runs in time O(log/sup 3/ n) and uses O(n) processors on a concurrent-read, concurrent-write parallel random access machine (CRCW PRAM). This result can be used to construct efficient parallel algorithms for three important classes of problems: MS (monadic second-order) properties, linear EMS (extended monadic second-order) extremum problems, and enumeration problems for MS properties, for graphs of tree width at most w. The sequential time complexity of the tree-composition problem for fixed w is improved, and some implications for this improvement are stated.< >
The cylindrical algebraic decomposition method decomposes E r into regions over which a given polynomial has constant sign by extension of one complicated decomposition of E r-1 . We investigate a method which decompo...
The cylindrical algebraic decomposition method decomposes E r into regions over which a given polynomial has constant sign by extension of one complicated decomposition of E r-1 . We investigate a method which decomposes E r into sign-invariant region by combining several but simpler decompositions of E r-1 . We can obtain a sign-invariaat decomposition of E 2 defined by a bivariate polynomial of total degree n and coefficient size d in time O(n 12 (d + log n) 2 log n) . Preliminary experiments suggest that the method is useful in practice.
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