Edge linearization operators are often used in computervision and in neural network models of vision to reconstruct noisy or incomplete edges. Such operators gather evidence for the presence of an edge at various ori...
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Edge linearization operators are often used in computervision and in neural network models of vision to reconstruct noisy or incomplete edges. Such operators gather evidence for the presence of an edge at various orientations across all image locations and then choose the orientation that best fits the data at each point. One disadvantage of such methods is that they often function in a winner-take-all fashion: the presence of only a single orientation can be represented at any point;multiple edges cannot be represented where they intersect. For example, the neural Boundary Contour System of Grossberg and Mingolla implements a form of winner-take-all competition between orthogonal orientations at each spatial location, to promote sharpening of noisy, uncertain image data. But that competition may produce rivalry, oscillation, instability, or mutual suppression when intersecting edges (e.g., a cross) are present. This 'cross problem' exists for all techniques, including Markov Random Fields, where a representation of a chosen favored orientation suppresses representations of alternate orientations. A new adaptive technique, using both an inhibitory learning rule and an excitatory learning rule, weakens inhibition between neurons representing poorly correlated orientations. It may reasonably be assumed that neurons coding dissimilar orientations are less likely to be coactivated than neurons coding similar orientations. Multiplexing by superposition is ordinarily generated: combinations of intersecting edges become represented by simultaneous activation of multiple neurons, each of which represents a single supported oriented edge. Unsupported or weakly supported orientations are suppressed. The cross problem is thereby solved.
The authors ask what is the complexity of computing equilibria for physically realizable analog networks like those of J.J. Hopfield (1984) and T.J. Sejnowski (1981) with arbitrary connectivity. It is shown that, if t...
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The authors ask what is the complexity of computing equilibria for physically realizable analog networks like those of J.J. Hopfield (1984) and T.J. Sejnowski (1981) with arbitrary connectivity. It is shown that, if the amplifiers are piecewise-linear, then such networks are instances of a game-theoretic model known as polymatrix games. Equilibria for the latter may be computed by vertex pivoting algorithms similar to the simplex method for linear programming, which are in practice of low order polynomial complexity. These algorithms appear to be the only ones both guaranteed to work and which are polynomial in practice. These results suggest that networks with few nonstable equilibria would be computationally attractive.< >
Two major structuring element decomposition techniques are compared, and the superiority of the two pixel decomposition techniques over the cellular decomposition technique is shown in terms of the number of pipeline ...
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Two major structuring element decomposition techniques are compared, and the superiority of the two pixel decomposition techniques over the cellular decomposition technique is shown in terms of the number of pipeline stages. As for the general structuring function decomposition, to the authors' knowledge, there is no efficient algorithm that has been found. The difficulty can be overcome by using an adequate representation of the general gray-scale structuring function. Representing a gray-scale image as a specific 3-D set, i.e. an umbra, makes it easier to shift all morphological theorems from the binary domain to the gray-scale domain; however, a direct umbra representation is not appropriate for the general gray-scale structuring function decomposition. The authors also provide a morphologically realizable representation and decomposition for the general gray-scale structuring function and show recursive algorithms which are pipelineable for efficiently performing gray-scale morphological operations.< >
computervision technology offers potential application for automation of space operations. The merits of computervision and automation for earth bound operations are magnified when they are applied to space. Human i...
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Our goal is to identify an inexpensive, accurate, and safe system for obtaining digitized range data for use in a variety of object recognition tasks. In this paper we present initial results on the use of a non-colli...
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Robot servoing from video rate data inputs is now possible. Area Parameter Acceleration techniques allow computers to isolate and geometrically describe as many as 255 objects each 33 millisecond video frame time. Fro...
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Robot servoing from video rate data inputs is now possible. Area Parameter Acceleration techniques allow computers to isolate and geometrically describe as many as 255 objects each 33 millisecond video frame time. From the parameters of each object, the host processor may determine size, location, orientation, and shape. It then may recognize appropriate objects and select the right target for robot action.
One of the major deficiencies in current robot control schemes is the lack of high-level knowledge in the feedback loop. Typically, the sensory data acquired are fed back to the robot controller with minimal amount of...
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Traditionally, a programmer's role has been reduced to designing computervisionalgorithms and coding them in a machine independent language since the knowledge of the architecture has been implicit in the compil...
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