This paper considers the control of two robotic manipulators jointly grasping-with no slippage-a rigid object. Two conflicting control problems are addressed: Adaptively apportioning the object load between the manipu...
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This paper considers the control of two robotic manipulators jointly grasping-with no slippage-a rigid object. Two conflicting control problems are addressed: Adaptively apportioning the object load between the manipulators and application of commanded interactive forces (or bias forces) to the object. By controlling the interactive forces, damage to the manipulators and/or the object can be prevented. Additionally, desired tension, torsion, or compression can be applied to the object. Adaptive load apportioning (sharing) increases the carrying capacity of the system by using possible mechanical advantage. The Hopfield net is used to minimize a quadratic energy function in the joint torques. The results are the optimal joint torques required to drive the load and supply the commanded bias force. Simulations are presented that show effective control of interactive forces while sharing the load in an optimal fashion.
Arranging the photosensitive elements of an imaging sensor in a log-polar grid automatically samples an image in a log-polar space. The Retina project is a chip with such a spatially varying layout that can produce th...
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Arranging the photosensitive elements of an imaging sensor in a log-polar grid automatically samples an image in a log-polar space. The Retina project is a chip with such a spatially varying layout that can produce the advantages of image processing in the new space at real-time speeds. The actual chip is a small part of a complete imaging system. The system is part of a class of imagers called foveal sensors, and these sensors have distinct and significant computational savings over conventional imagers, as many as 3-10 orders of magnitude improvement in processing time and memory. The design maintains a large region of high-resolution data, although it is still only a fraction of the total photosensitive area.
We explore the idea that the visual system uses specialized processes to extract critical information about 3-D motion and structure for visually-guided navigation. We first consider the computation of three essential...
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We explore the idea that the visual system uses specialized processes to extract critical information about 3-D motion and structure for visually-guided navigation. We first consider the computation of three essential properties: the relative 3-D direction of heading of the observer, the time-to-collision with approaching object surfaces, and the locations of object boundaries defined by motion discontinuities. We then focus on the heading computation, relating current algorithms to the perception of heading direction.
There is a growing need for progress in the technology for the management of knowledge derived from sensory data. In this paper we address the structure and operations of a cellular 'Experiential Knowledge Base - ...
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There is a growing need for progress in the technology for the management of knowledge derived from sensory data. In this paper we address the structure and operations of a cellular 'Experiential Knowledge Base - E*KB' system, which acts as a cognitive prosthesis to the decision maker. We concern ourselves with the experiential knowledge representation techniques, and with the methods and tools required for the extraction (relevance filtering), the contextual abstraction (data compression and generalization), the classification and storage of real time sensory knowledge in the cellular architecture of the E*KB.
We investigate the use of chromatic information in dense stereo correspondence. Specifically, the chromatic photometric constraint, which is used to specify a mathematical optimality criterion for solving the dense st...
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We investigate the use of chromatic information in dense stereo correspondence. Specifically, the chromatic photometric constraint, which is used to specify a mathematical optimality criterion for solving the dense stereo correspondence problem, is developed. The result is a theoretical construction for developing dense stereo correspondence algorithms which use chromatic information. The efficacy of using chromatic information via this construction is tested by implementing single-and multi-resolution versions of a stereo correspondence algorithm which uses simulated annealing as a means of solving the optimization problem. Results demonstrate that the use of chromatic information can significantly improve the performance of dense stereo correspondence.
A recognition system, which represents object categories by properties which can be deduced by analysis of 3-D shape, has been implemented and tested using the category 'chair' for case study. Functional descr...
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A recognition system, which represents object categories by properties which can be deduced by analysis of 3-D shape, has been implemented and tested using the category 'chair' for case study. Functional description is used to recognize classes and identify subclasses of known categories of objects, even if the specific object has never been encountered previously. Interpretation of the functionality of an object is accomplished through qualitative reasoning about its shape. This is, to our knowledge, the first implemented system to explore the use of purely function-based representation (that is, no geometric or structural object model) to recognize 3-D objects. During the recognition process, evidence is gathered as to how well the functional requirements are met by the input structure. This paper is concerned with choosing the type of operators that will be used in the combination or accrual of the functional evidence. Three pairs of conjunctive and disjunctive operators are evaluated. Each pair is used in the recognition process of the 100 + test objects. The results of all tests run are compared and differences are discussed.
Range images incorporate 3-D surface coordinates of a scene and are well suited for a variety of vision applications. For tasks, such as 3-D object recognition, a representation of the object(s) present in the image i...
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Range images incorporate 3-D surface coordinates of a scene and are well suited for a variety of vision applications. For tasks, such as 3-D object recognition, a representation of the object(s) present in the image is derived and then matched with stored models to determine the object(s) identity. Surface based representation is the most widely used representation in range image analysis. To generate surface based representation of an object, a segmentation of the object into a number of surfaces is needed. In this paper we present an approach for the segmentation of range images into a number of surfaces that, in turn, can be used to generate surface based representation. The approach integrates both edge detection and region growing techniques to achieve the segmentation. We start by detecting jump edges. Jump edge map is processed and regions surrounded by jump edges are isolated. Next, fold edges are detected iteratively using normals and residual. Fold edge map is processed to obtain the final segmented image. Jump and fold edge maps are processed using a Bayesian approach. The apriori knowledge is modeled using Markov Random Field. For jump edges we have used a coupled line and depth process. For fold edges we have combined line, residuals and normals to process the fold edge map. The performance of the algorithm on a number of range images is presented.
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.< >
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