We present a novel approach to solving the trajectory plan ning problem (TPP) in time-varying environments. The es sence of our approach lies in a heuristic but natural decom position of TPP into two subproblems: (1) ...
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We present a novel approach to solving the trajectory plan ning problem (TPP) in time-varying environments. The es sence of our approach lies in a heuristic but natural decom position of TPP into two subproblems: (1) planning a path to avoid collision with static obstacles and (2) planning the velocity along the path to avoid collision with moving obsta cles. We call thefirst subproblem the path planning problem (PPP) and the second the velocity planning problem (VPP). Thus, our decomposition is summarized by the equation TPP => PPP + VPP. The symbol => indicates that the de composition holds under certain assumptions, e.g., when obstacles are moving independently of (i.e., not tracking ) the robot. Furthermore, we pose the VPP in path-time space, where time is explicitly represented as an extra dimension, and reduce it to a graph search in this space. In fact, VPP is transformed to a two-dimensional PPP in path-time space with some additional constraints. Algorithms are then pre sented to solve the VPP with different optimality criteria: minimum length in path-time space, and minimum time.
A common task in computervision is to recognize the objects in an image. Most computervision systems do this by matching models for each possible object type in turn, recognizing objects by the best matches. This is...
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A common task in computervision is to recognize the objects in an image. Most computervision systems do this by matching models for each possible object type in turn, recognizing objects by the best matches. This is not ideal, as it does not take advantage of the similarities and differences between the possible object types. The computation time also increases linearly with the number of possible objects, which can become a problem if the number is large. This paper describes a new recognition method, the feature indexed hypotheses method, which takes advantage of the similarities and differences between object types, and is able to handle cases, where there are a large number of possible object types, in sub-linear computation time. A two-dimensional occluded parts recognition system using this method is described.
Orientation selection is the inference of orientation information out of images. It is one of the foundations on which other visual structures are built, since it must precede the formation of contours out of pointill...
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Orientation selection is the inference of orientation information out of images. It is one of the foundations on which other visual structures are built, since it must precede the formation of contours out of pointillist data and surfaces out of surface markings. We take a differential geometric view in defining orientation selection, and develop algorithms for actually doing it. The goal of these algorithms is formulated in mathematical terms as the inference of a vector field of tangents (to the contours), and the algorithms are studied in both abstract and computational forms. They are formulated as matching problems, and algorithms for solving them are reduced to biologically plausible terms. We show that two different matching problems are necessary, the first for 1-dimensional contours (which we refer to as Type I processes) and second for 2-dimensional flows (or Type II processes). We conjecture that this difference is reflected in the response properties of “simple” and “complex” cells, respectively, and predict several other psychophysical phenomena.
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