The authors present a stereo vision system in which they attempt to achieve robustness with respect to scene characteristics, from textured outdoor scenes to environments composed of highly regular man-made objects. T...
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The authors present a stereo vision system in which they attempt to achieve robustness with respect to scene characteristics, from textured outdoor scenes to environments composed of highly regular man-made objects. The system offers the advantages of both area-based (dense map) and feature-based (accurate disparity) processing by combining them whenever possible. The authors are able to geneate a disparity map that is sufficiently accurate to allow them to detect depth and surface orientation discontinuities, provided that the resolution is fine enough. They use an area-based cross-correlation, along with an ordering constraint and a weak surface smoothness assumption to produce an initial disparity map. Unlike other approaches, however, a match is accepted only if both views agree on a correlation peak and this peak is strong enough. This disparity map is a blurred version of the true one, however, because of the smoothing inherent in the correlation. The problem is most acute at C/sub 0/ (depth) and C/sub 1/ (crease) discontinuities but can be mitigated by introducing the edge information: the disparity map is adaptively smoothed subject to the constraint that the disparity at edges is fixed.< >
The authors discuss some of the issues that have to be tackled in order to perform geometric reasoning from range imagery. They begin by pointing out that a successful system must deal with real-world data, and theref...
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The authors discuss some of the issues that have to be tackled in order to perform geometric reasoning from range imagery. They begin by pointing out that a successful system must deal with real-world data, and therefore take into account the effects of noise and quantization. They suggest that adaptive smoothing may prove to be a helpful tool for such a task. The next stage of processing involves a symbolic representation of the original data. The authors spell out criteria for shape description, discuss current representation schemes and point out their limitations, and then propose some ideas for overcoming such limitations, illustrated on real examples. Finally, they look at the issues in recognition, and more specifically the matching part, with reference to different methodologies, tree search and constraint satisfaction network.< >
The authors present a method to smooth a signal-whether it is an intensity image, a range image, or a contour-which preserves discontinuities and thus facilitates their detection. This is achieved by repeatedly convol...
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The authors present a method to smooth a signal-whether it is an intensity image, a range image, or a contour-which preserves discontinuities and thus facilitates their detection. This is achieved by repeatedly convolving the signal with a very small averaging filter modulated by a measure of the signal discontinuity at each point. This process is related to the anisotropic diffusion reported by P. Perona and J. Malik (1987) but it has a much simpler formulation and is not subject to instability or divergence. Real examples show how this approach can be applied to the smoothing of various types of signals. The detected features do not move, and thus no tracking is needed. The last property makes it possible to derive a novel scale-space representation of a signal using a small number of scales. Finally, this process is easily implemented on parallel architectures: the running time on a 16 K connection machine is three orders of magnitude faster than on a serial machine.< >
A mathematical framework for reconditioning additive-type models is proposed, and a neuro-operator, based on the chaotic relaxation paradigm, whose resulting dynamics is neither concurrently synchronous nor sequential...
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A mathematical framework for reconditioning additive-type models is proposed, and a neuro-operator, based on the chaotic relaxation paradigm, whose resulting dynamics is neither concurrently synchronous nor sequentially asynchronous is derived. Necessary and sufficient conditions guaranteeing concurrent asynchronous convergence are established in terms of contracting operators. Lyapunov exponents are also computed to characterize the network dynamics and to ensure that throughput-limiting chaotic behavior in models reconditioned with concurrently asynchronous algorithms has been eliminated.< >
A characterization of the structure of uncertainty called the generalized matching condition is proposed. The uncertainty is further decomposed into two parts that are 'orthogonal' to each other. On the basis ...
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A characterization of the structure of uncertainty called the generalized matching condition is proposed. The uncertainty is further decomposed into two parts that are 'orthogonal' to each other. On the basis of this decomposition a linear feedback control design is proposed for linear uncertain systems. The control always exists if the generalized matching condition is met. The design is illustrated by a practical example.< >
A model-based vision system requires models in order to predict object appearances. How an object appears in the image is the result of interaction between the object properties and the sensor characteristics. Thus in...
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This paper presents a model-based object recognition method which combines a bottom-up evidence accumulation process and a top-down hypothesis verification process. The hypothesize-and-test paradigm is fundamental in ...
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