Paper
19 February 1988 Localized Noise Propagation Effects In Parameter Transforms
Andrea Califano, Ruud M. Bolle
Author Affiliations +
Proceedings Volume 0848, Intelligent Robots and Computer Vision VI; (1988) https://doi.org/10.1117/12.942724
Event: Advances in Intelligent Robotics Systems, 1987, Cambridge, CA, United States
Abstract
A parameter transform produces a density function on a parameter space. Ideally each instance of a parametric shape in the input would contribute to the density with a delta function. Due to noise these delta functions will be broadened. However, depending on the location and orientation of the parametric shapes in the input, differently shaped peaks will result. The reason for this is twofold: (1) In general a parameter transform is a nonlinear operation; (2) A parameter transform may also be a function of the location of the parametric shape in the input. We present a general framework that deals with both the above mentioned problems. By weighing the response of the transform by the determinant of a matrix, we obtain a more homogeneous response. This response preserves heights instead of volumes in the parameter space. We briefly touch upon the usefulness of these techniques for organizing the behavior of connectionist networks. Illustrative examples of parameter transform responses are given.
© (1988) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Andrea Califano and Ruud M. Bolle "Localized Noise Propagation Effects In Parameter Transforms", Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); https://doi.org/10.1117/12.942724
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Cited by 1 scholarly publication.
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KEYWORDS
Transform theory

Computer vision technology

Machine vision

Robot vision

Robots

Quantization

Image segmentation

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