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Proceedings Paper

Tactile pattern recognition with complex linear morphology
Author(s): Mohammad Rahmati; Laurence G. Hassebrook; Hsienchung Chi; Gongliang Guo; William A. Gruver
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Paper Abstract

Tactile information processing has received a relatively small amount of attention in the area of pattern recognition. However, while most of the attention has been given to image, acoustic, and electromagnetic signal processing, there are many applications where tactile information processing is applicable. For example, underwater salvage operations where the water may be opaque with debris and target objects may be covered with viscous substances like silt, sand, mud, or camouflaged by crustaceans. These environments may also be hazardous to humans, because of pressure, temperature, or contamination. Another example is industrial assembly where subcomponents, initially in random position and orientation, need to be assembled together. We assume that an object has been tactilly sampled into a polyhedron mesh. Our concentration in this writing is to identify this mesh as belonging to a target object independent of orientation and position. To solve this problem we present a fundamental approach we call Complex Linear Morphology (CLM). This technique involves non-linear architectures which rely on banks of linear correlation filter elements thus the term linear. These elements are comprised of complex weighted training images or solids, thus the term complex. These complex weights are used to approximate logical operations on the input images or solids which result in the discrimination of target objects from clutter objects, thus the term morphology. There are two architectures presented. The first architecture assumes the 3-D polyhedron mesh is converted to a 2-D image by projection. CLM is applied to these 2-D images which are rotation-variant. The second architecture uses CLM techniques to process 3-D information directly. Results are presented for the 2-D CLM approach and techniques are presented for the 3-D CLM approach.

Paper Details

Date Published: 1 July 1992
PDF: 12 pages
Proc. SPIE 1702, Hybrid Image and Signal Processing III, (1 July 1992); doi: 10.1117/12.60547
Show Author Affiliations
Mohammad Rahmati, Univ. of Kentucky (United States)
Laurence G. Hassebrook, Univ. of Kentucky (United States)
Hsienchung Chi, Univ. of Kentucky (United States)
Gongliang Guo, Univ. of Kentucky (United States)
William A. Gruver, Univ. of Kentucky (United States)

Published in SPIE Proceedings Vol. 1702:
Hybrid Image and Signal Processing III
David P. Casasent; Andrew G. Tescher, Editor(s)

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