Tactile Binary Image Identification

Tactile Binary Image Identification Recognition of a tactile image independent of position, size and orientation has been a goal of much recent research. Many tasks e.g. parts identification often give rise to situations which demand a more generalized methodology than the derivation of a single forward measurement, such as the computation of part area and perimeter from its runlengthcoding representation. In this situation, an interpretation procedure generally adopts the techniques and methodology of a pattern recognition approach. To achieve maximum utility and flexibility, the methods used should be sensitive to any image change in size, translation and rotation, and should provide good repeatability. The algorithm used in this article generally meets these conditions. The results show that recognition schemes based on these invariants are position, size and orientation independent, and also flexible enough to learn most sets of parts. Assuming that parts can vary only in location, orientation and size, then certain moments are very convenient for normalization. For instance, the first moments of area give the centroid of a part, which is a natural origin of coordinates for translation invariant measurements. Similarly, the eigenvectors of the matrix of second central moments define the directions of principal axes, which leads to rotation moment invariant measurements. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sensor Review Emerald Publishing

Tactile Binary Image Identification

Sensor Review, Volume 13 (4): 6 – Apr 1, 1993

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0260-2288
DOI
10.1108/eb007916
Publisher site
See Article on Publisher Site

Abstract

Recognition of a tactile image independent of position, size and orientation has been a goal of much recent research. Many tasks e.g. parts identification often give rise to situations which demand a more generalized methodology than the derivation of a single forward measurement, such as the computation of part area and perimeter from its runlengthcoding representation. In this situation, an interpretation procedure generally adopts the techniques and methodology of a pattern recognition approach. To achieve maximum utility and flexibility, the methods used should be sensitive to any image change in size, translation and rotation, and should provide good repeatability. The algorithm used in this article generally meets these conditions. The results show that recognition schemes based on these invariants are position, size and orientation independent, and also flexible enough to learn most sets of parts. Assuming that parts can vary only in location, orientation and size, then certain moments are very convenient for normalization. For instance, the first moments of area give the centroid of a part, which is a natural origin of coordinates for translation invariant measurements. Similarly, the eigenvectors of the matrix of second central moments define the directions of principal axes, which leads to rotation moment invariant measurements.

Journal

Sensor ReviewEmerald Publishing

Published: Apr 1, 1993

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