Recognizing one-DOF industrial tools using invariant moments

Authors:
J. -D. Lee
Affiliations:
Department of Electrical Engineering Chang Gung College of Medicine and Technology Tao-Yuan, Taiwan 333, R.O.C.
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
1996

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Abstract

In this paper, a simple but efficient approach is proposed to recognize one-DOF industrial tools. Since the shape is changed with the variation of the jaw angles and a feature vectorobtained by conventional approach is not unique, we use the invariant moments and the ratio of area to perimeter squared of a boundary image to construct the required feature vector for object recognition. Two statistical classifiers based on the nearest-neighbor rule and the minimum-mean-distance rule are then utilized to pattern recognition. Experimental results show the good performance of this method in the noisy environment, as well as noise-free environment are also included.