Share Email Print

Proceedings Paper

Recognition of containers using a multidimensional pattern classifier
Author(s): Michael Magee; Richard Weniger; Dennis J. Wenzel; Reza Pirasteh
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

A method for recognizing closed containers based on features extracted from their circular tops is presented. The approach developed consists of obtaining images from two spatially separated cameras that utilize both diffuse and specular light sources. The images thus obtained are used to segment target objects from the background and to extract representative features. The features utilized consist of container height as computed using stereopsis as well as the mean, variance, and second central moments of the intensities of the segmented caps. The recognition procedure is based on a minimum distance Mahalanobis classifier which takes feature covariance into account. The discussion that follows details the algorithmic approach for the entire system including image acquisition, object segmentation, feature extraction, and pattern classification. Result of test runs involving sets of several hundred training samples and untrained samples are presented.

Paper Details

Date Published: 1 November 1992
PDF: 13 pages
Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); doi: 10.1117/12.131514
Show Author Affiliations
Michael Magee, Univ. of Wyoming (United States)
Richard Weniger, Southwest Research Institute (United States)
Dennis J. Wenzel, Southwest Research Institute (United States)
Reza Pirasteh, Sky Chefs (United States)

Published in SPIE Proceedings Vol. 1825:
Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top