1 November 1992 Recognition of containers using a multidimensional pattern classifier
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Proceedings Volume 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision; (1992); doi: 10.1117/12.131514
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States
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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Magee, Richard Weniger, Dennis J. Wenzel, Reza Pirasteh, "Recognition of containers using a multidimensional pattern classifier", Proc. SPIE 1825, Intelligent Robots and Computer Vision XI: Algorithms, Techniques, and Active Vision, (1 November 1992); doi: 10.1117/12.131514; https://doi.org/10.1117/12.131514
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KEYWORDS
Image segmentation

Machine vision

Computer vision technology

Robot vision

Robots

Cameras

Feature extraction

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