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Invariant representation and hierarchical network for inspection of nuts from X‐ray images
Author(s) -
Sim A.,
Parvin B.,
Keagy P.
Publication year - 1996
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/(sici)1098-1098(199623)7:3<231::aid-ima11>3.0.co;2-1
Subject(s) - invariant (physics) , artificial intelligence , curvature , pattern recognition (psychology) , computer science , curse of dimensionality , mathematics , algorithm , topology (electrical circuits) , computer vision , geometry , combinatorics , mathematical physics
An X‐ray based system for the inspection of pistachio nuts and wheat kernels for internal insect infestation is presented. The novelty of this system is twofold. First, we construct an invariant representation of infested nuts from X‐ray images that is rich, robust, and compact. Insect infestation creates a tunnel, in the X‐ray image, with reduced density of the natural material. The tunneling effect is encoded by linking troughs on the image and constructing a joint curvature‐proximity distribution table for each nut. The latter step is designed to accentuate separation of those tunneling effects that are due to the natural structure of the nut. Second, since the representation is sparse, we partition the joint distribution table into several regions, where each region is used independently to train a backpropagation (BP) network. The outputs of these subnets are then collectively trained with another BP network. We show that the resulting hierarchical network has the advantage of reduced dimensionality while maintaining a performance similar to the standard BP network. © 1996 John Wiley & Sons, Inc.