Publications

Feature extraction transforms an object's image representation to an alternate reduced representation. In one-class object recognition, we would like this alternate representation to give improved discrimination between the object and all possible non-objects and improved generalization between different object poses. Feature selection can be time-consuming and difficult to optimize so we have investigated unsupervised neural networks for feature discovery. We first discuss an inherent limitation in competitive type neural networks for discovering features in gray level images. We then show how Sanger's Generalized Hebbian Algorithm (GHA) removes this limitation and describe a novel GHA application for learning object features that discriminate the object from clutter. Using a specific example, we show how these features are better at distinguishing the target object from other non-target objects with Carpenter's ART 2-A as the pattern classifier.

We have developed a video detection algorithm for measuring the residue left on a printed circuit board after a soldering process. Oblique lighting improves the contrast between the residue and the board substrate, but also introduces an illumination gradient. The algorithm uses the Boundary Contour System/Feature Contour System to produce an idealized clean board image by discounting the illuminant, detecting trace boundaries, and filling the trace and substrate regions. The algorithm then combines the original input image and ideal image using mathematical models of the normal and inverse Weber Law to enhance the residue on the traces and substrate. The paper includes results for a clean board and one with residue.