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Learning object recognition models from images

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2 Author(s)
A. R. Pope ; Dept. of Comput. Sci., British Columbia Univ., Vancouver, BC, Canada ; D. G. Lowe

To recognize an object in an image an internal model is required to indicate how that object may appear. The authors show how to learn such a model from a series of training images depicting a class of objects, producing a model that represents a probability distribution over the variation in object appearance. Features identified in an image through perceptual organization are represented by a graph whose nodes include feature labels and numeric measurements. A learning procedure generalizes multiple image graphs to form a model graph in which the numeric measurements are characterized by probability distributions. A matching procedure, using a similarity metric based on a non-parametric probability density estimator, compares model and image graphs to identify an instance of a modeled object in an image. Experimental results are presented from a system constructed to test this approach. The system learns to recognize partially occluded 2-D objects in 2-D images using shape cues. It can recognize objects as similar in general appearance while distinguishing them by their detailed features

Published in:

Computer Vision, 1993. Proceedings., Fourth International Conference on

Date of Conference:

11-14 May 1993