About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
Computer Vision and Image Understanding
Paper
A probabilistic approach to geometric hashing using line features
Abstract
Most current object recognition algorithms assume reliable image segmentation, which in practice is often not available. We examine the combination of the Hough transform with a variation of geometric hashing as a technique for model-based object recognition in seriously degraded single intensity images. Prior work on the performance analysis of geometric hashing has focused on point features, which can be hard to detect in an environment affected by serious noise and occlusion. This paper uses line features to compute recognition invariants in a potentially more robust way. We investigate the statistical behavior of these line features analytically. Various viewing transformations, which 2-D (or flat 3-D) objects undergo during image formation, are considered. For the case of affine transformations, which are often suitable substitutes for more general perspective transformations, we show experimentally that the technique is noise resistant and can be used in highly occluded environments. © 1996 Academic Press, Inc.