A Probabilistic Approach to Geometric Hashing Using Line Features
نویسنده
چکیده
One of the most important goals of computer vision research is object recognition. Most current object recognition algorithms assume reliable image segmentation, which in practice is often not available. This research exploits the combination of the Hough method with the geometric hashing technique for model-based object recognition in seriously degraded intensity images. We describe the analysis, design and implementation of a recognition system that can recognize, in a seriously degraded intensity image, multiple objects modeled by a collection of lines. We rst examine the factors a ecting line extraction by the Hough transform and proposed various techniques to cope with them. Line features are then used as primitive features from which we compute the geometric invariants used by the geometric hashing technique. Various geometric transformations, including rigid, similarity, a ne and projective transformations, are examined. We then derive the \spread" of computed invariant over the hash space caused by \perturbation" of the lines giving rise to this invariant. This is the rst of its kind for noise analysis on line features for geometric hashing. The result of the noise analysis is then used in a weighted voting scheme for the geometric hashing technique. We have implemented the system described and carried out a series of experiments on polygonal objects modeled by lines, assuming a ne approximations to perspective viewing transformations. Our experimental results show that the technique described is noise resistant and suitable in an environment containing many occlusions.
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عنوان ژورنال:
- Computer Vision and Image Understanding
دوره 63 شماره
صفحات -
تاریخ انتشار 1996