DEPARTMENT OF COMPUTER SCIENCE
DOCTORAL DISSERTATION DEFENSE


Candidate: Frank C. D. Tsai
Advisor: Jacob T. Schwartz

A Probabilistic Approach to
Geometric Hashing using Line Features

9:00 a.m., Thursday, July 22, 1993
12th floor conference room, 719 Broadway




Abstract

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 first examine the factors affecting 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, affine 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 first 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 affine approximations to perspective viewing transformations. Our experimental results show that the technique described is noise resistant and suitable in an environment containing many occlusions.