Invariant feature matching in parameter space with application to line features

Abstract

This paper examines the combination of the Hough transform with geometric hashing as a technique for object recognition. Geometric hashing is a technique for fast indexing into object-model databases by creating multiple invariant indices from model features; Yet its description applies to objects that are modeled by point sets. Extracting points locally from image data is a noise sensitive process, and the analysis of geometric hashing on point sets shows that it is very sensitive to noise. The use of the Hough transform as a first layer for extracting features imposes constraints on the image data, and in domains in which the constraints are appropriate there is a significant reduction in noise effects on geometric hashing. The use of arbitrary primitive features in geometric hashing schemes also has other advantages. As a concrete example, we experiment with objects modeled by lines. The output of the line-Hough transform on intensity images is used to directly encode invariant geometric properties of shapes. Points in Hough space that have high counts are combined to yield invariant geometric indices. Objects containing lines are modeled as collection of points in dual space and invariant indices in dual space are found by computing invariant dual space transformations. The combination of the Hough transform and geometric hashing is shown by experiments to be noise resistant and suitable for cluttered environments.