A new robust circular Gabor based object matching by using weighted Hausdorff distance

Abstract

This paper describes a new and efficient circular Gabor filter-based method for object matching by using a version of weighted modified Hausdorff distance. An improved Gabor odd filter-based edge detector is performed to get edge maps. A rotation invariant circular Gabor-based filter, which is different from conventional Gabor filter, is used to extract rotation invariant features. The Hausdorff distance (HD) has been shown an effective measure for determining the degree of resemblance between binary images. A version of weighted modified Hausdorff distance (WMHD) in the circular Gabor feature space is introduced to determine which position can be possible object model location, which we call ‘coarse’ location, and at the same time we get correspondence pairs of edge pixels for both object model and input test image. Then we introduce the geometric shape information derived from the above correspondence pairs of edge pixels to find the ‘fine’ location. The experimental results given in this paper show the proposed algorithm is robust to rotation, scale, occlusion, and noise etc.