Corner-based feature extraction for object retrieval

This paper addresses the problems of object corner detection and the applications of corners to affine-similar object matching for object retrieval from a database. Two simple and efficient corner detection methods are presented. One is based on detecting sharp angles on a smoothed object boundary curve, and the other is based on a 2D rotationally symmetric bandpass filter applied onto an image. Two object matching approaches which exploit these corners are then investigated. In the first approach, affine objects are retrieved by matching their features derived from the corners. Dissimilarity measures for objects are formulated based on these features. In the second approach, affine objects are retrieved by matching object boundary curves modeled by a constrained active B-spline curve model. The model differs from the conventional B-spline by retaining significant object corners as a subset of B-spline knot points. This enables better correspondences of corners and easy selection of starting points for modeled curves, and hence a more accurate affine object matching. Various images and object boundary curves were used to verify the proposed methods, and the experimental results are convincing