A Projectivity Diagnosis of Local Feature Using Template Matching

It is well known that points on a plane in 3D world are related to corresponding image points in a view of a moving camera by projective translation. Good image features have robust projectivity under any camera movements. In the standard performance evaluation of image processing, real captured images of a scene are used ordinarily. However, it is not enough to evaluate in detail because the variation of camera angle and distance to target objects are limited and the capturing cost is expensive. During the early stage of the image processing development, the basic performance measurement should be the most important in an easy way. We propose a projectivity diagnosis method to measure the performance of local descriptor base template matching between a template image and reference images which are created by deforming the template image. This template matching consists of a feature image point extraction and a local descriptor matching. The proposed method evaluates the positional accuracy of the extracted feature points and the matching with local descriptor. Four metrics are introduced to evaluate the projectivity of template matching. In the experiment, our proposed diagnosis method expose the projectivity of SIFT, SURF, and ORB. SIFT showed the better robustness than the others.