Chord-tangent transformation for object recognition

The problem of recognizing planar images in two-dimensional (2D) space has remained a problem of significant interest in computer vision for the last three decades. The Generalized Hough Transform has emerged as one of the more promising techniques because of its robustness to incomplete data and additive noise. However, the Generalized Hough transform is not well suited for similarity transformation because the parameters of scale and rotation cannot be solved using unary tangent information. In this paper, a new technique is introduced which uses a simple transformation of pairwise tangent information to allow for the direct computation of the parameters of scale and rotation and thus a more precise estimate of the translation parameters. This method shares many of the same advantages of the Generalized Hough Transform, while performing with greater efficiency and accuracy. This technique is applied to a database of objects, where the test object is a composite of model instances, having undergone similarity transformation, and in the presence of both noise and occlusion. The results are compared with that of the Generalized Hough Transform, and a critical analysis of the two methods is presented.