Using directional variance to extract curves in images, thus improving object recognition in clutter

Andrea Selinger and Randal C. Nelson
University of Rochester Department of Computer Science TR 712
April 1999

Abstract: In this report we describe a method for extracting curves from an image using directional pixel variances instead of gradient measures as low-level boundary evidence. The advantage of the variance over the image gradient is that we can accurately compute the direction of a local edge even if a sudden contrast change occurs in the background. This allows curves belonging to object contours to be followed more easily. We compared our method to a similar method based on the image gradient and we found that it obtains better results when run on synthetic and natural images. Our method also improved the performance of a contour-based 3D object recognition system in cluttered images.