Extraction of surface orientation from texture using the gray level difference statistics

Abstract

The problem of extracting orientation of an object surface from a monocular image is one of the important tasks in computer vision. Most of the existing methods for extracting surface orientation are ones using the structural features of texture such as texel and edge. However, to represent texture features statistically is shown to be effective also in texture discrimination and segmentation. Thus, in this paper we propose a method for extracting surface orientation using the statistical feature of a texture image. First, we assume uniformity of a probability density function of difference statistic on object surface; then using the fact that the difference statistics depend on the geometric factor of length and orientation, we formulate the relationship between distortion of a density function in an image caused by perspective projection and the object surface. Then we derive an algorithm for finding the object surface orientation by search based on this formulation. In addition we apply this method to simulation images and real images to show its effectiveness. This enables us to extract object surface directly from a gray level image without extracting the texel or edge (whose extraction is required in the existing methods).