A moment-based unified approach to image feature detection.

Abstract

In this paper, a novel model-based approach is proposed for generating a set of image feature maps (or primal sketches). For each type of feature, a piecewise smooth parametric model is developed to characterize the local intensity function in an image. Projections of the intensity profile onto a set of orthogonal Zernike-moment-generating polynomials are used to estimate model-parameters and, in turn, generate the desired feature map. A small set of moment-based detectors is identified that can extract various kinds of primal sketches from intensity as well as range images. One main advantage of using parametric model-based techniques is that it is possible to extract complete information (i.e., model parameters) about the underlying image feature, which is desirable in many high-level vision tasks. Experimental results are included to demonstrate the effectiveness of proposed feature detectors.