Title :
Zernike moment-based feature detectors
Author :
Ghosal, S. ; Mehrotra, R.
Author_Institution :
Center for Comput. Math., Colorado Univ., Denver, CO, USA
Abstract :
A novel model-based unified approach is proposed for generating a set of image feature maps (or primal sketches). For each type of feature, a parametric model is developed to characterize the local intensity function in an image. Projections of 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 the proposed feature detectors
Keywords :
edge detection; feature extraction; parameter estimation; polynomials; Zernike moment-based feature detectors; edge detection; experimental results; high-level vision tasks; image feature extraction; image feature maps; image intensity; intensity profile projections; local intensity function; model parameters estimation; model-based unified approach; orthogonal Zernike moment-generating polynomials; parametric model; parametric model-based techniques; primal sketches; range images; Computer vision; Data mining; Detectors; Face detection; Filters; Image analysis; Machine vision; Parametric statistics; Polynomials; Surface fitting;
Conference_Titel :
Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference
Conference_Location :
Austin, TX
Print_ISBN :
0-8186-6952-7
DOI :
10.1109/ICIP.1994.413246