1 Introduction
With image processing, pattern recognition, and computer/machine vision, dimensional model (curve and surface) fitting to a set of given data points is a very common task carried out during a working project, e.g., edge detection, information extraction from 2D-image or 3D-range image, and object reconstruction. For the purpose of dimensional model fitting, we can consider three methods, namely, moment method [15], [32], [35], [42], Hough transform [8], [19], [28], and least-squares method (LSM) [23]. The moment method and Hough transform are efficient for fitting of relatively simple models, while their application to a complex object model or to an object model with a large number of model parameters is not encouraged. In this paper, we consider the LS-fitting algorithms for implicit model features. By data modeling and analysis in various disciplines of science and engineering, implicit features are very often used because of their compact description in form of and because of the possibility of a simple on-off and inside-outside decision.