关键词: 多结构数据, 参数拟合, 鲁棒回归, 免疫原理, 启发式算法

Abstract: In computer vision domain, multi-structure model fitting is a common task. In convention algorithm, the input data set is first classified into several categories, and then the parameters of models are obtained by regression analysis of these categories of the data set. However, because the models are unknown, how to classify the input data set is still a tough task. In order to tackle this problem, mimicking the principle of antigen recognition and antibody generation of immune system, a heuristic robust multi-structure fitting algorithm is proposed in this paper. According to the algorithm, the process of data set classification is designed as an approximation process through correction step by step: firstly rough classifying the data set according to a coarse model through randomly sampling, then fitting the dominant data in each categories by convention robust regression algorithm for single model fitting, and then classifying the input data set again with the new model set to improve the classification. Through repeating the process of “classifying+fitting+classifying again+fitting again”, it approaches the precise classification and model set simultaneously. Simulations are conducted. The results show the algorithm has strong data classification and multi-structure model fitting capability with high computing efficiency.

Key words:  multi-structure data, parameter fitting, robust regression, immune principle, heuristic algorithm