基础矩阵估计的加权归一化线性算法  

A Weighted Normalization Algorithm for Estimation of Fundamental Matrix

作　　者：陈泽志[1] 吴成柯[1] 刘勇[1] 

机构地区：[1]西安电子科技大学综合业务网理论及关键技术国家重点实验室,陕西西安710071

出　　处：《软件学报》2001年第3期420-426,共7页Journal of Software

基　　金：国家自然科学基金资助项目!(6 9972 0 39);中法先进研究计划资助项目! (PRASI 0 0 - 0 4)&&

摘　　要：基础矩阵是对来自同一景物的两幅未定标图像进行分析的基本工具 .对其进行估计的常用线性算法有八点算法和改进的八点算法 ,其最大的优点是运算简单、易于实现 ,但对噪声和错误数据较敏感 ,因此实用性差 .通过引入与余差有关的代价函数 ,给出了一种新的鲁棒性线性算法——加权归一化算法 .首先将原始输入数据加权归一化处理 ,然后再用八点算法求 F阵的 8个参数 ,实现了 F阵的估计 .大量的模拟数据和真实图像的实验结果表明 ,此算法不仅具有良好的鲁棒性 ,而且可提高基础矩阵的估计精度 .The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras. The 8 point algorithm and the improved 8 point algorithm are widely used linear methods for estimating the fundamental matrix. They have advantages of simplicity in implementation. But they are extremely sensitive to noise and outliers. Hence in most cases, they are useless virtually. A new robust linear method——weighted normalization algorithm is developed by introducing a cost function related to residual errors. Firstly, the matching points with a weight factor are normalized. Secondly, the eight parameters of fundamental matrix are calculated by using the 8 point algorithm. Experiments on simulated and real image data are conducted. The results show that this algorithm is very robust to noises and outliers, and the fundamental matrix with high accuracy can be found.

关 键 词：鲁棒性 加权归一化算法 基础矩阵估计 图像分析 计算机视觉 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]