Camera Self-calibration from Multi-view Images
Camera calibration is a key technology in computer field, in which self-calibration is to compute camera intrinsic parameters only from a series of images. The Kruppas equations method not only needs to compute the fundamental matrix which includes all of the geometry relation between images, but also needs to compute the epipoles which variate with the different images. Hartley deduced a simple form of Kruppas equations in terms of fundamental matrix. The goal of this paper is to convert the equations into the form of cost function according to the Hartleys deduction and calculate the cost function by the sum of fundamental matrixes multiplying the corresponding weighting factors that are related with the proportion of the number of matching features to the image pixels. In this paper, the genetic optimal algorithms are used to achieve the minimum value of the cost function. Our algorithm doesnt require computing the image epipole and avoiding the results instability, and is easy for the calculation. Experimental results show that the proposed algorithm is more effective and accurate, and can become a versatile tool for camera calibration, it will be used in our cameras array calibration.
Self-calibration Fundamental Matrix Genetic Algorithms
Qiuyan Guo Ping An Zhaoyang Zhang
School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China;Key Laboratory of Advanced Displays and System Application,Ministry of Education, Shanghai 200072, China
国际会议
上海
英文
1005-1010
2007-03-12（万方平台首次上网日期，不代表论文的发表时间）