文章基本信息

标题：アフィン変換と相関によるKanade-Lucas特徴点追跡法の特徴点対応付け精度の改善
本地全文：下载
作者：Joon Bo Shim ; Yoshinori Takeuchi ; Toshiharu Mukai 等
期刊名称：映像情報メディア学会誌
印刷版ISSN：1342-6907
电子版ISSN：1881-6908
出版年度：2002
卷号：56
期号：4
页码：647-652
DOI：10.3169/itej.56.647
出版社：The Institute of Image Information and Television Engineers
摘要：The Kanade-Lucas (KL) feature tracker is one of the better matching methods. However it fails to find corresponding points of two images when image motion is quite large. We propose the feature point matching method, making use of affine transformation and correlation so that the KL method works well for large image motion. First, the KL method is used for matching points. Some points are matched well, other points are mismatched or failed to be matched. Second, correlation of two matched points between the first and the second image obtained by the KL method is used to discriminate between well matched points and badly matched points. If the correlation coefficient is lower than a specified threshold, it is regarded as an outlier. Third, all the points to be matched in the first image are transformed on the second image by affine transformation of well matched points alone. However, all the transformed points from the first image to the second image are not well matched well due to the movement of the image. The role of affine transformation is to reduce the computation time and the number of mismatching points by designating only a small searching area around the transformed point. Finally, correlation is used to find the best matching point around the transformed point. Real image data has been used to test the proposed method, and excellent results have been obtained with an average error of 0.811 pixels.
关键词：Kanade-Lucas (KL) method;affine transformation;point correspondence;correlation