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Alternative TitleResearch on Forward-looking Infrared and Visible Images Matching Method
Thesis Advisor史泽林
Keyword图像匹配 红外图像 直线段提取 直线编组 隐直线度
Call NumberTP391.41/A19/2013
Degree Discipline模式识别与智能系统
Degree Name博士
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Other AbstractWith the infrared imaging technology widely used in military, remote sensing, medical and other fields, the problem of the infrared and visible images matching has been the focus of attention in recent years at home and abroad. The purpose of images matching is to find out to obtain two or more common area in the images under different imaging conditions and then to determine the geometry corresponding relationship between them. Image matching can be divided into same and different source image matching. Infrared and visible images matching belong to different source images matching. And the difficulties in different source images matching are image geometry differences and spectral differences. At the same time, the most stability between infrared and visible images is edge contour information. So this dissertation studies the characteristics of straight line segments extraction, extension, and its use in the images matching. Because of the infrared imaging system used in long-range detection is seriously affected by the transmission, the temperature difference of the scene is small and signal to noise ratio of image is very low. In this case, all the existing line segment detection algorithms are difficult to effectively detect the straight line features in the low SNR images. This paper defines the regional non-isotropic degrees. The linear area gradient phase consistency were justified by mathematics. Through getting each image point neighborhood gradient distribution for the whole original image gradient field, this paper proposes a new steady line segment detection algorithm. Comparative experiments show that this line segment detection algorithm can effectively suppress noise interference. According to the different source image gray-level information cannot be used as a matching and the information in a single straight segment cannot used in the practical problems, this article make line segments in local neighborhood into affine invariance group. Then a stable middle features will be got and character description of image matching method is presented. Experiments show that the algorithm can effectively realize matching problem of infrared and visible light in the wide baseline condition. Under the narrow baseline condition, different source images matching can be simplified into parameter optimization problems. Studies have found that the optimum value of the space mapping is not correct in the matching algorithm based on implicit similarity function Aiming at this problem, the connotation and characteristic of straight line matching processing has carried on the generalization in this paper. We proposed a novel called implicit straightness of similar measure as the objective function. It is equivalent to a benchmark figure line segment features and real-time image area of the image matching and further through the multi-resolution analysis, from coarse to fine with Powell algorithm for parameter optimization to get the correct space mapping relation. The experimental results show that the algorithm can effectively realize the infrared and visible light image automatic registration and has a lot improvement in real-time performance than the existing algorithms. Finally we points out that the difficulties in the automatic target recognition are similar target recognition. The existing feature extraction algorithm based on image gray appearance is not suitable for target recognition problem and the edge contour shape category characteristics of these structures are reliable. Target classes due to huge database make the model difficult to meet the real-time requirements. That needs more effective middle shape feature extraction method to provide category target fast index. Structural characteristics of the image shape feature extraction and middle marshaling is an important research direction.
Contribution Rank1
Document Type学位论文
Recommended Citation
GB/T 7714
艾锐. 前视线外与可见光图像匹配方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2013.
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