A Global Feature Descriptor for Locally Similar Images

• Main Authors: Chi-Chun Pai, 栢啟鈞
• Other Authors: Bal Wang
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/37394896255759212107

碩士 === 淡江大學 === 資訊工程學系碩士在職專班 === 104 === In image processing, there are many basic research topics, for example, image recognition, face detection, image stich, 3D reconstruction, image matching applications, there is a same basic method: To extract feature points from image, through match to reach the goal of location. The definition and search from feature points, although basic, it is not simple but influence deeply; To correctly extract same position but difference angle from beginning, next steps will be work half the things times; On the contrary, if exist mismatch, subsequent processing great trouble is thrown. The topic of research discussion from the thesis is extract interest feature point according to every pixels neighbor area from image, then describes it, become local feature descriptors. Besides of this, adding global context information, as global feature descriptors, utilized them to match between images, increase the precise rate of matching, rather than mismatch. In the thesis, depends on “Scale Invariant Feature Transform”(SIFT), studying the algorithm of SIT, Scaling and Gaussian Blurring image to produce the Gaussian pyramid, then extract keypoint, increase the ability for noise, and stability for matching; Then to assign orientation for keypoint, let feature descriptor to be invariant to image rotation; Finally, adding the global descriptor information, so increase the matching ability for the partially similar image. Base on using the OpenSIFT library, which were written by Rob Hess, implement Global Context Feature Descriptor, to take many highly partially similar image, experiment the result, to compare with the result experiment by OpenSIFT, to exam if the method can improve the correct matching and speed. The thesis major in researching improved the incorrect matching rate between highly similar image; To utilize the combination of Global Feature Descriptors global information and local feature descriptors to decrease incorrect matching. For example, checkerboard has many similar areas, to verify if the matching rate improved then OpenSIFT is we are compared.