| Summary: | 碩士 === 國立中山大學 === 資訊工程學系研究所 === 102 === The description of local features of images has been successfully applied to many areas, including wide baseline matching, object recognition, texture recognition, image retrieval, robot localization, video data mining, etc. However, pure software implementations usually cannot achieve the requirement of real-time processing. In this thesis, we present software acceleration of general-purpose computing on graphics processing units (GPGPU) for two popular image feature extraction/description algorithms, Shift-Invariant Feature Transform (SIFT) and Speeded-Up Robust Feature (SURF). Furthermore, several versions of hardware SURF accelerators are also implemented. The four major parts of SIFT are scale-space extrema detection, keypoint localization, orientation assignment, and keypoint description where scale-space extrema detection and keypoint description, the most critical parts, take most of the total execution time. SURF is composed of four major steps: integral image calculation, fast Hessian detection, orientation assignment, and keypoint description. In terms of software implementation, the computation complexity of SURF is significantly reduced compared with that of SIFT. However, hardware acceleration of SURF is still required for real time processing requirement. In this thesis, we slightly modify the original SURF algorithms in order to significantly reduce the hardware complexity for the implementations of fast Hessian detection and keypoint description without sacrificing too much in speed performance. Experimental results of both software and hardware acceleration are also given and compared.
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