眾所周知，特徵的萃取為影像處理的物件辨識之基本重要步驟，為了達到萃取此較強健之特徵點，軟體執行相關演算法所需之計算較難以達到即時的效果，有鑑於此，本論文將以FPGA平台實現SIFT演算法達到快速而且能夠獲得較強健之特徵點的計算及描述。本論文亦選定滅火器及垃圾桶為地標物，並事先將此特徵描述向量儲存於FPGA平台中，並設計地標物的搜索策略，接著以自走車上的FPGA平台進行相關地標物之搜索。並以線性搜索的匹配準則，自走車在可辨識之距離進行地標物的搜索、辯識及匹配比對，最後以相關的實驗驗證所發展的平台執行SIFT演算法和地標物搜索策略之有效性及強健性。

It is known that feature extraction is a fundamental part to recognize objects in an image processed. For the purpose of robustly extracting these features, the demanded computation using software approach is difficult to obtain on-line applications. In this thesis, an FPGA-based scale invariant feature transform (SIFT) is implemented to accelerate the recognition and description of these features. The extinguisher and garbage can are chosen as specific landmarks and they are also pre-trained to accomplish their corresponding feature vectors stored in the FPGA platform. The matching criterion using linear search is then constructed to evaluate the successful recognition rate of the specific landmarks. The strategy for the search of the chosen landmarks through a mobile robot is designed and evaluated by different conditions. Finally, the corresponding experiments are given to validate the effectiveness and robustness of the proposed methodology.

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