臺灣博碩士論文加值系統

如何有效地擷取影像中的特徵點，適當的描述特徵點並且正確的配對是許多電腦視覺應用的基礎，本論文著重在特徵點描述的研究上。因為二元特徵點描述子(binary descriptor)相較於傳統的浮點數特徵點描述子(float point descriptor) (ex. SIFT) 有更低的計算複雜度以及更少的空間花費，這些特性使得二元特徵點描述子在電腦視覺相關領域越來越熱門。
近年來，大部分的二元特徵點描述子只參考影像中的灰階資訊進行比對，但是忽略了色彩資訊的重要性，因此本論文的主要貢獻在於，透過模擬人眼視網膜上的感光細胞分布，設計了一個能有效利用色彩資訊的二元特徵點描述子，相較於傳統僅使用灰階資訊的二元特徵點描述子，我們的方法能在影像上面擷取更多的資訊，藉此在影像配對上達到更精準的表現。除此之外，根據實驗結果，本論文提出的方法不僅維持著良好的準確率，而且跟熱門的二元特徵點描述子比較起來有著更快的配對速度，以及只需要花費不到一半的記憶體空間。

Feature matching between images is key to many computer vision applications. Effective Feature matching requires effective feature description. Recently, binary descriptors which are used to describe feature points are attracting increasing attention for their low computational complexity and small memory requirement. However, most binary descriptors are based on intensity comparisons of grayscale images and did not consider color information. In this paper, a novel binary descriptor inspired by human retina is proposed, which considers not only gray values of pixels but also color information. Experimental results show that the proposed feature descriptor spends fewer storage spaces while having better precision level than other popular binary descriptors. Besides, the proposed feature descriptor has the fastest matching speed among all the descriptors under comparison, which makes it suitable for real-time applications.

Chapter 1 Introduction 1

Chapter 2 Related Work 4

Chapter 3 The CREAK Descriptor 8
3.1 Motivation 8
3.2 Sampling pattern 10
3.3 Orientation pair 12
3.4 Building the Descriptor 15

Chapter 4 Experimental Results 18
4.1 Color Space Selection 19
4.2 Comparsion with different Orientation pairs 23
4.3 Comparsion with different Binary Descriptors 29
4.4 Comparsion with different Floating-point based Descriptor 33
4.5 Extremely matching case example 39
4.6 Other comparsion results 41

Chapter 5 Conclusion 70

Chapter 6 Reference 71

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