| Abstract: | 影像處理技術中，有許多基礎的研究議題存在，舉凡圖形辨識、人臉辨識、影像拼接、3D建模、圖形比對等應用，皆有一共同的基礎方法：從圖形中找出某些特徵點(feature points)，再經過比對(matching)達到定位的目的。特徵點的定義與找尋，雖然基礎但並不簡單且影響深遠。一開始能將許多不同角度獲取的圖形中相同位置正確無誤的對應出來，接下來的步驟將會是工半而事倍；反之如果存在錯誤的對應，則會引後續處理極大的麻煩。本篇論文所要研討的議題即為從圖形中提取依據各像素(pixels)的鄰近區域的特徵點，再對特徵點作描述，成為區域性特徵描述器，另外加入圖像大範圍資訊，成為廣域特徵描述器，並在不同部份對稱影像間比對，提高圖形比對(match)的準確率，而不會誤判。
在此篇論文中，將以 Scale Invariant Feature Transform (SIFT) 為基礎，研究 SIFT 的演算法，對原圖片縮放和高斯模糊產生高斯金字塔差，並提取其中的關鍵點，並提高抗噪訊能力，以及增加比對的穩定性；再分配關鍵點基準方向，使得特徵描述器具有旋轉不變性；最後再增加廣域描述器資訊，使得部份相似圖形中，可以增加比對準確性。
在此將使用Open Source的Library OpenSIFT為基礎，實作 Global Context Feature Descriptor，再取相似性高的各種不同圖片，套用實作的執行結果，並與原始的OpenSIFT作比較，以驗證所實作出的方法是可改善相似性高的影像比對正確率和速度。
本論文主要研究關鍵在於SIFT在相似性高的圖片中，利用本地特徵描述器與廣域特徵描述器的廣域資訊作結合，來減少圖像比對錯誤的錯誤比率，以及使用SIFT加GC和Sample Keypoints來加速比對速度。例如棋盤中有許多相似區域，使用棋盤來比較驗証實作比對的結果與OpenSIFT比對的結果。
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. |
