使用廣義霍夫轉換與尺度不變特徵轉換實現平面物件偵測之應用

近年來尺度不變特徵轉換演算法(Scale-Invariant Feature Transform, SIFT)已廣泛使用在相當多的應用系統中，如物件偵測與辨識系統、保全監控系統、工廠自動檢測系統、視訊索引系統等。固然SIFT 特徵點對影像尺度、旋轉、輕微透視及光線明亮等條件上，具有相當大的不變性與優越性，但在特徵點匹配上會有一定比例的匹配錯誤。尤其是當場景遇到測試物件的紋理、特徵與其它前景物件有相似或雷同的情況下， SIFT 在物件偵測上的錯誤比例會更高。為了改善匹配錯誤的問題，採用最近相鄰匹配法(Nearest Neighbor, NN) 、霍夫轉換(Hough Transform, HT)、隨機抽樣一致法(RANdom SAmple Consensus, RANSAC)等演算法可以刪除一些錯誤的匹配。但實驗結果證實霍夫轉換投票的方式僅可以改善一些錯誤的匹配，仍然無法克服當場景具有多個相同特徵、紋理的物件辨識。為了改善這個問題，本文提出一種方法，採用廣義霍夫轉換(Generalized Hough Transform, GHT)演算法結合SIFT來實現一個未知大小、旋轉及形狀的物件辨識。經由實驗結果證實，本文所提出之方法，於物件偵測的實驗中除了提升精確度外效率上可以節省一倍以上的時間且在相關的實驗中均具有相當大的穩定性。

關鍵字

物件偵測；尺度不變特徵轉換；最近相鄰匹配法；霍夫轉換；廣義霍夫轉換

並列摘要

We have seen wide range of applications, such as object detection and recognition systems, security monitoring systems, factory automation and detection systems, and video indexing systems on scale-invariant feature transform (SIFT) algorithm in recent years. Without a doubt, SIFT feature points present significant invariance and superiority with conditions such as scaling, rotation, slight perspective, and illumination changes in images. However, a certain degree of error is to be expected in feature point matching. SIFT is particularly less reliable in object detection when the textures or features of the test object are similar to or the same as those of other foreground objects. To address these errors in matching, researchers have proposed methods involving the Nearest Neighbor (NN), the Hough transform (HT), and RANSAC. However, experiments demonstrate that the voting method of the Hough transform can only slightly reduce errors and fails to overcome the problems caused by multiple objects having the same features or textures. These are combined with a model of reference points and edge points established with GHT. This allows for the detection of objects with unknown rotation changes, scale ratios, and irregular shapes. Our results prove that the proposed method improves the precision of object detection in experiments, and saves over 50% in computation time than the original method. In addition, the method achieves good stability in relevant experiments.

並列關鍵字

Object Detection ； SIFT ； Nearest Neighbor ； Hough Transform ； Generalized Hough Transform

參考文獻

1.D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” IJCV, 60(2):91–110, 2004

2.S. Arya and D.M. Mount, “Approximate nearest neighbor queries in fixed dimensions,” In Fourth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’93), pp. 271-280, 1993.

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Karlsruhe, Germany, pp. 1019-1022

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使用廣義霍夫轉換與尺度不變特徵轉換實現平面物件偵測之應用

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