基底影像重建技術之樣板比對及其影像校正之應用

樣板比對(Template matching)是指判斷兩個影像中是否存在相同的物體，其中一個為事先選定的樣板(Template)，另一個為所欲搜尋的目標物，傳統用於樣板比對之影像相減法(Image Difference)與相關係數法(Normalized Cross Correlation, NCC)在影像縱使只有小幅度的旋轉與尺寸之變化時，常造成比對錯誤的情形發生，因此本研究發展一個以基底影像(Basis images)為基礎的樣板比對方法，能夠克服小旋轉與些微的尺寸變化之影像圖樣。本研究所提出之基底影像比對方法是先由標準影像中選取數個樣板子影像，並將其組成一個基底影像陣列，而比對時則是將每一個待測的子影像利用基底影像之線性組合進行影像重建，測量兩比對影像之差異時分別計算兩項距離指標: 1)使用基底影像線性重組之係數值做為特徵向量，計算樣板的特徵向量與待測子影像之特徵向量的歐基里德距離; 2)計算待測子影像與其重建影像兩張影像之灰階差異。實驗結果顯示，基底影像比對方法對於旋轉角度的容忍能力在之間，而尺寸變化率之容忍度則在0.9至1.1之間(即之面積增減)。本研究所開發之樣板比對法應用在工業校正與保全機器人之移動物/入侵者偵測，在工業校正的應用中將每一個工件在組裝/檢測前對待測物進行影像定位與校正，減少因為工件位置的偏移所產生的組裝失敗或檢測錯誤; 此外在保全機器人的應用中將連續兩張影像校正到相同角度與位置，再以影像相減法將移動主體由背景中分割出來，能夠克服鏡頭晃動所造成的影像偏移變化，在影像尺寸160 120的實驗中每張影像計算時間為0.031秒，可達到即時偵測的效果。

關鍵字

樣板比對；基底影像；影像重建；影像校正；前景偵測

並列摘要

Template matching is a technique to find the instance of a template in an image. Template matching techniques are traditionally based on Image Difference and Normalized Cross Correlation. They are very sensitive to slight changes in rotation and scale. In this study, a basis image reconstruction method that can tolerate geometric changes is developed for template matching. To use the basis image reconstruction, a number of template patches with most representative patterns are first manually or automatically selected from a reference image. The templates then form the basis images. In the matching process, a windowed subimage of the template size is constructed by the linear combination of the basis images. Two measurement indicators are used to evaluate the similarity between two compared subimages. First, the coefficients of the linear combination of the basis images are used as the feature vector. The Euclidean distance between the feature vectors of the original template and the test subimage quantifies the similarity score. The second indicator is the gray level difference of the test subimage and its reconstruction. Experimental results show that the basis image reconstruction method is in rotation and 10% in scale changes. By including templates with larger rotational angles in the basis images, the proposed method can further identify objects with severe direction changes. The applications of the proposed template matching technique in this study are industrial calibration and the detection of moving objects (intruders) from a mobile robot. For industrial calibration, it can be applied in positioning and calibrating a workpiece so that the assembly failure or inspection error can be reduced. For security robots, the proposed technique can align two consecutive images in the video sequence due to the shaking of the camera. Then the simple image differencing can effectively and efficiently segment the moving objects from the background. In the experiment, an image of size 160 120 pixels needs only 0.031 seconds of processing time. The template matching technique proposed in this study can be used for real-time applications.

並列關鍵字

Template matching ； Basis image reconstruction ； Image registration ； Security robot.

參考文獻

Bartlett, M. S., J. R. Movellan and T. J. Sejnowski, 2002, “Face recognition by independent component analysis,’’ IEEE Transactions on Neural Networks, Vol. 13, pp. 1450-1464.

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基底影像重建技術之樣板比對及其影像校正之應用

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