結合邊緣裁切與圖縫裁減暨縮放之影像畫面調整技術

本研究提出結合多種運算子(multi-operator)的影像重新定位(image retargeting)演算法，數位影像被循序且適當地施予邊緣裁切(cropping)、圖縫裁減(seam carving)與縮放(scaling)等三種方式，達到目標解析度。首先，我們計算影像中的視覺顯著特徵 (visual saliency feature)以作為畫面調整的依據。在邊緣裁切中，擁有較大連通數的前景物體將被擷取，並以其邊緣訂出裁切邊界。圖縫裁減則利用動態規劃方法(dynamic programming)刪除最小能量圖縫，並利用限制刪除圖縫後所產生的局部能量大小決定圖縫裁減停止點。對於某些適合的影像，我們以增加圖縫的方式讓影像長寬比例進一步接近目標長寬比。最後，畫面將直接被縮放至目標大小以在適當的顯示器上呈現影像內容。實驗結果顯示，經由上述簡易的判斷與操作，我們確實能夠維持影像主體，避免在不同長寬比例的影像大小改變下產生嚴重失真，處理後的影像與其他使用較複雜方式所獲得的影像相差無幾，本演算法因而具有較高的實用性。

關鍵字

多種運算子；影像重新定位；邊緣裁切；圖縫裁減；視覺顯著特徵

並列摘要

A new multi-operator image retargeting approach is proposed in this research. Cropping, seam carving and scaling are applied sequentially on the image to acquire the image with the targeted resolution. The saliency map of the image is first computed to serve as the reference for the subsequent processing. The foreground objects that occupy larger areas will be extracted and the boundaries of objects will be used to determine the edges for cropping. Then, seam carving is applied to remove insignificant content by employing the dynamic programming. The local energy decides when the seam carving process should be stopped. For certain appropriate images, the seams are increased so that the resulting aspect ratio can be approaching the targeted one. Finally, the image is simply scaled to the resolution of the display. The experimental results demonstrate that the essential part the image can be maintained to avoid the serious distortion from the resolution changes. Compared with the images obtained by adopting more complicated methodologies, the image of our scheme is not inferior so the efficiency can be achieved.

並列關鍵字

Multi-Operator ； Image Retargeting ； Cropping ； Seam Carving ； Visual Saliency Feature

參考文獻

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Vision Computing, vol. 28, no. 3, pp. 391-402, 2010.

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[6] F. Stentiford, "Attention based auto image cropping," The 5th International Conference on Computer Vision Systems, Bielefeld, 2007.

國際替代計量

結合邊緣裁切與圖縫裁減暨縮放之影像畫面調整技術

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