隨著科技的發展顯示器的使用越來越普遍，並且可以和各種設備做緊密結合，例如手機，多媒體播放器等，因此配合不同大小螢幕的影像縮放技術越來越受重視，傳統的剪裁和非等比例縮放容易造成影像的變形或失真。Avidan and Shamir提出了一個影像細縫裁減(seam carving)，其依據影像內容來對影像重新調整大小的方法，被認為是一個有效的解決方法，使用簡單的濾波器來找出影像中高能量的區域並保留下來，但是很多時候這種演算法並不能產生令人滿意地結果，它無法應付各種類型的影像，例如有複雜背景或是高反差顏色的影像。本篇論文希望能夠對seam carving 演算法作改進，透過改變能量圖的計算方式，來降低複雜背景的高頻雜訊，使得在各種複雜背景的影像都能夠達到使用者期望的結果。

As displays become commonly used and are incorporated into more and more devices, such as mobile phones, multimedia players, there has been an increased focus on image resizing techniques to fill an image to an arbitrary screen size. Traditional methods such as cropping or scaling might introduce undesirable losses in information or distortion in perception. Recently, content-aware image retargeting method proposed by Avidan and Shamir called “Seam Carving” has good results. In particular, seam carving has gained attention as an effective solution, using simple filters to detect and preserve the high-energy areas of an image. But in some cases this algorithm may not provide satisfactory results, especially, for images having complex background or high-contrast colors. In this study, we would like to improve the existing seam carving methods by selecting more appropriate seams to remove so that it can adapt a variety of environment to provide desired results for users.

目錄
圖目錄	IV
表目錄	V
第一章 緒論	1
第二章 研究方法	4
2.1 能量圖的定義與求法	4
2.1-1 中間值濾波	4
2.1-2 邊緣偵測	5
2.1-3 利用分群法調整能量	6
2.2 尋找最佳細縫	8
2.3 結合scaling與seam carving	9
2.4 系統介面介紹	10
第四章 實驗結果與分析	13
第五章 結論與未來展望	19
參考文獻	20
附錄：英文論文	22

圖目錄
圖 一 比例縮放與裁切結果比較 (a) 原圖(b) 比例縮放(c) 裁切	1
圖 二 中間值濾波運算 (a) 原圖I (b) 中間值濾波結果I’	5
圖 三 Sobel運算後之邊緣圖G	6
圖 四 調整後的邊緣圖G’	7
圖 五 邊緣修補 (a) 對邊緣圖G做Hough transform之結果(b) 能量圖E	8
圖 六 forward energy考慮路徑移除後原本兩個不相鄰的像素合併後的梯度差	9
圖 七 系統介面 (a) 系統介面首頁(b) 選擇圖片	11
圖 八 系統結果顯示 (a) 四種模組產生之能量圖(b) 四種方法所移除之細縫	12
圖 九 影像縮小之結果 (a) 原圖 (b)~(e) Avidan and Shamir、Rubinstein、Han、與本文所提之模組一(α=4)	14
圖 十 影像縮小之結果 (a) 原圖 (b)~(e) Avidan and Shamir、Rubinstein、Han、與本文所提之模組二(α=4)	15
圖 十一 影像縮小之結果 (a) 原圖 (b)~(e) Avidan and Shamir、Rubinstein、Han、與本文所提之模組三(α=4, switch point=80%)	16
圖 十二 影像縮小之結果 (a) 原圖 (b)~(e) Avidan and Shamir、Rubinstein、Han、與本文所提之模組四(α=4, switch point=80%)	17
圖 十三 利用標記之結果 (a) 原圖中紅色方框與黑色方框分別代表欲保留區塊與欲移除區塊(b) 無標記之縮小結果(c) 標記欲保留區塊之縮小結果(d) 標記欲保留區塊及欲移除區塊之縮小結果	18

表目錄
表 一 本文提出之四種模組	13

[1]	S. Avidan and A. Shamir, “Seam Carving for Content-Aware Image Resizing,” ACM Trans. Graph., Vol. 26, Issue 3, Article 10, pp. 10:1-10:9, July 2007.
[2]	M. Rubinstein, A. Shamir, and S. Avidan, “Improved Seam Carving for Video Retargeting,” ACM Trans. Graph., Vol. 27, Article 16, pp. 16:1-16:9, Aug. 2008.
[3]	M. Frankovich and A. Wong, “Enhanced Seam Carving Via Integration of Energy Gradient Functionals,” IEEE Signal Processing Society, pp. 375-378, 2011.
[4]	W. M. Dong, N. Zhou, J. C. Paul, and X. P. Zhang, “Optimized Image Resizing Using Seam Carving and Scaling,” ACM Trans. Graph., Vol. 28, Issue 5, Article 125, pp. 125:1-125:10, 2009.
[5]	J. W. Han, K. S. Choi, T. S. Wang, S. H. Cheon, and S. J. Ko, “Improved Seam Carving Using a Modified Energy Function Based on Wavelet Decomposition,” in Proceedings of the 13th IEEE International Symposium on Consumer Electronics, pp. 38–41, 2009.
[6] Daw-Sen Hwang and Shao-Yi Chien. “Content-Aware Image Resizing Using Perceptual Seam Carving With Human Attention Model,” in Proceedings of the International Conference on Multimedia and Expo, pp. 1029-1032, 2008.
[7]	S. Cho, H. Choi, Y. Matsushita, and S. Lee, “Image Retargeting Using Importance Diffusion,” in Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 977-980, 2009.
[8]	Kazu Mishiba and Masaaki Ikehara, “Seam Merging for Image Resizing with Structure Preservation,” in Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 1001–1004, 2011.
[9]	S. Avidan and A. Shamir, “Seam Carving for Media Retargeting,” Communications of the ACM, Vol. 52, Issue 1, pp. 77-85, 2009.
[10]	L. Chen, X. Xie, X. Fan, W. Ma, H. Zhang, and H. Zhou, “A Visual Attention Model for Adapting Images on Small Displays,” ACM Multimedia Systems Journal, Vol. 9, No. 4, pp. 353–364, 2003.
[11]	Y. S. Wang, C. L. Tai, O. Sorkine, and T. Y. Lee, “Optimized Scale-and-Stretch for Image Resizing,” in Proceedings of the International Conference on Computer Graphics and Interactive Techniques, ACM, Vol. 27, No.  5, Article 118, pp. 118:1-118:8, 2008.
[12] D. Domingues, A. Alahi, and P. Vandergheynst, “Stream Carving: an Adaptive Seam Carving Algorithm,” in Proceedings of the IEEE International Conference on Image Processing, pp. 901-904, 2010.
[13]	Jinseok Lee and Daijin Kim, “Fast Seam Carving Using Partial Update and Divide and Conquer Method,” in Proceedings of the IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 107-112, 2009.
[14]	G. Pan, W. Li, W. Bai, J. Chen and L Li, “Reverse Seam Carving,” in Proceedings of the 2011 Sixth International Conference on Image and Graphics, pp 50-55.
[15]	H. M. Nam, K. Y. Byun, J. Y. Jeong, K.S. Choi, and S.J. Ko, “Low Complexity Content-Aware Video Retargeting  for Mobile Devices,” IEEE Transactions on Consumer Electronics, pp. 182-189, 2010.