物件移除(Object Removal)廣泛用於照片的人像移除，或是相片、影片中
不需要的標籤移除，目的在使移除不需要的物件後沒有明顯的失真(Distortion)。
本篇論文提出一種接縫裁剪的物件移除演算法。首先我們建立填補和移除的分散
邊界，利用邊界找到對圖片影響最小的像素組成接縫去放大圖片以及移除物體。
最後再計算並調整移除物體後的新亮度，使得移除物體後的區域更符合人眼視覺。
實驗結果與先前的方法比較起來，我們的演算法可以將物體完整的移除並且不會
影響到圖片中其他物體的結構。

Object removal is widely used to remove strangers from photographic portraits,
or logos from images or videos, i.e., the process of removing target object(s) from
digital images. This article proposes a seam carving based method to remove a target
object. First, we build the distributed boundary for the removing and inserting stages
to guide the seams to be found. Then we find all unnoticeable inserting seams to
enlarge the image, and find removing seams one by one to remove the object. After all
damaged areas have been removed, we adjust the luminance in a visually reasonable
way. Compared to previous proposals, the proposed method has good visual quality
and provides different resolutions for different devices. Experimental results
demonstrated that the proposed approach can remove the target object(s) without
destroying other object edges and adjust luminance in a visually meaningful manner.

[1] S. Avidan and A. Shamir, “Seam carving for content-aware image resizing,” ACM
Transactions on Graphics (TOG), 2007, pp. 10.
[2] M. Bertalmio, G. Sapiro, V. Caselles and C. Ballester, “Image inpainting,” Annual
Conference on Computer graphics and interactive techniques, 2000, pp. 417–424.
[3] T. F. Chan and J. Shen, “Mathematical models for local non-texture inpainting,”
SIAM Journal of Application Mathematics, 2001, pp.1019–1043.
[4] T. F. Chan and J. Shen, “Non-texture inpainting by curvature-driven diffusions,”
Journal of Visual Communication and Image Representation, 2001, pp. 436–449.
[5] S. Li, H. Wang, “Image Inpainting Using Curvature-Driven Diffusions Based on PLaplace
Operator,” Fourth International Conference on Innovative Computing,
Information and Control, 2009, pp. 323–325.
[6] T. Barbu, “Nonlinear Anisotropic Diffusion-based Structural Inpainting Framework,”
International Conference on Advanced Technologies, Systems and Services in
Telecommunications (TELSIKS), 2017, pp. 207–210.
[7] X.H. Yang, B.L. Guo, “Fractional-order tensor regularisation for image inpainting,”
IET Image Processing, 2017, pp. 734–745.
[8] T. Barbu, “Hybrid Image Interpolation Technique based on Nonlinear Second and
Fourth-order Diffusions,” International Symposium on Signals, Circuits and Systems
(ISSCS), 2017.
[9] A. Criminisi, P. Perez, and K. Toyama. “Object removal by exemplar based
inpainting”. In IEEE Computer Society Conference on Computer Vision and Pattern
Recognition, 2003, pp. 721–728.
[10] Q.H. Fan, H.S. Liu, Z.H. Fu, X.F. Li “Exemplar-Based Image Inpainting Based on
47
Pixel Inhomogeneity Factor”. Proceedings of APSIPA Annual Summit and Conference,
2017, pp. 1164–1168.
[11] V. Krishnamoorthy and S. Mathi. “An Enhanced Method for Object Removal Using
Exemplar-based Image Inpainting”. International Conference on Computer
Communication and Informatics, 2017.
[12] B. Yu and Y.D. Ding. “Exemplar-based Image Inpainting via Fast Global Optimal
Searching”. Advanced Information Technology, Electronic and Automation Control
Conference (IAEAC), 2017, pp. 723–726.
[13] K.L. Hung, and S.C. Lai. “Exemplar-Based Video Inpainting Approach Using
Temporal Relationship of Consecutive Frames”. International Conference on Awareness
Science and Technology, 2017, pp. 373–378.
[14] D. J. Tuptewar, and A. Pinjarkar. “Robust Exemplar Based Image and Video
Inpainting For Object Removal and Region Filling”. International Conference on
Intelligent Computing and Control, 2017.
[15] S.Z. Siadati, F. Yaghmaee, P. Mahdavi. “A New Exemplar-Based Image Inpainting
Algorithm Using Image Structure Tensors”. Iranian Conference on Electrical
Engineering, 2016, pp. 995–1001.
[16] Y.-J. Lin, and C.H. Lin, “Fast Image Completion Algorithm Based on Local
Similarity”, 2016.