臺灣博碩士論文加值系統

影像編輯是一個專業性的工作，具備影像相關知識的人才能做好這個工作。一般不具備影像專業知識的人僅能依靠特殊軟體才可能編輯好影像，就像傻瓜相機相對於不具備攝影知識的人一樣。我們的研究目的即是提供一套適合不具備影像專業知識的人使用的影像編輯軟體。本論文的研究內容是這個研究系列的第一步驟。
本系統中的基本功能有：亮度、對比、及色彩飽和度的調整。我們將實作出幾種方法比較，找出最符合人性操作的方式。而本論文最主要研究在於互動式與半自動化參數輪廓的擷取。在人性化編輯系統中選取工具提供兩種選擇，一是互動式選取，我們利用Catmull-Rom spline 實作出一選取工具，使用者可以在想要選取的物體外圍點下適當的點，然後系統會在這些點中內插適當的點而形成一封閉曲線，如果不準確，我們可以動態改變點的位置或額外加點內插以提高輪廓的準確性。二是利用主動輪廓模式 (active contour model) 做半自動的輪廓選取。主動輪廓模式是利用能量公式，將圈出來的大概輪廓自動逼近到準確的位置及形狀。由於主動輪廓模式的複雜度相當高，本研究的重點在於改進其執行速度與效果。

Image edition is a professional work; only people who have the related knowledge of images can do it well and the edited results are dependent on the levels of their related knowledge. Anyone can edit images with image edit software; however, the edited results heavily depend on his/her related knowledge. Anyone who has no photography knowledge can take well pictures using an automatic camera; so we think that there is an image edit software making people who have no image knowledge to edit images well.
In our study, we try to provide an image editor such that people who have no image knowledge can edit images well by using the editor. The work of this thesis is just the first step of this studying. This work has two major topics: one is the basic image editing operators and the other is the contour extraction. In the basic editing operations, several different-concept tuning operators on brightness, contrast, and saturation are proposed and compared.
Contour extraction is our main task. Two contour extraction algorithms are developed: one is the interactive contour extraction and the other is the semi-automatic parametric contour extraction. Catmull-Rom splines are used for interactive parametric contour extraction. The control points for the splines can be added and moved to approach the real contour shape. Semi-automatic parametric contour extraction is based on the active contour models. An adaptive active contour model improved from the fast greedy algorithm is proposed. Experiments show that the proposed method can extract object boundary contour more accurate and efficient.

摘 要 i
誌 謝 II
目 錄 III
第一章 緒論 一
第二章 相關研究 二
第三章 人性化編輯系統的基本功能 三
第四章 互動式參數輪廓擷取 四
第五章 半自動化參數輪廓選取 五
第六章 實驗 六
第七章 結論 七
英文版論文 八

[1]Bimbo, A. D. and P. Pala, “Visual image retrieval by elastic matching of user sketches,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.19, no.2, pp.121-132, 1997.
[2]Canny, J., Finding Edges and Lines in Images, Master Thesis, MIT Artificial Intelligence Laboratory, 1983.
[3]Canny, J. F., “A computational approach to edge detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.8, no.6, pp.697-698, 1986.
[4]Carpenter, K. H., B-spline, Technical Report, Electrical and Computer Engineering Department, Kansas State University, Nov. 28, 2001.
[5]Catmull, E. and R. Rom, “A class of local interpolating splines,” in Computer Aided Geometric Design, R. E. Barnhill and R. F. Reisenfeld, Eds., Academic Press, New York, pp.317-326, 1974.
[6]Choi, W., K. Lam, and W. Siu, “An adaptive active contour model for highly irregular boundaries,” Pattern Recognition, vol.34, pp.323-331, 2001.
[7]Cohen, L. D., “On active contour models and balloons,” CVGIP: Image Understanding, vol.53, no.2, pp.211-218, 1991.
[8]Cohen, L. D. and I. Cohen, “Finite-element methods for active contour models and balloons for 2-D and 3-D images,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.15, no.11, pp.1131-1147, 1993.
[9]Deriche, R., “Using canny’s criteria to derive a recursively implemented optimal edge detector,” Int’l J. Computer Vision, vol.1, no.2, pp.167-187, 1987.

[10]Elder, J. and S. Zucker, “Scale space localization, blur, and contour-based image coding,” in Proc. IEEE Conf. on Computer Vision Pattern Recognition, San Francisco, CA, June 1996, pp.27-34.
[11]Elder, J. and Zucker S., “Local scale control for edge detection and blur estimation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.20, no.7, pp.699-716, 1998.
[12]Elder, J., “Are edges incomplete?,” Int'l J. Computer Vision, vol.34, no.2, pp.97-122, 1999.
[13]Elder, J.H. and R. M. Goldberg, “Image edition in the contour domain,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.23, no.3, pp.291- 296, March 2001.
[14]Gersho, A. and R. M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Publishers, CA, 1992.
[15]Gleicher, M., “Image Snapping,” in Proc. SIGGRAPH’95, NY, 1995, pp.183-190.
[16]Gonzalez, R. C. and R. E. Woods, Digital Image Processing, Prentice-Hall, New Jersey, 2002.
[17]Gunn, S. R. and M. S. Nixon, “A robust snake implementation via a dual active contour,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.19, no.1, pp.63-68, 1997.
[18]Ji, L. and H. Yan, “An intelligent and attractable active contour model for boundary extraction,” in Proc. IEEE Int’l Conf. on Acoustic, Speech, and Signal Processing, Phoenix. Arizona, March 1999, pp.3309-3312.
[19]Ji, L. and H. Yan, “Attractable snake based on the greedy algorithm for extraction,” Pattern Recognition, vol.35, pp.791-806, 2002.
[20]Kass, M., A. Witikin and D. Terzopoulos, “Snakes: Active contour models,” International Journal of Computer Vision, vol.1, no.4, pp.321-331, 1978.
[21]Kim, Y. T., “Contrast enhancement using brightness preserving bi-histogram equalization,” IEEE Trans. on Consumer Electronics, vol.43, no.1, pp.1-8, Feb. 1997.
[22]Kim, S. Y., D. Han, S. J. Choi and J. S. Park, “Image contrast enhancement based on the piecewise-linear approximation of CDF,” IEEE Trans. on Consumer Electronics, vol.45, no.3, pp.828-834, Aug. 1999.
[23]Lam, K.M. and H. Yan, “Fast greedy algorithm for active contours,” Electronics Letters, vol.30, no.1, pp.21-22, 1994.
[24]Leclerc, Y. G. and S. W. Zucker, “The local structure of image discontinuities in one dimension,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.9, no.3, pp.341-355, 1987.
[25]Leymarie, F. and M. D. Levine, “Tracking deformable objects in the plane using an active contour model,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.15, no.6, pp.617-634, 1993.
[26]Mortensen, E. N. and A. B. William, ”Intelligent scissors for image composition,” in Proc. SIGGRAPH’95, New York, NY, 1995, pp.191-198.
[27]Nabout, A., B. Su, and H. Eldin, “A novel closed contour extractor, principle and algorithm,” in Proc. IEEE Int’l Symp. on Circuits and Systems, Seattle, WA, May 1995, pp.445-448.
[28]Rosenfeld, A. and A. Kak, Digital picture proceeding, 2nd Ed., Academic Press, New York, 1982.
[29]Ruzon, M. A. and C. Tomasi, “Alpha estimation in natural images,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Hilton Head Island, SC, June 2000, pp.597-604.
[30]Saund, E. and T. P. Moran, “A perceptually-supported sketch editor,” in Proc. User Interface Software and Technology, Marina del Rey, California, 1994, pp.175-184.
[31]Saund, E. and T. P. Moran, “Perceptual organization in an interactive sketch editing application,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, Cambridge, MA, 1995, pp.597-604.
[32]Smith, A. R. and J. F. Blinn, “Blue screen matting,” in Proc. SIGGRAPH’95, NY, Oct. 1995, pp.259-268.
[33]Sonka, M., V. Hlavac and R. Boyle, Image Processing, Analysis and Machine Vision, Chapman & Hall, London, 1993.
[34]Tan, K. H. and N. Ahuja, “Selecting objects with freehand sketches,” in Proc. IEEE Int’l Conf. on Computer Vision, Vancouver, Canada, July 2001, pp.337-344.
[35]Wang, Y., E. K. Teoh and D. Shen, “lane detection using Catmull-Rom spline,” in Proc. IEEE Int’l Conf. on Intelligent Vehicles, Bethesda, MD, 1998.
[36]Wang, Y., E. K. Teoh and D. Shen, ”lane detection using B-snake,” in Proc. IEEE Int’l Conf. on Information Intelligence and Systems, Bethesda, MD, Oct. 1999, pp.438-443.
[37]Weiss, Y., ” Segmentation using eigenvectors: a unifying view,” in Proc. IEEE Int’l Conf. on Computer Vision, Kerkyra, Greece, Sept. 1999, pp.975-982.
[38]Williams, D.J., and M. Shah, “A fast algorithm for active contours and curvature estimation,” CVGIP: Image Understanding, vol.55, no.1, pp.14-16, 1992.
[39]Wong, Y.Y., P.C. Yuen, and C.S. Tong, ”Contour length terminating criterion for snake model,” Pattern Recognition, vol.31, no.5 pp.597-606, 1998.
[40]Xu, C.Y. and J. L Prince., “Snakes, shapes, and gradient vector flow,” IEEE Trans. on Image Processing, vol.7, no.3, pp.359-369, 1998.