臺灣博碩士論文加值系統

在計算機視覺及影像處理的領域中，影像分割占了一個很重要的地位。
影像切割技術雖持續不斷地被提出，然而，目前影像切割仍存有許多困難，而現今有些方法可以被應用於彩色影像之切割，大多的思維只將影像從一維空間擴充到三維色彩空間，並沒有討論色彩資料中所提供的其他相關訊息。因此，色彩空間對於影像分割也是一個很值得深入研究的主題之一。
影像分割的目的，是希望能夠從影像中找出我們所感興趣的區域，或者是有意義的區域。
超像素能夠取得像冗餘資訊，且降低後續處理任務複雜度，目前已受到了國內外研究者的日益關注。本研究提出一個分割方式，以SLIC超像素方法劃分影像為多個子區域，再依照本研究所提出之合併方法，結合紋理與 H、S、V、R、G和B色彩特徵進行特徵值相差最小的子區域合併，針對多物件、背景複雜與物件及背景差異性低等類型之彩色影像進行區域分割。根據實驗結果，本研究提出方式可成功分割複雜背景影像中之突出之物件。最後對本研究的方法與應用進行了結果討論和未來展望。

In the field of computer vision and image processing, image segmentation occupies a very important position.
Image segmentation technology is constantly being put forward, however, the current image cutting still has many difficulties, and now some methods can be applied to the color image segmentation, most of the thinking only the image from one-dimensional space expansion to three-dimensional color space, and did not discuss other relevant information provided in color data. Therefore, the color space for image segmentation is also a very worthy of one of the topics of in-depth study.
The purpose of image segmentation is to be able to find areas of interest from the image, or a meaningful area.
Superpixel can achieve redundant information, and reduce the complexity of follow-up processing tasks, has been the growing concern of researchers at home and abroad. This study presents a segmentation approach to the SLIC superpixel approach and the sub-regions with the smallest of the eigenvalues of the H, S, V, R, G and B color characteristics combined with the texture are combined with the sub-regions, and the background is complex and the background is complex. Object and background difference of low type of color image for regional segmentation. According to the experimental results, this study suggests that the way to successfully segment the complex objects in complex background images.
Finally, the results of this study and application of the results of the discussion and future prospects.

第一章 緒論
1.1 研究背景
1.2 研究動機
1.3 論文貢獻
1.4 論文架構
第二章 文獻探討
2.1 常用的影像分割方法
2.2 以圖學為基礎的超像素分割
2.2.1 Graph-based方法
2.2.2 Ncuts方法
2.3 以梯度上升為基礎之超像素分割方法
2.3.1 分水嶺方法
2.3.2 Mean-shift方法
2.3.3 SLIC方法
2.4 其他相關方法之比較
2.5 色彩空間模型
2.5.1 RGB色彩空間模型
2.5.2 HSV色彩空間
2.5.3 CIE LAB色彩空間
2.5.4 色彩空間比較
第三章 材料與研究方法
3.1 實驗材料
3.2 實驗流程
3.3 影像前處理
3.3.1 雙邊濾波
3.3.2 邊緣銳化
3.4 以超像素為基礎的影像分割
3.5 超像素合併方法
第四章 實驗結果與討論
4.1 實驗環境
4.2 實驗評估指標
4.3 實驗結果
第五章 結論與未來展望
5.1 本研究之結論
5.2 研究限制
5.3 未來展望
參考文獻

Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2010). Slic superpixels (No. EPFL-REPORT-149300).
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012). SLIC superpixels compared to state-of-the-art superpixel methods. IEEE transactions on pattern analysis and machine intelligence, 34(11), 2274-2282.
Baatz, M. (2000). Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte geographische informationsverarbeitung, 12-23.
Bieniek, A., & Moga, A. (2000). An efficient watershed algorithm based on connected components. Pattern recognition, 33(6), 907-916.
Black, M. J., Sapiro, G., Marimont, D. H., & Heeger, D. (1998). Robust anisotropic diffusion. IEEE Transactions on image processing, 7(3), 421-432.
Boykov, Y., & Kolmogorov, V. (2004). An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE transactions on pattern analysis and machine intelligence, 26(9), 1124-1137.
Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on pattern analysis and machine intelligence, 23(11), 1222-1239.
Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), 679-698.
Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, 24(5), 603-619.
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International journal of computer vision, 59(2), 167-181.
Freixenet, J., Muñoz, X., Raba, D., Martí, J., & Cufí, X. (2002). Yet another survey on image segmentation: Region and boundary information integration. Computer Vision—ECCV 2002, 21-25.
Geneletti, D., & Gorte, B. G. H. (2003). A method for object-oriented land cover classification combining Landsat TM data and aerial photographs. International Journal of Remote Sensing, 24(6), 1273-1286.
Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2002). Image segmentation. Digital image processing, 2, 331-390.
Kapur, J. N., Sahoo, P. K., & Wong, A. K. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer vision, graphics, and image processing, 29(3), 273-285.
Laprade, R. H. (1988). Split-and-merge segmentation of aerial photographs. Computer Vision, graphics, and image Processing, 44(1), 77-86.
Levinshtein, A., Stere, A., Kutulakos, K. N., Fleet, D. J., Dickinson, S. J., & Siddiqi, K. (2009). Turbopixels: Fast superpixels using geometric flows. IEEE transactions on pattern analysis and machine intelligence, 31(12), 2290-2297.
LIU, J., & LI, P. (2014). A High Resolution Image Segmentation Method by Combined Structural and Spectral Characteristics. Acta Geodaetica et Cartographica Sinica, 5, 006.
Luccheseyz, L., & Mitray, S. K. (2001). Color image segmentation: A state-of-the-art survey. Proceedings of the Indian National Science Academy (INSA-A), 67(2), 207-221.
Meilǎ, M. (2005, August). Comparing clusterings: an axiomatic view. In Proceedings of the 22nd international conference on Machine learning (pp. 577-584). ACM.
Moore, A. P., Prince, S. J., Warrell, J., Mohammed, U., & Jones, G. (2008, June). Superpixel lattices. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1-8). IEEE.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), 62-66.
Ren, X., & Malik, J. (2003, October). Learning a classification model for segmentation. In null (p. 10). IEEE.
Rother, C., Kolmogorov, V., & Blake, A. (2004, August). Grabcut: Interactive foreground extraction using iterated graph cuts. In ACM transactions on graphics (TOG) (Vol. 23, No. 3, pp. 309-314). ACM.
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on pattern analysis and machine intelligence, 22(8), 888-905.
Shih, F. Y., & Cheng, S. (2005). Automatic seeded region growing for color image segmentation. Image and vision computing, 23(10), 877-886.
Smith, A. R. (1978). Color gamut transform pairs. ACM Siggraph Computer Graphics, 12(3), 12-19.
Vedaldi, A., & Soatto, S. (2008). Quick shift and kernel methods for mode seeking. Computer vision–ECCV 2008, 705-718.
Vincent, L., & Soille, P. (1991). Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis & Machine Intelligence, (6), 583-598.
Yuan, J., Wang, D., & Li, R. (2014). Remote sensing image segmentation by combining spectral and texture features. IEEE transactions on geoscience and remote sensing, 52(1), 16-24.
Yuan, F., Zhou, Z., & Song, X. (2007). K-means clustering algorithm with meliorated initial center. Jisuanji Gongcheng/ Computer Engineering, 32(3), 65-66.