Basic Search / Detailed Display

Author: 李國維
Guo-Wei Lee
Thesis Title: 系統性漫畫單格向量化和漫畫網點可操作性並即時渲染
Systemic Single-frame Comics Vectorization and Screentone Manipulation and Real-time Rending
Advisor: 姚智原
Chih-Yuan Yao
Yu-Chi Lai
Committee: 朱宏國
Hung-Kuo Chu
Yu-Shuen Wang
Degree: 碩士
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2015
Graduation Academic Year: 103
Language: 中文
Pages: 93
Keywords (in Chinese): 漫畫向量化網點程序渲染
Keywords (in other languages): Manga, Vectorization, Screentone, Procedural shaders
Reference times: Clicks: 387Downloads: 0
School Collection Retrieve National Library Collection Retrieve Error Report
  • 漫畫雖然是發源於日本, 但是,其易懂及普遍性, 目前已經逐漸成為全球流行的娛樂藝術之一。隨著網路技術以及通訊設備的進步下, 在智慧型手機及其他行動通訊裝置上, 閱讀欣賞漫畫也成為一種趨勢。但是,這些裝置並沒有一種統一規格, 因此,為了適應不同裝置的螢幕大小,如何有效的建構數位媒體以方便閱讀及顯示,成為漫畫閱讀的重要課題之一。目前漫畫數位媒體的產方式仍以人工掃描為主, 這類數化方式在產生的過程中, 會因為掃瞄解析度,而造成干擾影響閱讀。同時,它也要求太多的人工介入。因此設計一套方法及工具用於快速及有效地將文本漫畫數位化, 並且適當地解決在不同數位媒體上閱讀的問題。我們系統的輸入是傳統掃描的漫畫原始灰階資料, 為了方便後續演算法處理, 我們設計大津高斯二值法有效地將漫畫轉成二黑白資料, 在此同時清除掃描雜訊, 並保留是漫畫原本的細節, 如細小部分的網點區域、邊界交界處的模糊地帶保留下來。再來是解析漫畫的各個元素, 我們在分析漫畫網點區域時,在不同的解析度下藉由頻率的概念, 找出可能是網點區域的位置。再利用特徵的概念去分析可能區域, 結合局部二值模式直方圖(Local Binary Pattern (LBP) histogram)及賈柏濾波器(Gabor filtering)去進行資料庫的比對, 用以精確地找出網點的類型。我們可以再利用網點的特徵性, 幫助我們更輕易地找出漫畫中精確的網點區域。最後,系統會依據所偵測出的網點類型及參數, 依據現今的觀看參數使用顯示卡著色器(GPU Shader)去建構一張適當的網點貼圖,同時也自動產生對應的貼圖座標, 用以產成向量化的渲染結果。

    Manga are a popular artistic form around the world and artists use simple line drawing and screentone to create all kinds of interesting productions. It is important for digital reproduction to reproduce these elements for proper delivery of contents and intentionsㄛ. Additionally, popular Manga are usually reproduced as Cartoon animations and it is tedious to animate raster images. Vectorization can be helpful for both Manga reproduction and Cartoon production. However, scanning methods still dominate Manga digital reproduction and the results are not satisfied because of noises and limited resolution in addition to a large amount of man power. Therefore, this proposal aims at transforming a scanned Manga object to a vector representation for interactive manipulation and resolution independent rendering. The process can be progressed in the following steps: 1). Scanned Manga pages are transformed to its corresponding black-and-white format. Manga pages are generally scanned in gray format to maintain the screentone details. Although binarization can make Manga features distinct, it is not a trivial job to determine a global or local threshold to properly maintain screentone details. Therefore, we aim at designing a binarization method for Manga. 2). Manga elements are detected and identified along with their corresponding types and properties. Generally, elements can be categorized as strokes and screentone. However, screentone patterns are chosen based on authors’ preference, mood and the requirement of scenarios and therefore, it varies too much to be really difficult in robust detection and identification. We plan separate the process into two sub-steps: detection and identification. A algorithm is designed to locally determine the screentone regions and then another method extract the pattern in the region to look up in our collected screentone database. 3). These detected and identified Manga elements must be vectorized for reading with different scales on different devices of different resolutions and screen sizes. For strokes, a ii clear mechanics must be designed to remove mixing-up among strokes and screentone regions for smooth and distinct strokes. Then, the strokes can be represented as fat spline lines. Furthermore, screentone regions can be triangulated along with its screentone type and properties for later procedural rendering. 4). Finally, both represenations must be rendered according to the reading conditions such as screen size, screen resolution, and reading scale. Clearly rendering screentones is even more important to express the intention of authors. We plan to design different procedural pixel shaders for different types of screentone patterns and a curve-based shader for strokes. Additionally, after vectorization, we also plan to design several interesting manipulation tools such as modify the screentone pattern and density and deform the vectorized characters and objects for interesting effects to ease the generation of Manga and Cartoon.

    中文摘要 i Abstract ii 目錄 iv 表目錄 vii 圖目錄 viii 演算法目錄 xi 1 介紹 1 1.1 貢獻 4 1.2 論文架構 5 2 相關研究 7 2.1 卡通和漫畫數位化 7 2.2 基於紋理的分割 8 2.3 圖像向量化 9 2.4 與漫畫向量化及操縱的比較 9 3 方法 11 4 二值化 13 5 漫畫中部件偵測 17 5.1 多層資網格區域偵測 17 5.2 黑色圖層向量化 19 5.3 網格類型辨別 21 5.4 網格相關參數粹取 23 5.5 網格內實心顏色區域精練 25 6 漫畫向量化 29 6.1 邊界向量化 30 6.2 產生統一的著色單位 30 6.3 基於著色分段重新三角化 31 6.4 路徑隱式化著色資訊嵌入 32 6.5 網點紋理座標生成 33 7 生成 37 7.1 重覆生成定義函數 37 7.2 程序性的建構出網點紋理 39 7.2.1 簡單的網點樣式 39 7.2.2 複雜的網點樣式 40 7.3 即時渲染上色 41 7.3.1 純色三角形 41 7.3.2 複合三角形 43 8 結果與比較. 46 8.1 二值化結果 46 8.2 多解析度辨識結果 61 8.3 網格辨識結果 63 8.4 向量化結果 67 9 結論與未來工作 74 參考文獻 76

    [1] L. Xu, Q. Yan, Y. Xia, and J. Jia, “Structure extraction from texture via
    relative total variation,” ACM Trans. Graph., vol. 31, pp. 139:1–139:10, Nov.
    [2] R. Adhitya, “Manga vectorization and manipulation,” Master’s thesis, National
    Taiwan University of Science and Technology, 2014.
    [3] J. Kopf and D. Lischinski, “Digital reconstruction of halftoned color comics,”
    ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2012),
    vol. 31, p. to appear, Nov. 2012.
    [4] G. Noris, A. Hornung, R. W. Sumner, M. Simmons, and M. Gross, “Topologydriven
    vectorization of clean line drawings,” ACM Trans. Graph., vol. 32,
    pp. 4:1–4:11, Feb. 2013.
    [5] Y. Qu, T.-T. Wong, and P.-A. Heng, “Manga colorization,” ACM Transactions
    on Graphics (SIGGRAPH 2006 issue), vol. 25, pp. 1214–1220, July 2006.
    [6] Y. Qu, W.-M. Pang, T.-T. Wong, and P.-A. Heng, “Richness-preserving manga
    screening,” ACM Transactions on Graphics (SIGGRAPH Asia 2008 issue),
    vol. 27, pp. 155:1–155:8, December 2008.
    [7] S.-H. Zhang, T. Chen, Y.-F. Zhang, S.-M. Hu, and R. Martin, “Vectorizing cartoon
    animations,” Visualization and Computer Graphics, IEEE Transactions
    on, vol. 15, pp. 618–629, July 2009.
    [8] M. Galun, E. Sharon, R. Basri, and A. Brandt, “Texture segmentation by
    multiscale aggregation of filter responses and shape elements,” in Proceedings 76
    of the Ninth IEEE International Conference on Computer Vision - Volume 2,
    ICCV ’03, pp. 716–, 2003.
    [9] M. Varma and A. Zisserman, “Texture classification: are filter banks necessary?,”
    in Computer Vision and Pattern Recognition, 2003. Proceedings. 2003
    IEEE Computer Society Conference on, vol. 2, pp. II–691–8 vol.2, June 2003.
    [10] D. S´ykora, J. Buri´anek, and J. ˇZ´ara, “Unsupervised colorization of black-andwhite
    cartoons,” in Proceedings of the 3rd International Symposium on Nonphotorealistic
    Animation and Rendering, NPAR ’04, (New York, NY, USA),
    pp. 121–127, ACM, 2004.
    [11] A. Buades, T. Le, J. M. Morel, and L. Vese, “Fast cartoon + texture image
    filters,” Image Processing, IEEE Transactions on, vol. 19, pp. 1978–1986, Aug
    [12] T. Hofmann, J. Puzicha, and J. Buhmann, “Unsupervised texture segmentation
    in a deterministic annealing framework,” Pattern Analysis and Machine
    Intelligence, IEEE Transactions on, vol. 20, pp. 803–818, Aug 1998.
    [13] N. Paragios and R. Deriche, “Geodesic active regions for supervised texture
    segmentation,” in Computer Vision, 1999. The Proceedings of the Seventh IEEE
    International Conference on, vol. 2, pp. 926–932 vol.2, 1999.
    [14] Y. Matsui, T. Yamasaki, and K. Aizawa, “Interactive manga retargeting,”
    in ACM SIGGRAPH 2011 Posters, SIGGRAPH ’11, (New York, NY, USA),
    pp. 35:1–35:1, ACM, 2011.
    [15] Z. Liao, H. Hoppe, D. Forsyth, and Y. Yu, “A subdivision-based representation
    for vector image editing,” IEEE Transactions on Visualization and Computer
    Graphics, vol. 99, no. PrePrints, 2012.77.
    [16] M. Finch, J. Snyder, and H. Hoppe, “Freeform vector graphics with controlled
    thin-plate splines,” ACM Trans. Graph., vol. 30, no. 6, pp. 166:1–166:10, 2011.
    [17] Y.-K. Lai, S.-M. Hu, and R. R. Martin, “Automatic and topology-preserving
    gradient mesh generation for image vectorization,” ACM Trans. Graph., vol. 28,
    no. 3, pp. 85:1–85:8, 2009.
    [18] T. Xia, B. Liao, and Y. Yu, “Patch-based image vectorization with automatic
    curvilinear feature alignment,” ACM Trans. Graph., vol. 28, no. 5, pp. 115:1–
    115:10, 2009.
    [19] L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise
    removal algorithms,” Physica D: Nonlinear Phenomena, vol. 60, no. 1, pp. 259
    – 268, 1992.
    [20] P. Selinger, “Potrace: a polygon-based tracing algorithm,” 2003.
    [21] P. Buchanan, M. Doggett, and R.Mukundan, “Structural vectorization of raster
    images,” in Proceedings of the 27th Conference on Image and Vision Computing
    New Zealand, IVCNZ ’12, pp. 319–324, 2012.
    [22] A. Hayashi and R. Yagizawa, Manga Skills Vol. 1: Master the Drawing Fundamentals
    of Characters. Graphics Inc., 2002.
    [23] T. Ojala, M. Pietikぴainen, and D. Harwood, “A comparative study of texture
    measures with classification based on featured distributions,” Pattern Recognition,
    vol. 29, no. 1, pp. 51–59, 1996.
    [24] C. Loop and J. Blinn, “Resolution independent curve rendering using programmable
    graphics hardware,” ACM Trans. Graph., vol. 24, pp. 1000–1009,
    July 2005.78.
    [25] Q. Lou and L. Liu, “Curve intersection using hybrid clipping,” Computers &
    Graphics, vol. 36, no. 5, pp. 309 – 320, 2012.
    [26] A. Jacobson, I. Baran, J. Popovi´c, and O. Sorkine, “Bounded biharmonic
    weights for real-time deformation,” ACM Transactions on Graphics (proceedings
    of ACM SIGGRAPH), vol. 30, no. 4, pp. 78:1–78:8, 2011.
    [27] H. Liu, W. Liu, and L. Latecki, “Convex shape decomposition,” in Computer
    Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pp. 97–
    104, June 2010.
    [28] Y. Liu and R. T. Collins, “A computational model for repeated pattern perception
    using frieze and wallpaper groups,” IEEE Xplore, vol. 1, no. 8, 2000.
    [29] N. Otsu, “A threshold selection method from gray-level histograms,” Systems,
    Man and Cybernetics, IEEE Transactions on, vol. 9, pp. 62–66, Jan 1979.
    [30] R. L. Stevenson, “Inverse halftoning via map estimation,” Trans. Img. Proc.,
    vol. 6, no. 4, pp. 574–583.

    無法下載圖示 Full text public date 2020/08/27 (Intranet public)
    Full text public date This full text is not authorized to be published. (Internet public)
    Full text public date This full text is not authorized to be published. (National library)