English  |  正體中文  |  简体中文  |  Items with full text/Total items : 53694/88316 (61%)
Visitors : 10189216      Online Users : 77
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/76036


    Title: Image stylisation
    Authors: Yen, Shwu-huey;Yeh, Jih-pin;Lin, Hwei-jen;Lee, Tai-kuang;Pai, Yi-chun
    Contributors: 淡江大學資訊工程學系
    Keywords: stained glass;image stylisation;segmentation;Voronoi diagram;seed;back-propagation neural network (BPNN)
    Date: 2012
    Issue Date: 2012-04-30 18:00:09 (UTC+8)
    Publisher: Leeds: Maney Publishing
    Abstract: We propose a flexible image stylisation system for reforming a source image into a
    stained-glass style or some other style desired by users. It consists of three stages including image segmentation, colour or texture allocation and synthesis of calmes. Each stage provides some options for users to choose from. The system provides three options of image
    segmentation for the users to choose from, including automatic, semi-automatic and manual image segmentation. The automatic option randomly generates seeds in the image region, calculates the distance between each seed and each pixel and then assigns each pixel to the nearest seed region to accomplish image segmentation. The semi-automatic one segments the image according to the seeds provided by the user. The user then repeatedly adjusts the result by deleting or adding some seeds. The last version involves manual image segmentation performed by the users. The next step in segmentation is colour or texture allocation. Each segmented region is allocated with the colour or texture of reference images chosen from a pre-built database. The proposed system provides many options for this allocation, allowing the users to select from a variety of methods of texture or colour allocation. A unique feature of stained glass is that it is held together with lead, zinc, brass or copper strips called calmes. We use a back-propagation neural network to train a calme generator using an image of line drawing as the input together with an image of the corresponding calme of the line drawing as the output. The users can choose whether or not to put the synthesised calmes on the resulting images. The proposed system is easy to use and very flexible. It provides many options for users to stylise an image into a variety of stylised results. Our experiments show that the proposed system can generate a wide range of satisfactory results.
    Relation: Imaging Science Journal 60(5), pp.248-255
    DOI: 10.1179/1743131X11Y.0000000046
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

    Files in This Item:

    File Description SizeFormat
    1368-2199_60(5)p248-255.pdf1015KbAdobe PDF99View/Open
    index.html0KbHTML169View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback