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    政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 >  Item 140.119/109339


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/109339


    Title: Generating Ambiguous Figure-Ground Images
    Authors: Kuo, Y.-M.;Chu, H.-K.;Chi, Ming Te;Lee, R.-R.;Lee, T.-Y.
    紀明德
    Contributors: 資科系
    Keywords: Binary images;Bins;Computation theory;Deformation;Pattern matching;Automatic algorithms;Figure-ground perception;Image binarization;Image composition;Image cropping;Multiple interpretations;Partial shape matching;Quality of results;Image processing
    Date: 2017-05
    Issue Date: 2017-05-08 14:40:33 (UTC+8)
    Abstract: Ambiguous figure-ground images, mostly represented as binary images, are fascinating as they present viewers a visual phenomena of perceiving multiple interpretations from a single image. In one possible interpretation, the white region is seen as a foreground figure while the black region is treated as shapeless background. Such perception can reverse instantly at any moment. In this paper, we investigate the theory behind this ambiguous perception and present an automatic algorithm to generate such images. We model the problem as a binary image composition using two object contours and approach it through a three-stage pipeline. The algorithm first performs a partial shape matching to find a good partial contour matching between objects. This matching is based on a content-aware shape matching metric, which captures features of ambiguous figure-ground images. Then we combine matched contours into a compound contour using an adaptive contour deformation, followed by computing an optimal cropping window and image binarization for the compound contour that maximize the completeness of object contours in the final composition. We have tested our system using a wide range of input objects and generated a large number of convincing examples with or without user guidance. The efficiency of our system and quality of results are verified through an extensive experimental study. © 2016 IEEE.
    Relation: IEEE Transactions on Visualization and Computer Graphics, 23(5), 1534-1545
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1109/TVCG.2016.2535331
    DOI: 10.1109/TVCG.2016.2535331
    Appears in Collections:[資訊科學系] 期刊論文

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