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    請使用永久網址來引用或連結此文件: https://ir.lib.ncu.edu.tw/handle/987654321/107261


    題名: SIFT-guided multi-resolution video inpainting with innovative scheduling mechanism and irregular patch matching
    作者: 蘇柏齊;Kuo, Tien-Ying;Su, Po-Chyi;Kuan, Yun-Ping
    貢獻者: 資訊電機學院資訊工程學系
    關鍵詞: Algorithms;Clips;Inpainting;Matching;Motion vector field;Multi-resolution;Scheduling;Sift;Similarity;Surface layer;Television;Texture;Video
    日期: 2016-12-10
    上傳時間: 2026-04-23 14:03:17 (UTC+8)
    出版者: Elsevier Inc.;Elsevier Inc
    摘要: 摘要: •An effective video inpainting method for repairing damaged video, or removing such unwanted objects as burned–in subtitles in TV programs.•A classifier and scheduler scheme to prioritize inpainting order, which significantly affects the inpainting results.•A multi–resolution approach with SIFT guiding the inpainting path to resolve uncertainty in large inpainting regions.•Irregular patch–filling to better preserve visually significant areas and speed up the process. Effective video inpainting that maintains both temporal and spatial continuity is required in many applications. This research presents a new video inpainting algorithm targeted at achieving a better tradeoff between visual quality and computational complexity. The inpainting regions in video frames are classified into two types, i.e., structure and smooth. A scheduling mechanism considering texture data amount, inpainting confidence and motion vector uniformity is proposed to determine an appropriate processing order of inpainting points. A novel patch comparison function to evaluate the similarity and an irregular patch-filling method are developed to ensure decent visual quality and faster inpainting speed. The ideas of multi-resolution processing and scale-invariant feature transform are further adopted such that larger inpainting areas can still be processed well. To evaluate the effectiveness of the proposed algorithm, inpainting on television programs and video clips from literature is tested. The experimental results demonstrate better performance when compared with traditional methods.
    出版者: Elsevier Inc
    出版日期: 2016-12-10
    出處: Information sciences, 2016-12, Vol.373, p.95-109
    資源來源: Elsevier ScienceDirect Journals: Bodleian Libraries collection
    版權: 2016 Elsevier Inc.
    識別號: ISSN: 0020-0255
    識別號: EISSN: 1872-6291
    識別號: DOI: 10.1016/j.ins.2016.08.091
    顯示於類別:[資訊工程學系] 期刊論文

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