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  • 标题:Distinctive Image Feature Techniques from Scale Invariant Key Points
  • 本地全文:下载
  • 作者:Sanjivanikache ; Sapana Chikundre ; Varsha Salve
  • 期刊名称:International Journal of Innovative Research in Computer and Communication Engineering
  • 印刷版ISSN:2320-9798
  • 电子版ISSN:2320-9801
  • 出版年度:2017
  • 卷号:5
  • 期号:3
  • 页码:6357
  • DOI:10.15680/IJIRCCE.2017.0503411
  • 出版社:S&S Publications
  • 摘要:In the compacted video is transmitted over a correspondence arrange and examined by a server. Theserver incorporates key point identification, descriptor figuring, and highlights coordinating. Video pressure hasnegative impact on picture highlight coordinating execution. The negative effect of pressure can be limited by utilizingthe key focuses removed from the uncompressed video to ascertain descriptors from the compacted video. Theproposed framework gives these key focuses to the server as side data and to separate just the descriptors from thecompacted video. In the first place, we present four diverse casing sorts for key guide encoding toward addressdistinctive sorts of changes in video content. These edge sorts speak to another scene, a similar scene, a graduallyevolving scene, or a quickly moving scene, and are controlled by contrasting components between progressive videooutlines. Intra skip and entomb methods of encoding the key focuses for various edge sorts. For instance, key focusesfor new scenes are encoded utilizing the Intra mode and key focuses for unaltered scenes are skipped. Thus, the bitrateof the side data identified with key point encoding is essentially lessened. At long last, show combine insightfulcoordinating and picture recovery tests led to assess the execution of the proposed approach utilizing the Stanfordportable increased reality dataset and 720p organization recordings. The outcomes demonstrate that the proposedapproach offers essentially enhanced component coordinating and picture recovery execution at a given bitrates.
  • 关键词:Coding; H.265/HEVC; key points; matching; prediction; retrieval; SIFT.
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