Simple Combination of Appearance and Depth for Foreground Segmentation

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

10 被引用数 (Scopus)

抄録

In foreground segmentation, the depth information is robust to problems of the appearance information such as illumination changes and color camouflage; however, the depth information is not always measured and suffers from depth camouflage. In order to compensate for the disadvantages of the two pieces of information, we define an energy function based on the two likelihoods of depth and appearance backgrounds and minimize the energy using graph cuts to obtain a foreground mask. The two likelihoods are obtained using background subtraction. We use the farthest depth as the depth background in the background subtraction according to the depth information. The appearance background is defined as the appearance with a large likelihood of the depth background to eliminate appearances of foreground objects. In the computation of the likelihood of the appearance background, we also use the likelihood of the depth background for reducing false positives owing to illumination changes. In our experiment, we confirm that our method is sufficiently accurate for indoor environments using the SBM-RGBD 2017 dataset.

本文言語英語
ホスト出版物のタイトルNew Trends in Image Analysis and Processing – ICIAP 2017 - ICIAP International Workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Revised Selected Papers
編集者Sebastiano Battiato, Giovanni Maria Farinella, Marco Leo, Giovanni Gallo
出版社Springer Verlag
ページ266-277
ページ数12
ISBN(印刷版)9783319707419
DOI
出版ステータス出版済み - 2017
イベント19th International Conference on Image Analysis and Processing, ICIAP 2017 - Catania, イタリア
継続期間: 6 5 20176 9 2017

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
10590 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他19th International Conference on Image Analysis and Processing, ICIAP 2017
Countryイタリア
CityCatania
Period6/5/176/9/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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