Simple Combination of Appearance and Depth for Foreground Segmentation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationNew Trends in Image Analysis and Processing – ICIAP 2017 - ICIAP International Workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Revised Selected Papers
EditorsSebastiano Battiato, Giovanni Maria Farinella, Marco Leo, Giovanni Gallo
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319707419
Publication statusPublished - 2017
Event19th International Conference on Image Analysis and Processing, ICIAP 2017 - Catania, Italy
Duration: Jun 5 2017Jun 9 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10590 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other19th International Conference on Image Analysis and Processing, ICIAP 2017

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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