TY - GEN
T1 - Simple Combination of Appearance and Depth for Foreground Segmentation
AU - Minematsu, Tsubasa
AU - Shimada, Atsushi
AU - Uchiyama, Hideaki
AU - Taniguchi, Rin Ichiro
N1 - Funding Information:
Acknowledgment. This work was partially supported by JSPS KAKENHI Grant Number JP16J02614 and JP15K12066. We acknowledge the SBM-RGB dataset web page http://rgbd2017.na.icar.cnr.it/SBM-RGBDdataset.html.
Publisher Copyright:
© 2017, Springer International Publishing AG.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
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U2 - 10.1007/978-3-319-70742-6_25
DO - 10.1007/978-3-319-70742-6_25
M3 - Conference contribution
AN - SCOPUS:85041129216
SN - 9783319707419
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 266
EP - 277
BT - New Trends in Image Analysis and Processing – ICIAP 2017 - ICIAP International Workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Revised Selected Papers
A2 - Battiato, Sebastiano
A2 - Farinella, Giovanni Maria
A2 - Leo, Marco
A2 - Gallo, Giovanni
PB - Springer Verlag
T2 - 19th International Conference on Image Analysis and Processing, ICIAP 2017
Y2 - 5 June 2017 through 9 June 2017
ER -