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Semantic Segmentation of Indoor Scenes Using Depth Superpixel
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 Title & Authors
Semantic Segmentation of Indoor Scenes Using Depth Superpixel
Kim, Seon-Keol; Kang, Hang-Bong;
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 Abstract
In this paper, we propose a novel post-processing method of semantic segmentation from indoor scenes with RGBD inputs. For accurate segmentation, various post-processing methods such as superpixel from color edges or Conditional Random Field (CRF) method considering neighborhood connectivity have been used, but these methods are not efficient due to high complexity and computational cost. To solve this problem, we maximize the efficiency of post processing by using depth superpixel extracted from disparity image to handle object silhouette. Our experimental results show reasonable performances compared to previous methods in the post processing of semantic segmentation.
 Keywords
Depth;Superpixel;RGBD;Segmentation;Semantic Segmentation;
 Language
Korean
 Cited by
1.
Single Image Depth Estimation With Integration of Parametric Learning and Non-Parametric Sampling,;;

한국멀티미디어학회논문지, 2016. vol.19. 9, pp.1659-1668 crossref(new window)
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