Semantic segmentation of indoor-scene RGB-D images based on iterative contraction and merging

Jia Hao Syu*, Shih Hsuan Cho, Sheng-Jyh Wang, Li-Chun Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose an iterative contraction and merging framework (ICM) for semantic segmentation in indoor scenes. Given an input image and a raw depth image, we first derive the dense prediction map from a convolutional neural network (CNN) and a normal vector map from the depth image. By combining the RGB-D image with these two maps, we can guide the ICM process to produce a more accurate hierarchical segmentation tree in a bottom-up manner. After that, based on the hierarchical segmentation tree, we design a decision process which uses the dense prediction map as a reference to make the final decision of semantic segmentation. Experimental results show that the proposed method can generate much more accurate object boundaries if compared to the state-of-the-art methods.

Original languageEnglish
Title of host publicationImage and Signal Processing - 8th International Conference, ICISP 2018, Proceedings
EditorsDriss Mammass, Fathallah Nouboud, Alamin Mansouri, Abderrahim El Moataz
PublisherSpringer Verlag
Pages252-261
Number of pages10
ISBN (Print)9783319942100
DOIs
StatePublished - 1 Jan 2018
Event8th International Conference on Image and Signal Processing, ICISP 2018 - Cherbourg, France
Duration: 2 Jul 20184 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10884 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Image and Signal Processing, ICISP 2018
CountryFrance
CityCherbourg
Period2/07/184/07/18

Keywords

  • Convolutional neural network
  • Iterative contraction and merging
  • RGB-D image
  • Semantic segmentation

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