RGBD Salient Object Detection using Spatially Coherent Deep Learning Framework

Posheng Huang, Chin Han Shen, Hsu-Feng Hsiao

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

33 Scopus citations

Abstract

In this paper, a learning based salient object detection method for RGBD images is introduced. With the assistance of depth information, the silhouette features of an object can be retrieved primarily, and it can lead to better detection of salient objects. In addition, many recent works still rely on some image post-processing methods to improve their performance. We develop a more efficient end-to-end model with a modified design of loss function used in our training network. The design of the new loss function is to increase the spatial coherence of detected salient objects. From the evaluation results, the proposed approach shows good performance compared with the methods that are considered to be state-of-the-art.

Original languageAmerican English
Title of host publication2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538668115
DOIs
StatePublished - 2 Jul 2018
Event23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
Duration: 19 Nov 201821 Nov 2018

Publication series

NameInternational Conference on Digital Signal Processing, DSP
Volume2018-November

Conference

Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018
Country/TerritoryChina
CityShanghai
Period19/11/1821/11/18

Keywords

  • deep learning
  • fully convolutional networks
  • salient object detection

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