RGBD Salient Object Detection using Spatially Coherent Deep Learning Framework

Posheng Huang, Chin Han Shen, Hsu-Feng Hsiao

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781538668115
DOIs
出版狀態Published - 31 一月 2019
事件23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
持續時間: 19 十一月 201821 十一月 2018

出版系列

名字International Conference on Digital Signal Processing, DSP
2018-November

Conference

Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018
國家China
城市Shanghai
期間19/11/1821/11/18

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  • 引用此

    Huang, P., Shen, C. H., & Hsiao, H-F. (2019). RGBD Salient Object Detection using Spatially Coherent Deep Learning Framework. 於 2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018 [8631584] (International Conference on Digital Signal Processing, DSP; 卷 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDSP.2018.8631584