A Three-pathway Psychobiological Framework of Salient Object Detection Using Stereoscopic Technology
This paper is accepted at ICCV workshop.
Full-text is available on CVF.
Saliency detection, finding the most important parts of an image, has become increasingly popular in computer vision. Existing proposal methods are mostly based on color information, which may not be effective for cluttered backgrounds. We propose a new algorithm leveraging stereopsis to generate optical flow which can obtain addition cue (depth cue) to get the final saliency map. The proposed framework consists of three pathways. The first pathway eliminates the background based on cellular automata. The second pathway gets the optical flow and color flow saliency map. The third pathway calculates a coarse saliency map. Finally, we fuse these three pathways to generate the final saliency map. Besides, we construct a new high-quality dataset with the complex scene to make computer challenge human vision. Experimental results on our dataset and another three popular datasets demonstrate that our method is superior to the existing methods in terms of robustness.
You can download in here
Dataset is public.
You can download in here
@InProceedings{Zhu_2017_ICCV_Workshops,
author = {Zhu, Chunbiao and Li, Ge},
title = {A Three-Pathway Psychobiological Framework of Salient Object Detection Using Stereoscopic Technology},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
This work was supported by the grant of National Natural Science Foundation of China (No.U1611461), Shenzhen Peacock Plan (20130408-183003656), and Science and Technology Planning Project of Guangdong Province, China (No. 2014B090910001).
If you have any general doubt about our work or code which may be of interest for other researchers, please use the public issues section on this github repo. Alternatively, drop us an e-mail at mailto:zhuchunbiao@pku.edu.cn.