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Visual Attention Prediction for Stereoscopic Video by Multi-Module Fully Convolutional Network.
- Source :
- IEEE Transactions on Image Processing; Nov2019, Vol. 28 Issue 11, p5253-5265, 13p
- Publication Year :
- 2019
-
Abstract
- Visual attention is an important mechanism in the human visual system (HVS) and there have been numerous saliency detection algorithms designed for 2D images/video recently. However, the research for fixation detection of stereoscopic video is still limited and challenging due to the complicated depth and motion information. In this paper, we design a novel multi-module fully convolutional network (MM-FCN) for fixation detection of stereoscopic video. Specifically, we design a fully convolutional network for spatial saliency prediction (S-FCN), where the initial spatial saliency map of stereoscopic video is learned by image database of object detection. Furthermore, the fully convolutional network for temporal saliency prediction (T-FCN) is constructed by combining saliency results from S-FCN and motion information from video frames. Finally, the fully convolutional network for depth fixation prediction (D-FCN) is designed to compute the final fixation map of stereoscopic video by learning depth features with spatiotemporal features from T-FCN. The experimental results show that the proposed MM-FCN can predict fixation results for stereoscopic video more effectively and efficiently than other related fixation prediction methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 28
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 138256557
- Full Text :
- https://doi.org/10.1109/TIP.2019.2916766