Light-field depth estimation powers salient object detection

Xi Zhu; Huai Xia; Changwei Zhang; Xucheng Wang; Zhenrong Zheng

doi:10.1117/12.3035962

22 November 2024 Light-field depth estimation powers salient object detection

Xi Zhu, Huai Xia, Changwei Zhang, Xucheng Wang, Zhenrong Zheng

Author Affiliations +

Proceedings Volume 13239, Optoelectronic Imaging and Multimedia Technology XI; 132390W (2024) https://doi.org/10.1117/12.3035962
Event: SPIE/COS Photonics Asia, 2024, Nantong, Jiangsu, China

Conference Poster

Abstract

Compared to traditional RGB images, light field images fulfill the demand for high-dimensional information in salient object detection. In this paper, we propose a salient object detection method based on light field depth estimation. In particular, we adopted a supervised learning approach for the design of the light field depth estimation algorithm. Subaperture images in each of the 4 viewing directions are constructed into epipolar plane images (EPI) as inputs to the multistream network. The multi-stream network consists of 4 branches, each containing a specific number of convolutional modules to extract the depth information in the epipolar plane images of the corresponding directions. The features extracted from the 4 branches are fed into the merged network to compute the correlation between the different epipolar plane images. The obtained disparity maps and RGB images are simultaneously inputted into a two-stream convolutional neural network for training. The trained model can achieve highly generalized and robust light field salient object detection. Experiments with real-world light field images indicate the superior performance of our method for clearly plotting the boundaries of saliency objects.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Xi Zhu, Huai Xia, Changwei Zhang, Xucheng Wang, and Zhenrong Zheng "Light-field depth estimation powers salient object detection", Proc. SPIE 13239, Optoelectronic Imaging and Multimedia Technology XI, 132390W (22 November 2024); https://doi.org/10.1117/12.3035962

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