A learning-based method using data augmentation for light field salient object detection

Xi Zhu; Xucheng Wang; Zhenrong Zheng

doi:10.1117/12.2686628

27 November 2023 A learning-based method using data augmentation for light field salient object detection

Xi Zhu, Xucheng Wang, Zhenrong Zheng

Proceedings Volume 12767, Optoelectronic Imaging and Multimedia Technology X; 1276704 (2023) https://doi.org/10.1117/12.2686628
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Abstract

In this paper, a new method is proposed for light field SOD by using convolutional neural networks. First, the light field dataset is extended by geometric transformations such as stretching, cropping, flipping, rotating, etc. The augmented data are then weighted with natural data to train the light field SOD. We propose a mutual attention approach in this process, extracting and fusing features from RGB images as well as depth maps. Therefore, our network can generate an accurate saliency map from the input light field images after training. The obtained saliency map can provide reliable a priori information for tasks such as semantic segmentation, target recognition, and visual tracking.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xi Zhu, Xucheng Wang, and Zhenrong Zheng "A learning-based method using data augmentation for light field salient object detection", Proc. SPIE 12767, Optoelectronic Imaging and Multimedia Technology X, 1276704 (27 November 2023); https://doi.org/10.1117/12.2686628

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KEYWORDS

Education and training

Object detection

RGB color model

Data modeling

Feature extraction

Cameras

Depth maps

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