Multimodal feature cooperation fusion-based light field salient object detection

Sijia Xu

doi:10.1117/12.3048145

3 October 2024 Multimodal feature cooperation fusion-based light field salient object detection

Sijia Xu

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720X (2024) https://doi.org/10.1117/12.3048145
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Light field data provides effective spatial information and focus cues for saliency detection in complex scenes. However, existing research on light field saliency detection still faces limitations. Therefore, this paper proposes a light field image saliency detection network based on Multi-Modal Feature Cooperation Fusion. First, we construct a Focal Feature Aggregation (FFA) module, which aims to explore and enhance features obtained from the unique structure of focus stacks. At the same time, the Multi-Modal Feature Cooperation Fusion (MFCF) module is utilized to establish connections between features in two different dimensions, achieving multi-level complementary effects through cross-modal feature collaboration between all-focus and focus stack features.

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

Citation Download Citation

Sijia Xu "Multimodal feature cooperation fusion-based light field salient object detection", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720X (3 October 2024); https://doi.org/10.1117/12.3048145

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KEYWORDS

Object detection

RGB color model

Feature fusion

Image fusion

Feature extraction

Ablation

Visualization

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