Saliency detection method for panoramic images based on GCN-ELM

Nan An; Haiyang Yu; Ripei Zhang; Xiaojuan Hu; Yanfeng Li

doi:10.1117/12.3055729

9 January 2025 Saliency detection method for panoramic images based on GCN-ELM

Nan An, Haiyang Yu, Ripei Zhang, Xiaojuan Hu, Yanfeng Li

Author Affiliations +

Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 134861O (2025) https://doi.org/10.1117/12.3055729
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China

Abstract

In the field of saliency detection for panoramic images, traditional equirectangular and cube projection methods in panoramic image saliency detection often face issues like distortion and discontinuities, impacting detection accuracy. This study introduces an innovative image resampling technique and a GCN-ELM joint model. By evenly distributing spherical pixels onto a 2D plane, the method reduces pixel redundancy from equirectangular projection. Experimental results show that this approach significantly enhances saliency detection performance compared to existing methods.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nan An, Haiyang Yu, Ripei Zhang, Xiaojuan Hu, and Yanfeng Li "Saliency detection method for panoramic images based on GCN-ELM", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 134861O (9 January 2025); https://doi.org/10.1117/12.3055729

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KEYWORDS

Panoramic photography

RGB color model

Visualization

Convolution

Visual process modeling

Feature extraction

Extreme learning machines

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