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Authors: Gisel Bastidas-Guacho 1 ; 2 ; Patricio Moreno-Vallejo 1 ; 2 ; Boris Vintimilla 1 and Angel Sappa 1 ; 3

Affiliations: 1 ESPOL Polytechnic University, Campus Gustavo Galindo, Km. 30.5 Vía Perimetral, Guayaquil, Ecuador ; 2 ESPOCH Polytechnic University, South Pan-American Highway km 1 1/2, Riobamba, Ecuador ; 3 Computer Vision Center, Campus UAB, 08193 Bellaterra, Barcelona, Spain

Keyword(s): Multimodal, Fusion, Semantic Segmentation, Application-Driven.

Abstract: This paper proposes an enhanced application-driven image fusion framework to improve final application results. This framework is based on a deep learning architecture that generates fused images to better align with the requirements of applications such as semantic segmentation and object detection. The color-based and edge-weighted correlation loss functions are introduced to ensure consistency in the YCbCr space and emphasize structural integrity in high-gradient regions, respectively. Together, these loss components allow the fused image to retain more features from the source images by producing an application-ready fused image. Experiments conducted on two public datasets demonstrate a significant improvement in mIoU achieved by the proposed approach compared to state-of-the-art methods.

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Paper citation in several formats:
Bastidas-Guacho, G., Moreno-Vallejo, P., Vintimilla, B. and Sappa, A. (2025). Application-Guided Image Fusion: A Path to Improve Results in High-Level Vision Tasks. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3; ISSN 2184-4321, SciTePress, pages 178-187. DOI: 10.5220/0013307300003912

@conference{visapp25,
author={Gisel Bastidas{-}Guacho and Patricio Moreno{-}Vallejo and Boris Vintimilla and Angel Sappa},
title={Application-Guided Image Fusion: A Path to Improve Results in High-Level Vision Tasks},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={178-187},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013307300003912},
isbn={978-989-758-728-3},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Application-Guided Image Fusion: A Path to Improve Results in High-Level Vision Tasks
SN - 978-989-758-728-3
IS - 2184-4321
AU - Bastidas-Guacho, G.
AU - Moreno-Vallejo, P.
AU - Vintimilla, B.
AU - Sappa, A.
PY - 2025
SP - 178
EP - 187
DO - 10.5220/0013307300003912
PB - SciTePress

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