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Authors: Gibran Benitez-Garcia 1 and Hiroki Takahashi 1 ; 2 ; 3

Affiliations: 1 Graduate School of Informatics and Engineering, The University of Electro-Communications, Japan ; 2 Artificial Intelligence eXploration Research Center (AIX), The University of Electro-Communications, Japan ; 3 Meta-Networking Research Center (MEET), The University of Electro-Communications, Japan

Keyword(s): Semantic Segmentation, Finger Segmentation, DDRNet, Real-Time CNN, IPN-Hand Dataset.

Abstract: Semantic segmentation at the finger level poses unique challenges, including the limited pixel representation of some classes and the complex interdependency of the hand anatomy. In this paper, we propose FingerSeg, a novel architecture inspired by Deep Dual-Resolution Networks, specifically adapted to address the nuances of finger-level hand semantic segmentation. To this end, we introduce three modules: Enhanced Bilateral Fusion (EBF), which refines low- and high-resolution feature fusion via attention mechanisms; Multi-Attention Module (MAM), designed to augment high-level features with a composite of channel, spatial, orientational, and categorical attention; and Asymmetric Dilated Up-sampling (ADU), which combines standard and asymmetric atrous convolutions to capture rich contextual information for pixel-level classification. To properly evaluate our proposal, we introduce IPN-finger, a subset of the IPN-Hand dataset, manually annotated pixel-wise for 13 finger-related classes. Our extensive empirical analysis, including evaluations of the synthetic RHD dataset against current state-of-the-art methods, demonstrates that our proposal achieves top results. FingerSeg reaches 73.8 and 71.1 mIoU on the IPN-Finger and RHD datasets, respectively, while maintaining an efficient computational cost of about 7 GFLOPs and 6 million parameters at VGA resolution. The dataset, source code, and a demo of FingerSeg will be available upon the publication of this paper. (More)

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Paper citation in several formats:
Benitez-Garcia, G. and Takahashi, H. (2024). FingerSeg: Highly-Efficient Dual-Resolution Architecture for Precise Finger-Level Semantic Segmentation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 242-251. DOI: 10.5220/0012575000003660

@conference{visapp24,
author={Gibran Benitez{-}Garcia. and Hiroki Takahashi.},
title={FingerSeg: Highly-Efficient Dual-Resolution Architecture for Precise Finger-Level Semantic Segmentation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={242-251},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012575000003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - FingerSeg: Highly-Efficient Dual-Resolution Architecture for Precise Finger-Level Semantic Segmentation
SN - 978-989-758-679-8
IS - 2184-4321
AU - Benitez-Garcia, G.
AU - Takahashi, H.
PY - 2024
SP - 242
EP - 251
DO - 10.5220/0012575000003660
PB - SciTePress