loading
Documents

Research.Publish.Connect.

Paper

Authors: Adel Saleh 1 ; Hatem Rashwan 1 ; Mohamed Abdel-Nasser 2 ; Vivek Singh 1 ; Saddam Abdulwahab 1 ; Md. Sarker 1 ; Miguel Garcia 3 and Domenec Puig 1

Affiliations: 1 Department of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, Spain ; 2 Department of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, Spain, Electrical Engineering Department, Aswan University, 81542 Aswan, Egypt ; 3 Department of Electronic and Communications Technology, Autonomous University of Madrid, Madrid, Spain

ISBN: 978-989-758-354-4

Keyword(s): Semantic Segmentation, Fully Convolutional Network, Pixel-wise Classification, Finger Parts.

Abstract: Image semantic segmentation is in the center of interest for computer vision researchers. Indeed, huge number of applications requires efficient segmentation performance, such as activity recognition, navigation, and human body parsing, etc. One of the important applications is gesture recognition that is the ability to understanding human hand gestures by detecting and counting finger parts in a video stream or in still images. Thus, accurate finger parts segmentation yields more accurate gesture recognition. Consequently, in this paper, we highlight two contributions as follows: First, we propose data-driven deep learning pooling policy based on multi-scale feature maps extraction at different scales (called FinSeg). A novel aggregation layer is introduced in this model, in which the features maps generated at each scale is weighted using a fully connected layer. Second, with the lack of realistic labeled finger parts datasets, we propose a labeled dataset for finger parts segmentat ion (FingerParts dataset). To the best of our knowledge, the proposed dataset is the first attempt to build a realistic dataset for finger parts semantic segmentation. The experimental results show that the proposed model yields an improvement of 5% compared to the standard FCN network. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 100.26.182.28

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Saleh, A.; Rashwan, H.; Abdel-Nasser, M.; Singh, V.; Abdulwahab, S.; Sarker, M.; Garcia, M. and Puig, D. (2019). FinSeg: Finger Parts Semantic Segmentation using Multi-scale Feature Maps Aggregation of FCN.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 77-84. DOI: 10.5220/0007382100770084

@conference{visapp19,
author={Adel Saleh. and Hatem A. Rashwan. and Mohamed Abdel{-}Nasser. and Vivek K. Singh. and Saddam Abdulwahab. and Md. Mostafa Kamal Sarker. and Miguel Angel Garcia. and Domenec Puig.},
title={FinSeg: Finger Parts Semantic Segmentation using Multi-scale Feature Maps Aggregation of FCN},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={77-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007382100770084},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - FinSeg: Finger Parts Semantic Segmentation using Multi-scale Feature Maps Aggregation of FCN
SN - 978-989-758-354-4
AU - Saleh, A.
AU - Rashwan, H.
AU - Abdel-Nasser, M.
AU - Singh, V.
AU - Abdulwahab, S.
AU - Sarker, M.
AU - Garcia, M.
AU - Puig, D.
PY - 2019
SP - 77
EP - 84
DO - 10.5220/0007382100770084

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.