research-article

Convexity local contour sequences for gesture recognition

Authors:

Pablo V. A. Barros,

Nestor T. M. Junior,

Juvenal M. M. Bisneto,

Bruno J. T. Fernandes,

Byron L. D. Bezerra,

Sergio M. M. FernandesAuthors Info & Claims

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

Pages 34 - 39

https://doi.org/10.1145/2480362.2480371

Published: 18 March 2013 Publication History

Abstract

Algorithms for hand feature extraction used in gesture recognition systems have some problems such as unnecessary information gathering. This paper proposes a novel method for feature extraction in gesture recognition systems based on the Local Contour Sequence (LCS). It is called the Convexity Local Contour Sequence (CLCS) and represents the hand shape only with the most significant information. This generates a smaller output result, but capable to model an entire dynamic gesture. It is used to classify dynamic gestures with an Elman Recurrent Network and Hidden Markov Model and presents a better result compared to regular LCS.

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Cited By

Richhariya PKane LChauhan PNigam M(2020)A Survey: Movement of the Hand motion Trajectory for dependent and independent Recognition2nd International Conference on Data, Engineering and Applications (IDEA)10.1109/IDEA49133.2020.9170698(1-6)Online publication date: Feb-2020
https://doi.org/10.1109/IDEA49133.2020.9170698
Neiva DZanchettin C(2016)A Dynamic Gesture Recognition System to Translate between Sign Languages in Complex Backgrounds2016 5th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2016.082(421-426)Online publication date: Oct-2016
https://doi.org/10.1109/BRACIS.2016.082
Santos DFernandes BBezerra B(2015)HAGR-D: A Novel Approach for Gesture Recognition with Depth MapsSensors10.3390/s15112864615:11(28646-28664)Online publication date: 12-Nov-2015
https://doi.org/10.3390/s151128646
Show More Cited By

Index Terms

Convexity local contour sequences for gesture recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
      2. Computer vision tasks
        Scene understanding
  2. Computer graphics
    1. Image manipulation

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cover image ACM Conferences

SAC '13: Proceedings of the 28th Annual ACM Symposium on Applied Computing

March 2013

2124 pages

ISBN:9781450316569

DOI:10.1145/2480362

Conference Chairs:
Sung Y. Shin
South Dakota State University, United States
,
José Carlos Maldonado
ICMC - University of São Paulo, Brazil

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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Association for Computing Machinery

New York, NY, United States

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Published: 18 March 2013

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SAC '13: SAC '13

March 18 - 22, 2013

Coimbra, Portugal

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SAC '13 Paper Acceptance Rate 255 of 1,063 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Richhariya PKane LChauhan PNigam M(2020)A Survey: Movement of the Hand motion Trajectory for dependent and independent Recognition2nd International Conference on Data, Engineering and Applications (IDEA)10.1109/IDEA49133.2020.9170698(1-6)Online publication date: Feb-2020
https://doi.org/10.1109/IDEA49133.2020.9170698
Neiva DZanchettin C(2016)A Dynamic Gesture Recognition System to Translate between Sign Languages in Complex Backgrounds2016 5th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2016.082(421-426)Online publication date: Oct-2016
https://doi.org/10.1109/BRACIS.2016.082
Santos DFernandes BBezerra B(2015)HAGR-D: A Novel Approach for Gesture Recognition with Depth MapsSensors10.3390/s15112864615:11(28646-28664)Online publication date: 12-Nov-2015
https://doi.org/10.3390/s151128646
Santos DNeto RFernandes BBezerra B(2015)FSSGR: Feature Selection System to Dynamic Gesture RecognitionNew Trends in Image Analysis and Processing -- ICIAP 2015 Workshops10.1007/978-3-319-23222-5_29(234-241)Online publication date: 21-Aug-2015
https://doi.org/10.1007/978-3-319-23222-5_29
Zhong JCanamero L(2014)From continuous affective space to continuous expression space: Non-verbal behaviour recognition and generation4th International Conference on Development and Learning and on Epigenetic Robotics10.1109/DEVLRN.2014.6982957(75-80)Online publication date: Oct-2014
https://doi.org/10.1109/DEVLRN.2014.6982957
Gilman ADong THupman KStockin KPawley M(2013)Dolphin fin pose correction using ICP in application to photo-identification2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013)10.1109/IVCNZ.2013.6727046(388-393)Online publication date: Nov-2013
https://doi.org/10.1109/IVCNZ.2013.6727046
Júnior NBarros PFernandes BBezerra BFernandes S(2013)A Dynamic Gesture Prediction System Based on the CLCS Feature ExtractionProceedings of the 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence10.1109/BRICS-CCI-CBIC.2013.89(501-506)Online publication date: 8-Sep-2013
https://dl.acm.org/doi/10.1109/BRICS-CCI-CBIC.2013.89

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