research-article

Active-GNG: model acquisition and tracking in cluttered backgrounds

Authors:

Anastassia Angelopoulou,

Alexandra Psarrou,

José Garcia Rodriguez,

Gaurav GuptaAuthors Info & Claims

VNBA '08: Proceedings of the 1st ACM workshop on Vision networks for behavior analysis

Pages 17 - 22

https://doi.org/10.1145/1461893.1461897

Published: 31 October 2008 Publication History

Abstract

The Self-Organising Artificial Neural Network Models, of which we have used the Growing Neural Gas (GNG) can be applied to preserve the topology of an input space. Traditionally these models neither do include local adaptation of the nodes nor colour information. In this paper, we extend GNG by adding an active step to the network, which we call Active-Growing Neural Gas (A-GNG) that has both global and local properties and can track in cluttered backgrounds. The approach is novel in that the topological relations of the model are based on a number of attributes (e.g. global and local transformations, mapping function and skin colour information) which allow us to automatically model and track 2D gestures. To measure the quality of the tracked correspondences we use two interlinked topology preservation measures. Experimental results have shown better performance of our proposed method over the original GNG and the Active Contour Model.

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Cited By

Ramík DSabourin CMoreno RMadani K(2014)A machine learning based intelligent vision system for autonomous object detection and recognitionApplied Intelligence10.1007/s10489-013-0461-540:2(358-375)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s10489-013-0461-5
Gupta GPsarrou AAngelopoulou AGarcia-Rodriguez J(2012)Region analysis through close contour transformation using growing neural gasThe 2012 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2012.6252764(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/IJCNN.2012.6252764
Moreno RGraña M(2012)A hybrid gradient for n-dimensional images through hyperspherical coordinatesProceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II10.1007/978-3-642-28931-6_39(404-415)Online publication date: 28-Mar-2012
https://dl.acm.org/doi/10.1007/978-3-642-28931-6_39

Index Terms

Active-GNG: model acquisition and tracking in cluttered backgrounds
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Computing methodologies

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cover image ACM Conferences

VNBA '08: Proceedings of the 1st ACM workshop on Vision networks for behavior analysis

October 2008

116 pages

ISBN:9781605583136

DOI:10.1145/1461893

General Chairs:
Hamid Aghajan
Stanford University, USA
,
Andrea Prati
University of Modena and Reggio Emilia, Italy

Copyright © 2008 ACM.

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Published: 31 October 2008

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October 31, 2008

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Cited By

Ramík DSabourin CMoreno RMadani K(2014)A machine learning based intelligent vision system for autonomous object detection and recognitionApplied Intelligence10.1007/s10489-013-0461-540:2(358-375)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1007/s10489-013-0461-5
Gupta GPsarrou AAngelopoulou AGarcia-Rodriguez J(2012)Region analysis through close contour transformation using growing neural gasThe 2012 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2012.6252764(1-8)Online publication date: Jun-2012
https://doi.org/10.1109/IJCNN.2012.6252764
Moreno RGraña M(2012)A hybrid gradient for n-dimensional images through hyperspherical coordinatesProceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II10.1007/978-3-642-28931-6_39(404-415)Online publication date: 28-Mar-2012
https://dl.acm.org/doi/10.1007/978-3-642-28931-6_39

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