Tracking gestures using a probabilistic Self-Organising network

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 distribution. Traditionally these models neither do include local adaptation of the nodes nor colour information. In this paper, we present an extension to the original growing neural gas network that has probabilistic features and can be applied to preserve the topology of a non-stationary distribution. The network consists of the geometrical position of the nodes, the underline local feature structure of the image, and the distance vector between the modal image and any successive images. Accurate correspondence of the nodes between successive images, is measured through the calculation of the topographic product. The method performs continuously mapping over a distribution that changes over time and works with both smooth and abrupt changes. The method is successfully applied to object modelling and tracking.

[1]  Timothy F. Cootes,et al.  A minimum description length approach to statistical shape modeling , 2002, IEEE Transactions on Medical Imaging.

[2]  Xiang Cao,et al.  Video shot motion characterization based on hierarchical overlapped growing neural gas networks , 2003, Multimedia Systems.

[3]  Rhodri H. Davies,et al.  Learning Shape: Optimal Models for Analysing Natural Variability , 2004 .

[4]  Teuvo Kohonen,et al.  In: Self-organising Maps , 1995 .

[5]  David C. Hogg,et al.  Learning Flexible Models from Image Sequences , 1994, ECCV.

[6]  Antonios Atsalakis,et al.  Hand Gesture Recognition Via a New Self-organized Neural Network , 2005, CIARP.

[7]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[8]  José García Rodríguez,et al.  Geodesic Topographic Product: An Improvement to Measure Topology Preservation of Self-Organizing Neural Networks , 2004, IBERAMIA.

[9]  Bernd Fritzke,et al.  A Self-Organizing Network that Can Follow Non-stationary Distributions , 1997, ICANN.

[10]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[11]  James S. Duncan,et al.  Parametrically deformable contour models , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Nicholas Ayache,et al.  Fast segmentation, tracking, and analysis of deformable objects , 1993, 1993 (4th) International Conference on Computer Vision.

[13]  Shen Furao,et al.  An incremental network for on-line unsupervised classification and topology learning , 2006, Neural Networks.

[14]  Stefan Schaal,et al.  Nonparametric Regression for Learning , 1994 .

[15]  Michael I. Mandel,et al.  Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation , 2004, NIPS.

[16]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[17]  Thierry Fraichard,et al.  A Novel Self Organizing Network to Perform Fast Moving Object Extraction from Video Streams , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Anil K. Jain,et al.  Automatic Construction of 2D Shape Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Klaus Pawelzik,et al.  Quantifying the neighborhood preservation of self-organizing feature maps , 1992, IEEE Trans. Neural Networks.

[20]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[21]  Thomas Martinetz,et al.  Topology representing networks , 1994, Neural Networks.

[22]  Christopher J. Taylor,et al.  A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Alan L. Yuille,et al.  Feature extraction from faces using deformable templates , 2004, International Journal of Computer Vision.

[24]  Pragya Agarwal,et al.  Self-Organising Maps , 2008 .

[25]  Michael Isard,et al.  Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking , 2000, ECCV.

[26]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[27]  Hervé Frezza-Buet,et al.  Following non-stationary distributions by controlling the vector quantization accuracy of a growing neural gas network , 2008, Neurocomputing.