A manifold representation as common basis for action production and recognition

Authors:
Jan Steffen;Michael Pardowitz;Helge Ritter
Affiliations:
Neuroinformatics Group, Faculty of Technology, Bielefeld University, Germany;Neuroinformatics Group, Faculty of Technology, Bielefeld University, Germany;Neuroinformatics Group, Faculty of Technology, Bielefeld University, Germany
Venue:
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Year:
2009

Citing 3
Cited 1

Principal Surfaces from Unsupervised Kernel Regression

IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models

The Journal of Machine Learning Research
Topologically-constrained latent variable models

Proceedings of the 25th international conference on Machine learning

Covariance Matrix Self-Adaptation and Kernel Regression - Perspectives of Evolutionary Optimization in Kernel Machines

Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we first review our previous work in the domain of dextrous manipulation, where we introduced Manipulation Manifolds - a highly structured manifold representation of hand postures which lends itself to simple and robust manipulation control schemes. Coming from this scenario, we then present our idea of how this generative system can be naturally extended to the recognition and segmentation of the represented movements providing the core representation for a combined system for action production and recognition.