Video segmentation for markerless motion capture in unconstrained environments

Authors:
Martin Côté;Pierre Payeur;Gilles Comeau
Affiliations:
School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada;School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada;Department of Music, University of Ottawa, Ottawa, Ontario, Canada
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Year:
2007

Citing 8
Cited 0

Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust analysis of feature spaces: color image segmentation

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Hybrid image segmentation using watersheds and fast region merging

IEEE Transactions on Image Processing
Adaptive perceptual color-texture image segmentation

IEEE Transactions on Image Processing
Unsupervised video segmentation based on watersheds and temporal tracking

IEEE Transactions on Circuits and Systems for Video Technology
Semiautomatic video object segmentation using VSnakes

IEEE Transactions on Circuits and Systems for Video Technology
A Bayesian approach to video object segmentation via merging 3-D watershed volumes

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Segmentation is a first and important step in video-based motion capture applications. A lack of constraints can make this process daunting and difficult to achieve. We propose a technique that makes use of an improved JSEG procedure in the context of markerless motion capture for performance evaluation of human beings in unconstrained environments. In the proposed algorithm a non-parametric clustering of image data is performed in order to produce homogenous colour-texture regions. The clusters are modified using soft - classifications and allow the J-Value segmentation to deal with smooth colour and lighting transitions. The regions are adapted using an original merging and video stack tracking algorithm.