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)
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Frame difference energy image for gait recognition with incomplete silhouettes
Pattern Recognition Letters
MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
Accurate Silhouettes for Surveillance - Improved Motion Segmentation Using Graph Cuts
DICTA '10 Proceedings of the 2010 International Conference on Digital Image Computing: Techniques and Applications
Silhouette Segmentation in Multiple Views
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The main motivation of this work is to find and evaluate solutions for generating binary masks (silhouettes) of foreground targets in an automatic way. To this end, four renowned unsupervised image segmentation algorithms are applied to foreground segmentation. A comparison among these algorithms is carried out using the MuHAVi dataset of multi-camera human action video sequences. This dataset presents significant challenges in terms of harsh illumination resulting for example in high contrast and deep shadows. The segmentation results have been objectively evaluated against manually derived ground-truth silhouettes.