Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Bayesian Modeling of Dynamic Scenes for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monocular Video Foreground/Background Segmentation by Tracking Spatial-Color Gaussian Mixture Models
WMVC '07 Proceedings of the IEEE Workshop on Motion and Video Computing
Robust Real-Time Bi-Layer Video Segmentation Using Infrared Video
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Fusing Time-of-Flight Depth and Color for Real-Time Segmentation and Tracking
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Improved video segmentation by adaptive combination of depth keying and mixture-of-gaussians
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Time-consistent foreground segmentation of dynamic content from color and depth video
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
A computer vision framework for finger-tapping evaluation in Parkinson's disease
Artificial Intelligence in Medicine
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In this paper we present a novel foreground segmentation system that combines color and depth sensors information to perform a more complete Bayesian segmentation between foreground and background classes. The system shows a combination of spatial-color and spatial-depth region-based models for the foreground as well as color and depth pixel-wise models for the background in a Logarithmic Opinion Pool decision framework used to correctly combine the likelihoods of each model. A posterior enhancement step based on a trimap analysis is also proposed in order to correct the precision errors that the depth sensor introduces. The results presented in this paper show that our system is robust in front of color and depth camouflage problems between the foreground object and the background, and also improves the segmentation in the area of the objects' contours by reducing the false positive detections that appear due to the lack of precision of the depth sensors.