The theory and practice of Bayesian image labeling
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Region-based parametric motion segmentation using color information
Graphical Models and Image Processing
W4: Real-Time Surveillance of People and Their Activities
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Video Segmentation by MAP Labeling of Watershed Segments
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Detection and Location of People in Video Images Using Adaptive Fusion of Color and Edge Information
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Effective Gaussian Mixture Learning for Video Background Subtraction
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Moving Cast Shadow Detection from a Gaussian Mixture Shadow Model
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A Dynamic Conditional Random Field Model for Foreground and Shadow Segmentation
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A Texture-Based Method for Modeling the Background and Detecting Moving Objects
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Image segmentation in video sequences: a probabilistic approach
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Markovian framework for foreground-background-shadow separation of real world video scenes
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Simultaneous motion estimation and segmentation
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Motion segmentation by multistage affine classification
IEEE Transactions on Image Processing
Spatiotemporal video segmentation based on graphical models
IEEE Transactions on Image Processing
Region-Level Motion-Based Background Modeling and Subtraction Using MRFs
IEEE Transactions on Image Processing
Fast and automatic video object segmentation and tracking for content-based applications
IEEE Transactions on Circuits and Systems for Video Technology
Efficient moving object segmentation algorithm using background registration technique
IEEE Transactions on Circuits and Systems for Video Technology
Automatic segmentation of moving objects in video sequences: a region labeling approach
IEEE Transactions on Circuits and Systems for Video Technology
On using hierarchical motion history for motion estimation in H.264/AVC
IEEE Transactions on Circuits and Systems for Video Technology
Signal Processing
Real-time video surveillance on an embedded, programmable platform
Microprocessors & Microsystems
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This paper presents a probabilistic approach for automatically segmenting foreground objects from a video sequence. In order to save computation time and be robust to noise effects, a region detection algorithm incorporating edge information is first proposed to identify the regions of interest, within which the spatial relationships are represented by a region adjacency graph. Next, we consider the motion of the foreground objects and, hence, utilize the temporal coherence property in the regions detected. Thus, the foreground segmentation problem is formulated as follows. Given two consecutive image frames and the segmentation result priorly obtained, we simultaneously estimate the motion vector field and the foreground segmentation mask in a mutually supporting manner by maximizing the conditional joint probability density function of these two elements. To represent the conditional joint probability density function in a compact form, a Bayesian network is adopted, which is derived to model the interdependency of these two elements. Experimental results for several video sequences are provided to demonstrate the effectiveness of the proposed approach.