Digital video processing
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Matching Shape Sequences in Video with Applications in Human Movement Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic video segmentation using genetic algorithms
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Carried object detection using ratio histogram and its application to suspicious event analysis
IEEE Transactions on Circuits and Systems for Video Technology
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Circuits and Systems for Video Technology
A Change Information Based Fast Algorithm for Video Object Detection and Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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This article addresses a problem of moving object detection by combining two kinds of segmentation schemes: temporal and spatial. It has been found that consideration of a global thresholding approach for temporal segmentation, where the threshold value is obtained by considering the histogram of the difference image corresponding to two frames, does not produce good result for moving object detection. This is due to the fact that the pixels in the lower end of the histogram are not identified as changed pixels (but they actually correspond to the changed regions). Hence there is an effect on object background classification. In this article, we propose a local histogram thresholding scheme to segment the difference image by dividing it into a number of small non-overlapping regions/windows and thresholding each window separately. The window/block size is determined by measuring the entropy content of it. The segmented regions from each window are combined to find the (entire) segmented image. This thresholded difference image is called the change detection mask (CDM) and represent the changed regions corresponding to the moving objects in the given image frame. The difference image is generated by considering the label information of the pixels from the spatially segmented output of two image frames. We have used a Markov Random Field (MRF) model for image modeling and the maximum a posteriori probability (MAP) estimation (for spatial segmentation) is done by a combination of simulated annealing (SA) and iterated conditional mode (ICM) algorithms. It has been observed that the entropy based adaptive window selection scheme yields better results for moving object detection with less effect on object background (mis) classification. The effectiveness of the proposed scheme is successfully tested over three video sequences.