Digital video processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Hybrid Evolutionary Search Method Based on Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Handbook of Image and Video Processing
Handbook of Image and Video Processing
Video sequence segmentation using genetic algorithms
Pattern Recognition Letters
Journal of Global Optimization
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Automatic video segmentation using genetic algorithms
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
Detection of object motion regions in aerial image pairs with a multilayer Markovian model
IEEE Transactions on Image Processing
Genetic algorithms for video segmentation
Pattern Recognition
Differential evolution algorithm with ensemble of parameters and mutation strategies
Applied Soft Computing
Self-adaptive randomized and rank-based differential evolution for multimodal problems
Journal of Global Optimization
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators
IEEE Transactions on Evolutionary Computation
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-Level Motion-Based Background Modeling and Subtraction Using MRFs
IEEE Transactions on Image Processing
Image Segmentation Using Hidden Markov Gauss Mixture Models
IEEE Transactions on Image Processing
Region-based representations of image and video: segmentation tools for multimedia services
IEEE Transactions on Circuits and Systems for Video Technology
Multifeature Object Trajectory Clustering for Video Analysis
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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In this article, we present an algorithm for detecting moving objects from a given video sequence. Here, spatial and temporal segmentations are combined together to detect moving objects. In spatial segmentation, a multi-layer compound Markov Random Field (MRF) is used which models spatial, temporal, and edge attributes of image frames of a given video. Segmentation is viewed as a pixel labeling problem and is solved using the maximum a posteriori (MAP) probability estimation principle; i.e., segmentation is done by searching a labeled configuration that maximizes this probability. We have proposed using a Differential Evolution (DE) algorithm with neighborhood-based mutation (termed as Distributed Differential Evolution (DDE) algorithm) for estimating the MAP of the MRF model. A window is considered over the entire image lattice for mutation of each target vector of the DDE; thereby enhancing the speed of convergence. In case of temporal segmentation, the Change Detection Mask (CDM) is obtained by thresholding the absolute differences of the two consecutive spatially segmented image frames. The intensity/color values of the original pixels of the considered current frame are superimposed in the changed regions of the modified CDM to extract the Video Object Planes (VOPs). To test the effectiveness of the proposed algorithm, five reference and one real life video sequences are considered. Results of the proposed method are compared with four state of the art techniques and provide better spatial segmentation and better identification of the location of moving objects.