Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Experiments in the machine interpretation of visual motion
Experiments in the machine interpretation of visual motion
Digital video processing
Markov random field modeling in computer vision
Markov random field modeling in computer vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Genetic Algorithms for Pattern Recognition
Genetic Algorithms for Pattern Recognition
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
Region-based representations of image and video: segmentation tools for multimedia services
IEEE Transactions on Circuits and Systems for Video Technology
Genetic algorithm-based reconstruction of old films corrupted by scratches and blotches
Pattern Recognition Letters
Research on genetic segmentation and recognition algorithms
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Moving object detection using Markov Random Field and Distributed Differential Evolution
Applied Soft Computing
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The current paper presents a new genetic algorithm (GA)-based method for video segmentation. The proposed method is specifically designed to enhance the computational efficiency and quality of the segmentation results compared to standard GAs. The segmentation is performed by chromosomes that independently evolve using distributed genetic algorithms (DGAs). However, unlike conventional DGAs, the chromosomes are initiated using the segmentation results of the previous frame, instead of random values. Thereafter, only unstable chromosomes corresponding to moving object parts are evolved by crossover and mutation. As such, these mechanisms allow for effective solution space exploration and exploitation, thereby improving the performance of the proposed method in terms of speed and segmentation quality. These advantages were confirmed based on experiments where the proposed method was successfully applied to both synthetic and natural video sequences.