A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Genetic Algorithms for Scientists and Engineers
An Introduction to Genetic Algorithms for Scientists and Engineers
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Algorithms for Graphics and Imag
Algorithms for Graphics and Imag
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Digital Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
One-pixel-wide closed boundary identification
IEEE Transactions on Image Processing
Region growing: a new approach
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
Image segmentation and analysis via multiscale gradient watershed hierarchies
IEEE Transactions on Image Processing
Regions adjacency graph applied to color image segmentation
IEEE Transactions on Image Processing
A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering
IEEE Transactions on Image Processing
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
The strongest schema learning GA and its application to multilevel thresholding
Image and Vision Computing
Novel classification and segmentation techniques with application to remotely sensed images
Transactions on rough sets VII
Region merging for severe oversegmented images using a hierarchical social metaheuristic
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Hi-index | 0.00 |
Evolutionary image segmentation algorithms have a number of advantages such as continuous contour, non-oversegmentation, and non-thresholds. However, most of the evolutionary image segmentation algorithms suffer from long computation time because the number of encoding parameters is large. In this paper, design and analysis of an efficient evolutionary image segmentation algorithm EISA are proposed. EISA uses a K-means algorithm to split an image into many homogeneous regions, and then uses an intelligent genetic algorithm IGA associated with an effective chromosome encoding method to merge the regions automatically such that the objective of the desired segmentation can be effectively achieved, where IGA is superior to conventional genetic algorithms in solving large parameter optimization problems. High performance of EISA is illustrated in terms of both the evaluation performance and computation time, compared with some current segmentation methods. It is empirically shown that EISA is robust and efficient using nature images with various characteristics.