Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gibbs random fields, fuzzy clustering, and the unsupervised segmentation of textured images
CVGIP: Graphical Models and Image Processing
Unsupervised segmentation of noisy and textured images using Markov random fields
CVGIP: Graphical Models and Image Processing
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Unsupervised Texture Segmentation in a Deterministic Annealing Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ant algorithms for discrete optimization
Artificial Life
IEEE Transactions on Image Processing
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Applying multiquadric quasi-interpolation for boundary detection
Computers & Mathematics with Applications
A stochastic gravitational approach to feature based color image segmentation
Engineering Applications of Artificial Intelligence
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This paper proposes a novel scheme for texture segmentation and representation based on Ant Colony Optimization (ACO). Texture segmentation and texture characteristic expression are two important areas in image pattern recognition. Nevertheless, until now, how to find an effective way for accomplishing these tasks is still a major challenge in practical applications such as iris image processing. We propose a framework for ACO based image processing methods. Considering the specific characteristics of various tasks, such a framework possesses the flexibility of only defining different criteria for ant behavior correspondingly. By defining different kinds of direction probability and movement difficulty for artificial ants, an ACO based image segmentation algorithm and a texture representation method are then presented for automatic iris image processing. Experimental results demonstrated that the ACO based image processing methods are competitive and quite promising, with excellent effectiveness and practicability especially for images with complex local texture situations.