Local linear transforms for texture measurements
Signal Processing
Segmentation of textured images using Gibbs random fields
Computer Vision, Graphics, and Image Processing
Unsupervised textured image segmentation using feature smoothing probabilistic relaxation techniques
Computer Vision, Graphics, and Image Processing
Digital Picture Processing
Digital Image Processing and Analysis
Digital Image Processing and Analysis
Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Random Fields Via Borrowed Strength Density Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On an Asymptotically Optimal Adaptive Classifier Design Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine vision for the inspection of natural products
Pattern Recognition Letters
Segmentation of kidney from ultrasound B-mode images with texture-based classification
Computer Methods and Programs in Biomedicine
Object density-based image segmentation and its applications in biomedical image analysis
Computer Methods and Programs in Biomedicine
Evolving descriptors for texture segmentation
Pattern Recognition
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Gradual land cover change detection based on multitemporal fraction images
Pattern Recognition
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
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A description is given of a supervised textured image segmentation algorithm that provides improved segmentation results. An improved method for extracting textured energy features in the feature extraction stage is described. It is based on an adaptive noise smoothing concept that takes the nonstationary nature of the problem into account. Texture energy features are first estimated using a window of small size to reduce the possibility of mixing statistics along region borders. The estimated texture energy feature values are smoothed by a quadrant filtering method to reduce the variability of the estimates while retaining the region border accuracy. The estimated feature values of each pixel are used by a Bayes classifier to make an initial probabilistic labeling. The spatial constraints are enforced through the use of a probabilistic relaxation algorithm. Two probabilistic relaxation algorithms are investigated. Limiting the probability labels by probability threshold is proposed. The tradeoff between efficiency and degradation of performed is studied.