Data dependent filters for the edge enhancement of Landsat images
Computer Vision, Graphics, and Image Processing
Texture Measures for Carpet Wear Assessment
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Texture analysis and discrimination in additive noise
Computer Vision, Graphics, and Image Processing
Automatic threshold selection based on histogram modes and a discriminant criterion
Machine Vision and Applications
Statistical Control by Monitoring and Feedback Adjustment
Statistical Control by Monitoring and Feedback Adjustment
Filter-based feature selection for rail defect detection
Machine Vision and Applications
Natural Image Statistics for Natural Image Segmentation
International Journal of Computer Vision
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A random set view of texture classification
IEEE Transactions on Image Processing
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Quality control using continuous monitoring from images is emerging as an active research area. These applications require adaptive statistical techniques in order to detect and isolate process abnormalities. A novel approach is introduced for monitoring schemes in the setting of image data when the quality is associated with uniform pixel gray-scales. The proposed approach requires the definition of a statistic which takes into account both the spatial dependency and the changes in local variability. An application on paper surface demonstrates how the monitoring scheme performs in practical applications.