Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random signal processing
Data Mining and Knowledge Discovery for Process Monitoring and Control
Data Mining and Knowledge Discovery for Process Monitoring and Control
Texture classification using wavelet transform
Pattern Recognition Letters
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Wavelet-based defect detection in solar wafer images with inhomogeneous texture
Pattern Recognition
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Several continuous manufacturing processes use stochastic texture images for quality control and monitoring. Large amounts of pictorial data are acquired, providing both important information about the materials produced and about the manufacturing processes involved. However, it is often difficult to measure objectively the similarity among such images, or to discriminate between texture images of materials with distinct properties. The degree of discrimination required by industrial processes sometimes goes beyond the limits of human visual perception. This work presents a new method for multi-resolution stochastic texture analysis, interpretation and discrimination based on the wavelet transform. A multi-resolution distance measure for stochastic textures is proposed, and applications of our method in the non-woven textiles industry are reported. The conclusions include ideas for future work.