A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Statistical methods for speech recognition
Statistical methods for speech recognition
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Classification of Raw Textile Defects
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Defect detection in random colour textures using the MIA t2 defect maps
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Unsupervised texture segmentation using multichannel decomposition and hidden Markov models
IEEE Transactions on Image Processing
HMM-Based Defect Localization in Wire Ropes --- A New Approach to Unusual Subsequence Recognition
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns
Artificial Intelligence in Medicine
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In this paper a novel method for the purpose of random texture defect detection using a collection of 1-D HMMs is presented. The sound textural content of a sample of training texture images is first encoded by a compressed LBP histogram and then the local patterns of the input training textures are learned, in a multiscale framework, through a series of HMMs according to the LBP codes which belong to each bin of this compressed LBP histogram. The hidden states of these HMMs at different scales are used as a texture descriptor that can model the normal behavior of the local texture units inside the training images. The optimal number of these HMMs (models) is determined in an unsupervised manner as a model selection problem. Finally, at the testing stage, the local patterns of the input test image are first predicted by the trained HMMs and a prediction error is calculated for each pixel position in order to obtain a defect map at each scale. The detection results are then merged by an inter-scale post fusion method for novelty detection. The proposed method is tested with a database of grayscale ceramic tile images.