Texture Measures for Carpet Wear Assessment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
A texture thesaurus for browsing large aerial photographs
Journal of the American Society for Information Science - Special topic issue: artificial intelligence techniques for emerging information systems applications
Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling, and Analog Implementation
Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling, and Analog Implementation
Pattern Recognition with Neural Network in C++
Pattern Recognition with Neural Network in C++
Wavelet-based rotational invariant roughness features for texture classification and segmentation
IEEE Transactions on Image Processing
Invariant texture classification for biomedical cell specimens via non-linear polar map filtering
Computer Vision and Image Understanding
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In the recent years, image processing and pattern recognition techniques have been applied to develop intelligent systems for both of fresh and fossil pollen grains discrimination. In this paper, we aim at the texture identification of pollen surface images. A method of texture description using wavelet transforms in combination with cooccurrence matrices is presented, and a neural network is used to classify the extracted image features. In this combined method, through wavelet decomposition and reconstruction, an approximation image and a new details image are generated for the input image. The surface texture of pollen grains is characterised by using a rotational invariant feature set, which is formed from the joint distribution of the grey level and the details information. In order to form effective feature vectors, the moment invariants also were employed to describe the surface shape of pollen grains. Both the back-propagation (BP) and the learning vector quantisation (LVQ) networks were used for classification of the resulting feature vectors. In experiments with sixteen types of airborne pollen grains, more than 91% pollen images are correctly classified using both the methods.