Texture Measures for Carpet Wear Assessment
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Feature Extraction and Selection for Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Floating search methods in feature selection
Pattern Recognition Letters
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer and Robot Vision
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Using active learning to annotate microscope images of parasite eggs
Artificial Intelligence Review
The use of texture for image classification of black & white air photographs
International Journal of Remote Sensing
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People are interested in the composition of honeybee pollen due to its nutritional value and therapeutic benefits. Its palynological composition depends on the local flora surrounding the beehive, and its identification is currently done manually using optical microscopy. This procedure is tedious and expensive in systematic application and is unable to automatically separate pollen loads of different species of plants. We present an automatic methodology to discriminate pollen loads based on texture image classification. Texture features are generated using a multiscale filtering scheme. A statistical evaluation of the algorithm is provided and discussed.