A local binary pattern based texture descriptors for classification of tea leaves

References

Page 1

	Year	Citations
Textural Features for Image Classification Robert M. Haralick, Karthikeyan Shanmugam, I. Dinstein IEEE Transactions on Systems Man and Cybernetics Image ClassificationImage AnalysisMachine VisionData ScienceTextural Features	1973	22.2K
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns Timo Ojala, Matti Pietikäinen, Topi Mäenpää IEEE Transactions on Pattern Analysis and Machine Intelligence Local Binary PatternsImage ClassificationMachine VisionImage AnalysisFeature Detection	2002	15.1K
Face Description with Local Binary Patterns: Application to Face Recognition Timo Ahonen, Abdenour Hadid, Matti Pietikäinen IEEE Transactions on Pattern Analysis and Machine Intelligence EngineeringMachine LearningFeature DetectionBiometricsFace Recognition	2006	5.6K
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions Guoying Zhao, Matti Pietikäinen IEEE Transactions on Pattern Analysis and Machine Intelligence EngineeringBiometricsFace DetectionFacial Recognition SystemImage Analysis	2007	2.8K
Description of interest regions with local binary patterns Marko Heikkilä, Matti Pietikäinen, Cordelia Schmid Pattern Recognition Local Binary PatternsMachine VisionImage AnalysisData ScienceEngineering	2008	1.2K
Dominant Local Binary Patterns for Texture Classification Shu Liao, Meng Law, Albert C. S. Chung IEEE Transactions on Image Processing Image RotationTexture ClassificationImage ClassificationImage AnalysisMachine Vision	2009	781
Textural Infornation in SAR Images F.T. Ulaby, F. Kouyate, Brian Brisco, IEEE Transactions on Geoscience and Remote Sensing Precision AgricultureEngineeringLand UseForestryLand Cover	1986	578
Local binary patterns variants as texture descriptors for medical image analysis Loris Nanni, Alessandra Lumini, Sheryl Brahnam Artificial Intelligence in Medicine Machine VisionFeature DetectionMedical ImagingImage AnalysisPattern Recognition	2010	494
Performance evaluation for four classes of textural features Philippe P. Ohanian, Richard C. Dubes Pattern Recognition Machine VisionImage AnalysisEngineeringPattern RecognitionDesign	1992	389
Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM) Quansheng Chen, Jiewen Zhao, Chao Fang, Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy Support Vector MachineInfrared SpectroscopySpectroscopyRemote SensingNear-infrared Reflectance Spectroscopy	2006	261

Page 1