Search results for "texture classification"

showing 2 items of 2 documents

Analysis of HMAX Algorithm on Black Bar Image Dataset

2020

An accurate detection and classification of scenes and objects is essential for interacting with the world, both for living beings and for artificial systems. To reproduce this ability, which is so effective in the animal world, numerous computational models have been proposed, frequently based on bioinspired, computational structures. Among these, Hierarchical Max-pooling (HMAX) is probably one of the most important models. HMAX is a recognition model, mimicking the structures and functions of the primate visual cortex. HMAX has already proven its effectiveness and versatility. Nevertheless, its computational structure presents some criticalities, whose impact on the results has never been…

Computer Networks and CommunicationsComputer sciencelcsh:TK7800-8360Context (language use)02 engineering and technologySet (abstract data type)03 medical and health sciences0302 clinical medicineGabor filterBBIDEncoding (memory)0202 electrical engineering electronic engineering information engineeringElectrical and Electronic EngineeringModularity (networks)Contextual image classificationbusiness.industrylcsh:ElectronicsPattern recognitioncomputational modelBlack Bar Image DatasetHardware and ArchitectureControl and Systems EngineeringHMAXSignal Processingtexture classification020201 artificial intelligence & image processingArtificial intelligencerecognitionbusiness030217 neurology & neurosurgeryimage classificationElectronics
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Development of an Automatic Pollen Classification System Using Shape, Texture and Aperture Features

2015

International audience; Automatic detection and classification of pollen species has value for use inside of palynologic allergen studies. Traditional labeling of different pollen species requires an expert biologist to classify particles by sight, and is therefore time-consuming and expensive. Here, an automatic process is developed which segments the particle contour and uses the extracted features for the classification process. We consider shape features, texture features and aperture features and analyze which are useful. The texture features analyzed include: Gabor Filters, Fast Fourier Transform, Local Binary Patterns, Histogram of Oriented Gradients, and Haralick features. We have s…

Texture classification[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Image processingMachine learningComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONPollen[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
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