6533b825fe1ef96bd1283276

RESEARCH PRODUCT

Analysis of HMAX Algorithm on Black Bar Image Dataset

Olivier BoisardMichel PaindavoineAlessandro Carlini

subject

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 classification

description

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 systematically assessed. Traditional assessments based on photographs force to choose a specific context

yearjournalcountryeditionlanguage
2020-03-28Electronics
https://doi.org/10.3390/electronics9040567