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Threshold logic based low-level vision sparse object features

International Journal of Intelligent Computing and Cybernetics 2016年第4期9卷 314-324页

作者： Nitha Thomas Joshin John Mathew Alex James Department of Research EnviewTrivandrumIndia ARS Traffic and Transport Technology TrivandrumIndia School of Engineering Nazarbayev UniversityAstanaKazakhstan

Purpose-The real-time generation of feature descriptors for object recognition is a challenging *** this research,the purpose of this paper is to provide a hardware friendly framework to generate sparse features that can be useful for key feature point selection,feature extraction,and descriptor *** inspiration is drawn from feature formation processes of the human brain,taking into account the sparse,modular,and hierarchical processing of visual ***/methodology/approach-A sparse set of neurons referred as active neurons determines the feature points necessary for high-level vision applications such as object recognition.A psycho-physical mechanism of human low-level vision relates edge detection to noticeable local spatial stimuli,representing this set of active neurons.A cognitive memory cell array-based implementation of low-level vision is *** of memory cell in edge detection are used for realizing human vision inspired feature selection and leading to feature vector construction for high-level vision ***-True parallel architecture and faster response of cognitive circuits avoid time costly and redundant feature extraction *** of proposed feature vector toward high-level computer vision applications is demonstrated using standard object recognition *** comparison against existing state-of-the-art object recognition features and methods shows an accuracy of 97,95,69 percent for Columbia Object Image Library-100,ALOI,and PASCAL VOC 2007 databases indicating an increase from benchmark methods by 5,3 and 10 percent,***/value-A hardware friendly low-level sparse edge feature processing system isproposed for recognizing *** edge features are developed based on threshold logic of neurons,and the sparse selection of the features applies a modular and hierarchical processing inspired from the human neural system.

关键词： Object recognition Object features Sparse features Threshold logic Low-level vision Active neurons cognitive memory-cell array Edge detection Feature description

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