Efficient Neural Models for Visual Attention View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Sylvain Chevallier , Nicolas Cuperlier , Philippe Gaussier

ABSTRACT

Human vision rely on attention to select only a few regions to process and thus reduce the complexity and the processing time of visual task. Artificial vision systems can benefit from a bio-inspired attentional process relying on neural models. In such applications, what is the most efficient neural model: spiked-based or frequency-based? We propose an evaluation of both neural model, in term of complexity and quality of results (on artificial and natural images). More... »

PAGES

257-264

References to SciGraph publications

Book

TITLE

Computer Vision and Graphics

ISBN

978-3-642-15909-1
978-3-642-15910-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-15910-7_29

DOI

http://dx.doi.org/10.1007/978-3-642-15910-7_29

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1015750169


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