Robust bioinspired architecture for optical-flow computation

Authors:
Guillermo Botella;Antonio García;Manuel Rodríguez-Álvarez;Eduardo Ros;Uwe Meyer-Baese;María C. Molina
Affiliations:
Department of Computer Architecture and Automation, Complutense University of Madrid, Madrid, Spain;Department of Electronics and Computer Technology, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, University of Granada, Granada, Spain;Department of Computer Architecture and Technology, University of Granada, Granada, Spain;Department of Electrical and Computer Engineering, College of Engineering, Florida A&M University-Florida State University, Tallahassee, FL;Department of Computer Architecture and Automation, Complutense University of Madrid, Madrid, Spain
Venue:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Year:
2010

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Abstract

Motion estimation from image sequences, called optical flow, has been deeply analyzed by the scientific community. Despite the number of different models and algorithms, none of them covers all problems associated with real-world processing. This paper presents a novel customizable architecture of a neuromorphic robust optical flow (multichannel gradient model) based on reconfigurable hardware with the properties of the cortical motion pathway, thus obtaining a useful framework for building future complex bioinspired real-time systems with high computational complexity. The presented architecture is customizable and adaptable, while emulating several neuromorphic properties, such as the use of several information channels of small bit width, which is the nature of the brain. This paper includes the resource usage and performance data, as well as a comparison with other systems. This hardware platform has many application fields in difficult environments due to its bioinspired nature and robustness properties, and it can be used as starting point in more complex systems.