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International Journal of Reconfigurable Computing
Volume 2008, Article ID 428265, 9 pages
http://dx.doi.org/10.1155/2008/428265
Research Article

Neuromorphic Configurable Architecture for Robust Motion Estimation

1Department of Computer Architecture and Automation, Complutense University of Madrid, 28040 Madrid, Spain
2Department of Computer Architecture and Technology, University of Granada, 18071 Granada, Spain
3Department of Electronics and Computer Technology, University of Granada, 18071 Granada, Spain

Received 1 July 2008; Accepted 8 October 2008

Academic Editor: Gustavo Sutter

Copyright © 2008 Guillermo Botella et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The robustness of the human visual system recovering motion estimation in almost any visual situation is enviable, performing enormous calculation tasks continuously, robustly, efficiently, and effortlessly. There is obviously a great deal we can learn from our own visual system. Currently, there are several optical flow algorithms, although none of them deals efficiently with noise, illumination changes, second-order motion, occlusions, and so on. The main contribution of this work is the efficient implementation of a biologically inspired motion algorithm that borrows nature templates as inspiration in the design of architectures and makes use of a specific model of human visual motion perception: Multichannel Gradient Model (McGM). This novel customizable architecture of a neuromorphic robust optical flow can be constructed with FPGA or ASIC device using properties of the cortical motion pathway, constituting a useful framework for building future complex bioinspired systems running in real time with high computational complexity. This work includes the resource usage and performance data, and the comparison with actual systems. This hardware has many application fields like object recognition, navigation, or tracking in difficult environments due to its bioinspired and robustness properties.