Paper Info
Title
A neuron-inspired computational architecture for spatiotemporal visual processing: real-time visual sensory integration for humanoid robots.
Abstract
In this article, we present a neurologically motivated computational architecture for visual information processing. The computational architecture's focus lies in multiple strategies: hierarchical processing, parallel and concurrent processing, and modularity. The architecture is modular and expandable in both hardware and software, so that it can also cope with multisensory integrations - making it an ideal tool for validating and applying computational neuroscience models in real time under real-world conditions. We apply our architecture in real time to validate a long-standing biologically inspired visual object recognition model, HMAX. In this context, the overall aim is to supply a humanoid robot with the ability to perceive and understand its environment with a focus on the active aspect of real-time spatiotemporal visual processing. We show that our approach is capable of simulating information processing in the visual cortex in real time and that our entropy-adaptive modification of HMAX has a higher efficiency and classification performance than the standard model (up to similar to+6 %).
Year
DOI
Venue
2014
10.1007/s00422-014-0597-3
BIOLOGICAL CYBERNETICS
Keywords
DocType
Volume
Concurrent information processing,Visual Object Recognition,Biologically-inspired computational architecture,Online image processing
Journal
108.0
Issue
ISSN
Citations 
3
0340-1200
0
PageRank 
References 
Authors
0.34
13
2
Name
Order
Citations
PageRank
Andreas Holzbach1936.29
Gordon Cheng21250115.33