2003 Fiscal Year Final Research Report Summary
Massively Parallel Intelligent Preprocessing
| Project/Area Number |
13023205
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| Research Category |
Grant-in-Aid for Scientific Research on Priority Areas
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| Allocation Type | Single-year Grants |
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| Review Section |
Science and Engineering
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| Research Institution | Toyohashi University of Technology |
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Principal Investigator |
YONEZU Hiroo Toyohashi University of Technology, Dept. of Electrical and Electronic Engineering, Professor, 工学部, 教授 (90191668)
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| Co-Investigator(Kenkyū-buntansha) |
FURUKAWA Yuzo Toyohashi University of Technology, Dept. of Electrical and Electronic Engineering, Research Associate, 工学部, 助手 (20324486)
FURUKAWA Yuzo Toyohashi University of Technology, Dept. of Electrical and Electronic Engineering, Research Associate (20324486)
FURUKAWA Yuzo Toyohashi University of Technology, Dept. of Electrical and Electronic Engineering, Research Associate (20324486)
FURUKAWA Yuzo Toyohashi University of Technology, Dept. of Electrical and Electronic Engineering, Research Associate (20324486)
FURUKAWA Yuzo Toyohashi University of Technology, Dept. of Electrical and Electronic Engineering, Research Associate (20324486)
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| Project Period (FY) |
2000 – 2002
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| Keywords | parallel networks / vision / biological retinas / analog LSI / edge-detection / motion-detection / motion detection / shape recognition |
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| Research Abstract |
Real-time processing of moving images is ultimately hard in present time-sequential computer systems. Thus, we aimed to realize massively parallel pre-processing networks with low-power dissipation, based on the vision mechanisms of biological retinas and brains. Practically, analog LSIs have been tried to be developed, which detect the motion and recognize simple shapes of moving targets.
In biological retinas and brains, the information on the edge of a target is extracted at an early stage. An edge-detection network was realized based on local adaptation and feedback to photoreceptors in vertebrate retinas. The edge was detected from an image with a wide brightness range of five orders of magnitude. The power dissipation was less by two orders of magnitude than that of vision chips under development with present technology.
Then, a motion-detection network was developed based on the retinas and brains of locusts and flies, in which the edge signal was inputted. The speed of an approac
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hing target was detected. The time required, for the speed detection was shorter by one order of magnitude than that of present systems. A network for simple-shape recognition was developed based on the vision system of flogs. As a result, the simple shapes such as a circle, triangle and square were recognized. In these networks, a binary output and feedback circuit were adopted to suppress the wrong operation of analog circuits due to the mismatch of MOS transistors.
It is predicted that a network for the motion detection of an approaching objects, combined with the simple-shape recognition, can be realized in a LSI. A curved motion consists of local direct-motion. Thus, a network was developed for a trace of motion of specific points of a target. Another motion-detection network was developed in which the motion of a background was suppressed. It is valuable for the collision avoidance of mobile vehicles. It is predicted that a time for the detection is reduced by 1/(number of pixels) (〜1/1000) when a massively parallel outputs are obtained without scanning. Less
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