| Co-Investigator(Kenkyū-buntansha) |
KUSAKABE Shigeru Kyushu Univ.,Grad.School of Eng.Sci.,Research Associate, 総合理工学研究科, 助手 (70234416)
TSURUTA Naoyuki Kyushu Univ.,Grad.School of Eng.Sci.,Research Associate, 総合理工学研究科, 助手 (60227478)
MIYAZAKI Akio Kyushu Univ.,Faculty of Engineering,Associate Professor, 工学部, 助教授 (70192763)
FUKUDA Akira Kyushu Univ.,Faculty of Engineering,Associate Professor, 工学部, 助教授 (80165282)
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| Research Abstract |
(a)Massively parallel architecture for computer vision and neural networks We have made a research into a massively parallel computer vision system based on the concept of object-oriented system in this research project. This is because object-oriented systems provide us a good framework for representing parallel processes, as well as for representing complex systems such as computer vision systems. Neural networks, which are quite useful in computer vision, have been also considered from the viewpoint of massive parallelism. The architecture which we have adopted is based on a simple, static dataflow model with a two dimensional mesh communication network, and we have designed a massively parallel machine called AMP. The key issue is to provide a light-weight message handling mechanism, which is inevitable in fine-grained parallel process(i.e.,object system)execution. In addition, we have shown the high efficiency of AMP in computer vision applications by software simulation.
(b)Programming language for neural networks We have augmented a programming language Valid, which is a basic functional language for AMP, by introducing the object-oriented concept in order to simplify the description of large-scaled neural networks. The augmented language provides us a mechanism to encapsulate functions of neurons and neuron networks, and also provides us a class library of neural networks.
(c)Massively parallel computer vision system based on a neural network We have proposed a massively parallel computer vision system called ICE(Image CEntered)System, which is based on a multi-layered, hierarchical neural network. In this system a result of image understanding is represented in a sequence of combinations of activated units in the highest layer: each of the units corresponds to a word meaning.
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