A research into fine-grained object systems for computer vision and neural networks

（a）计算机视觉和神经网络的大规模并行体系结构在本研究项目中，我们基于面向对象系统的概念对大规模并行计算机视觉系统进行了研究。这是因为面向对象系统为我们提供了一个很好的框架来表示并行过程，以及表示复杂系统，如计算机视觉系统。在计算机视觉中非常有用的神经网络也从大规模并行的角度考虑。我们所采用的架构是基于一个简单的，静态的二维网状通信网络的并行模型，我们已经设计了一个大规模的并行机称为AMP。关键问题是提供一个轻量级的消息处理机制，这在细粒度并行处理中是不可避免的（即，对象系统）执行。此外，我们已经通过软件仿真显示了AMP在计算机视觉应用中的高效率。（B）神经网络的程序设计语言为了简化大规模神经网络的描述，通过引入面向对象的概念，我们扩充了作为AMP的基本函数式语言的程序设计语言Valid。扩充语言为我们提供了一种封装神经元和神经元网络函数的机制，也为我们提供了一个神经网络类库。（c）基于神经网络的大规模并行计算机视觉系统我们已经提出了一种称为ICE（图像中心）系统的大规模并行计算机视觉系统，其基于多层、分级神经网络。在该系统中，图像理解的结果被表示在最高层中的激活单元的组合序列中：每个单元对应于一个单词含义。

(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.