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Applications of cellular neural networks for shape from shading problem

机译:细胞神经网络在阴影问题中的形状应用

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The Collular Neural Networks (CNN) model consist of many parallel analog processors computing in real time. CNN is nowadays a paradigm of cellular analog programmable multidimensional processor array with distributed local logic and memory. One desirable feature is that these processors are arranged in a two dimensional grid and have only local connections. This structure can be easily translated into a VLSI implementation, where the connections beteen the processors are determined by a cloning template. This template describes the strength of nearest-neighbour interconnections in the network. The focus of this paper is to present one new methodology to solve Shape from Shading problem using CNN. Some practical results are presented and briefly discussed, demonstrating the successful operation of the proposed algorithm.
机译:Collural Neural Networks(CNN)模型由许多实时并行计算的并行模拟处理器组成。如今,CNN是具有分布式本地逻辑和内存的蜂窝模拟可编程多维处理器阵列的范例。一个理想的特征是这些处理器被布置在二维网格中并且仅具有本地连接。此结构可以轻松转换为VLSI实现,其中处理器之间的连接由克隆模板确定。该模板描述了网络中最近邻居互连的强度。本文的重点是提出一种使用CNN来解决“ Shading from Shading”问题的新方法。提出并简要讨论了一些实际结果,证明了该算法的成功运行。

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