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首页> 外文会议>Conference on Image Processing: Algorithms and Systems III; 20040119-20040121; San Jose,CA; US >Cellular Pulse Coupled Neural Network with Adaptive Weights for Image Segmentation and its VLSI Implementation
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Cellular Pulse Coupled Neural Network with Adaptive Weights for Image Segmentation and its VLSI Implementation

机译：具有自适应权重的细胞脉冲耦合神经网络图像分割及其VLSI实现

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摘要

We present a cellular pulse coupled neural network with adaptive weights and its analog VLSI implementation. The neural network operates on a scalar image feature, such as grey scale or the output of a spatial filter. It detects segments and marks them with synchronous pulses of the corresponding neurons. The network consists of integrate-and-fire neurons, which are coupled to their nearest neighbors via adaptive synaptic weights. Adaptation follows either one of two empirical rules. Both rules lead to spike grouping in wave like patterns. This synchronous activity binds groups of neurons and labels the corresponding image segments. Applications of the network also include feature preserving noise removal, image smoothing, and detection of bright and dark spots. The adaptation rules are insensitive for parameter deviations, mismatch and non-ideal approximation of the implied functions. That makes an analog VLSI implementation feasible. Simulations showed no significant differences in the synchronization properties between networks using the ideal adaptation rules and networks resembling implementation properties such as randomly distributed parameters and roughly implemented adaptation functions. A prototype is currently being designed and fabricated using an Infineon 130nm technology. It comprises a 128 x 128 neuron array, analog image memory, and an address event representation pulse output.
机译：我们提出了具有自适应权重的细胞脉冲耦合神经网络及其模拟VLSI实现。神经网络对标量图像特征（例如灰度或空间滤波器的输出）进行操作。它检测段并用相应神经元的同步脉冲标记它们。该网络由整合并发射神经元组成，这些神经元通过自适应突触权重耦合到其最近的邻居。适应遵循两个经验规则之一。这两个规则都会导致以波状图案进行尖峰分组。这种同步活动会绑定神经元组并标记相应的图像段。该网络的应用还包括保留噪声消除功能，图像平滑功能以及亮点和暗点检测功能。自适应规则对于隐含函数的参数偏差，不匹配和非理想逼近不敏感。这使得模拟VLSI实现成为可能。仿真表明，使用理想的自适应规则的网络与类似于实现属性（例如随机分布的参数和大致实现的适应函数）的网络之间的同步属性之间没有显着差异。目前正在使用英飞凌130nm技术设计和制造原型。它包括一个128 x 128神经元阵列，一个模拟图像存储器以及一个地址事件表示脉冲输出。

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