Kuntimad G, Ranganath H S
Rocketdyne Division, Boeing North American, Huntsville, AL 35806, USA.
IEEE Trans Neural Netw. 1999;10(3):591-8. doi: 10.1109/72.761716.
This paper describes a method for segmenting digital images using pulse coupled neural networks (PCNN's). The pulse coupled neuron (PCN) model used in PCNN is a modification of Eckhorn's cortical neuron model. A single layered laterally connected PCNN is capable of perfectly segmenting digital images even when there is a considerable overlap in the intensity ranges of adjacent regions. Conditions for perfect image segmentation are derived. It is also shown that addition of an inhibition receptive field to the neuron model increases the possibility of perfect segmentation. The inhibition input reduces the overlap of intensity ranges of adjacent regions by effectively compressing the intensity range of each region.
本文描述了一种使用脉冲耦合神经网络（PCNN）对数字图像进行分割的方法。PCNN中使用的脉冲耦合神经元（PCN）模型是对埃克霍恩皮层神经元模型的一种改进。即使相邻区域的强度范围存在相当大的重叠，单层横向连接的PCNN也能够完美地分割数字图像。推导了实现完美图像分割的条件。研究还表明，在神经元模型中添加抑制感受野会增加实现完美分割的可能性。抑制输入通过有效压缩每个区域的强度范围来减少相邻区域强度范围的重叠。
