光学学报, 1996, 16 (6): 763, 网络出版: 2006-12-04
基于数学形态谱和二维矢量分类网络的模式识别及二维矢量分类网络的光学实现
Shift, Rotation Invariant Pattern Recognition Based on Morphology Pattern Spectrum and 2-D Vector Quantization Network and the Optical Implementation of 2-D Vector Quantization Network
归一数学形态谱 二维矢量分类网络及光学实现 pattern recognition 2-D vector quantization network morphology pattern spectrum optical implementation
摘要
介绍一种基于数学形态谱和二维矢量分类网络的模式识别体系。数学形态谱相对于图像平移和旋转不变。建立了光学二维矢量分类网络,利用光学逻辑操作和最大值网络的循环操作,得到与输入图像最佳匹配的模式。
Abstract
In this paper, we propose a shift and rotation invariant pattern recognition architecture, using morphology pattern spectrum as image feature representation and 2-D vector quantization network as vector classifier. Morphology pattern spectrum is invariant not to image shift, but to image rotation when the structure element is rotationally symmetric. We use it constructing a feature vector of an image to train a 2-D vector quantization network. After training, it can implement the classification of feature vector. The optical implementation of the 2-D vector quantization network is dementrated and the experiment results are given.
参考文献
王宁, 刘立人, 梁丰. 基于数学形态谱和二维矢量分类网络的模式识别及二维矢量分类网络的光学实现[J]. 光学学报, 1996, 16(6): 763. 王宁, 刘立人, 梁丰. Shift, Rotation Invariant Pattern Recognition Based on Morphology Pattern Spectrum and 2-D Vector Quantization Network and the Optical Implementation of 2-D Vector Quantization Network[J]. Acta Optica Sinica, 1996, 16(6): 763.