基于SOFM神经网络的茄子图像分割方法

Method of image segmentation for eggplant based on SOFM neural network

以将茄子图像从复杂的背景中分割出来为目的,在分析茄子图像色差和色相的基础上,选取R-B、G-B和H作为自组织特征映射(SOFM)网络的输入特征向量,利用该网络自组织学习的特征进行聚类。采用信噪比、面积比、分割时间和傅里叶边界描述子等指标来评价分割精度。试验证明,基于SOFM神经网络图像分割评价优于单一阈值分割,适合复杂背景的彩色图像分割。

The purpose of this article was to segment eggplant from its complex background. R-B , G-B and H were selected as the input-vectors of the self-organizing feature maps （SOFM） network based on analyzing the color-difference and hue characteristics of eggplant image. The input-vectors were classified according to the self-organizing characteristics of this network. In order to make the segmentation results objective and reasonable, signal-noise ratio, area ratio, segmentation times and Fourier-Descriptor were a- dopted to evaluate the segmentation precision. The experiment demonstrates that SOFM network was better than the single-threshold segmentation and more suitable for the color image segmentation with complex background.