摘要
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.
作者
姚立健
丁为民
赵三琴
杨玲玲
YAO Li-jian;DING Wei-min;ZHAO San-qin;YANG Ling-ling(College of Engineering,Nanjing Agricultural University,Nanjing 210031,China)
出处
《南京农业大学学报》
CAS
CSCD
北大核心
2008年第3期140-144,共5页
Journal of Nanjing Agricultural University
