学术
基于简化脉冲耦合神经网络的蝗虫图像二值分割
熊雪梅[1] 王一鸣[2] 张小超[3] 郑永军[4]
[1]中国农业大学信息与电气工程学院讲师、博士后,北京市100083 [2]中国农业大学信息与电气工程学院教授,北京市100083 [3]中国农业机械化科学研究院研究员,北京市100083 [4]中国农业大学信息与电气工程学院讲师、博士生,北京市100083
摘 要:
采用参数简化的脉冲耦合神经网络（PCNN）分割图像，进行了蝗虫图像分割实验，区域正确识别率达94％，为蝗虫自动侦测系统中的数据处理提供了技术支持。计算机仿真表明，采用PCNN图像分割算法，图像中的目标（蝗虫）易于发现，分割效果明显好于采用开操作处理的图像。[著者文摘]
文章出处:
《农业机械学报》-2007年38卷10期,107 -84-86,107页
栏目信息:
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文献标识码:
A
Image Binary Segmentation Based on Pulse-coupled Neural Network for the Locust Detection System
Xiong Xuemei, Wang Yiming, Zhang Xiaochao, Zheng Yongjun（1. China Agricultural University ;2. Chinese Academy of Agricultural Mechanization Sciences）
Abstract:
Image binary segmentation is the most fundamental and important preprocessing in image analysis and pattern recognition, which directly affects analyses and results of post-processing. The crucial step in image data processing of automatic locust detection system （ALDS） is image segmentation. A parametrically simplified pulse-coupled neural network （PCNN） was brought forward. Experiments were done on locust images. Area recognition rate （ARR） achieved 94%. The results of computer simulation showed that the objects （locusts） in the image were easier to be found by using PCNN than by the ‘open’ operation. The performance of PCNN in image processing has been tested, and a new approach to detect locusts has been developed.[著者文摘]
Key words:
Locust, Image segmentation, Pulse-coupled neural network
基金资助:
中国博士后科学基金资助项目（项目编号：20060390545）和国家科技部科研院所社会公益研究专项项目（项目编号：2004DIB3J076）
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