摘要
在云平台和分布式处理系统中进行船舶图像分类,提高船舶的调度和识别能力,提出一种基于Harris角点检测和BP神经网络的船舶图像分类算法,在云平台和分布式系统下进行船舶图像采集,对采集的船舶图像进行二值化降噪处理,采用Harris角点检测技术提取船舶的分类标识性特征量,将提取的特征量输入到BP神经网络分类器中,实现云平台环境下的船舶图像分类。仿真结果表明,采用该方法进行船舶图像分类的准确性较高,抗类间干扰性较强。
Ship image classification is carried out in the cloud platform and the distributed processing system,and the ship image classification algorithm based on Harris corner point detection and BP neural network is proposed.The classification identification feature quantity of the ship is extracted under the cloud platform and the distributed system,and the extracted feature quantity is input into the BP neural network classifier to realize the classification of the ship image in cloud platform.The simulation results show that the accuracy of the classification of the ship image is high and the anti-class interference is stronger.
出处
《舰船科学技术》
北大核心
2018年第2X期163-165,共3页
Ship Science and Technology
基金
河南省商丘市科技局基础前沿资助项目(143017)
关键词
云平台
图像分类
分布式处理系统
HARRIS角点检测
cloud platform
image classification
distributed processing system
Harris corner detection
