机构

• 长春光学精密机械与物... [2]

采集方式

• OAI收割 [2]

内容类型

• 会议论文 [2]

发表日期

• 2012 [1]
• 2011 [1]

学科主题

筛选

浏览/检索结果: 共2条，第1-2条 帮助 限定条件 内容类型：会议论文    专题：长春光学精密机械与物理研究所    第一署名单位    第一作者单位    通讯作者单位     条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序题名升序题名降序作者升序作者降序发表日期升序发表日期降序 Moving target detection and classification using spiking neural networks (EI CONFERENCE) 会议论文  OAI收割 2nd Sino-Foreign-Interchange Workshop on Intelligent Science and Intelligent Data Engineering, IScIDE 2011, October 23, 2011 - October 25, 2011, Xi'an, China 作者:  Sun H.;  Wang Z.;  Wang Z.;  Wang P.;  Sun H. 收藏  |  浏览/下载：36/0  |  提交时间：2013/03/25 We proposed a spiking neural network (SNN) to detect moving target in video streams and classify them into real categorization in this paper. The proposed SNN uses spike trains to encoding information such as the gray value of pixels or feature parameters of the target  detects moving target by simulating the visual cortex for motion detection in biological system with axonal delays and classify them into different categorizations according to their distance to categorization's centers found by Hebb learning rule. The experimental results show that the proposed SNN is promising in intelligence computation and applicable in general visual surveillance system. 2012 Springer-Verlag.   Design for target classifier based on semi-supervised learning (EI CONFERENCE) 会议论文  OAI收割 2011 International Conference on Electric Information and Control Engineering, ICEICE 2011, April 15, 2011 - April 17, 2011, Wuhan, China Jiangrui K. 收藏  |  浏览/下载：18/0  |  提交时间：2013/03/25 The target classifier is an ingredient of the target recognition system. In order to achieve the automation and computerization of target recognition  a method for training target classifier based on semi-supervised learning is provided. It adopts CFS algorithm for dada feature selection  and uses semi-supervised learning algorithm  Co-training to construct the target classifiers. The final classifier was produced through integration learning method. Experimental results show that the performance of the target classifier based on semi-supervised learning trained is superior to the traditional target classifier. 2011 IEEE.