摘要
在经典的基于混合高斯模型减背景算法的基础上,在脉冲耦合神经网络(PCNN)对前景和背景的分割过程中,运用了多阈值思想,其迭代次数由简化的最大熵准则决定,并且提出了一种新的模型学习率。经过实验证明,该算法在检测能力、抑制噪声、稳定性等方面得到了较好的改进。
Motion detection has a wide range of applications in many artificial intelligence implementations.An improved motion detection algorithm was proposed.Gaussian mixture model of classical algorithm was used,and background and foreground were classified by using Pulse Couple Neural Network(PCNN).PCNN was modified,multi-threshold was adopted to detect object and simple maximum entropy rule was applied to end iteration.Also,a new learning rate was proposed in the model updating stage.Experimental results show th...
出处
《计算机应用》
CSCD
北大核心
2009年第3期739-741,共3页
journal of Computer Applications
基金
国家自然科学基金资助项目(60572011)
教育部新世纪优秀人才支持计划项目(NCET-06-0900)
甘肃省自然科学基金资助项目(0710RJZA015)
关键词
运动目标检测
脉冲耦合神经网络
多阈值
简化最大熵
学习率
motion detection
Pulse Couple Neural Network（PCNN）
multi-threshold
simple maximum entropy
learning rate
