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高斯核密度估计背景建模及噪声与阴影抑制 被引量:10
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作者 毛燕芬 施鹏飞 《系统仿真学报》 EI CAS CSCD 北大核心 2005年第5期1182-1184,共3页
提出了一种多模态非参数背景模型,用于背景减方法检测运动目标。针对户外监控系统存在背景局部运动以及摄像机抖动、活动阴影等问题,利用像素邻域相关性信息进行多模态高斯核密度估计,并采用HMMD色彩值抑制阴影。通过抖动噪声去除以及... 提出了一种多模态非参数背景模型,用于背景减方法检测运动目标。针对户外监控系统存在背景局部运动以及摄像机抖动、活动阴影等问题,利用像素邻域相关性信息进行多模态高斯核密度估计,并采用HMMD色彩值抑制阴影。通过抖动噪声去除以及阴影抑制处理,降低了目标检测的虚警率。实验结果表明该算法在运动目标检测中具有对噪声和阴影的鲁棒性,可用于户外复杂场景监控系统。 展开更多
关键词 核函数密度估计 阴影抑制 HMMD色彩空间 运动目标检测
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湖北省财政性科研投入效率测度及时空演化
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作者 黄万华 王梦迪 高红贵 《西部经济管理论坛》 2023年第5期38-47,共10页
文章利用SBM模型测度了2011—2020年湖北省12个地级市财政性科研投入产出综合效率、纯技术效率和规模效率的变化趋势,借用莫兰指数与Kernel核密度估计函数刻画了湖北省财政性科研投入产出效率的时空动态演化特征。研究发现:从整体看,湖... 文章利用SBM模型测度了2011—2020年湖北省12个地级市财政性科研投入产出综合效率、纯技术效率和规模效率的变化趋势,借用莫兰指数与Kernel核密度估计函数刻画了湖北省财政性科研投入产出效率的时空动态演化特征。研究发现:从整体看,湖北省地级市财政性科研产出效率整体较高,且呈增长趋势,纯技术效率波动与综合效率升降趋势较为同步,区域效率差异主要源于规模效率;在时序上,地级市相继由低值区向高值区跃迁,处于低值区的地级市数量在减少,处于高值区的地级市数量在增加,技术进步与技术扩散在加快;在空间上,高效率区域和低效率区域间存在趋同效应,具有明显的区块分布特征,高值区域可以拉动低值区域的绩效水平,区域间的差距呈现缩小的趋势。 展开更多
关键词 投入产出效率 SBM模型 莫兰指数 Kernel密度估计函数
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Calculation of One-Valued Control Limits by Control Chart of Angles 被引量:2
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作者 李元生 方英 朱险峰 《Journal of China University of Mining and Technology》 2001年第2期229-230,共2页
The data we use to express angle or direction are entitled directional data. In a plan right angled coordinate system the traditional control chart can’t solve the quality control problem which the characteristic val... The data we use to express angle or direction are entitled directional data. In a plan right angled coordinate system the traditional control chart can’t solve the quality control problem which the characteristic value is angle. This paper analyses and calculates the one valued control limits by control chart of angles. 展开更多
关键词 control chart ANGLE directional data
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Robust background subtraction in traffic video sequence 被引量:6
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作者 高韬 刘正光 +3 位作者 岳士弘 张军 梅建强 高文春 《Journal of Central South University》 SCIE EI CAS 2010年第1期187-195,共9页
For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background mod... For intelligent transportation surveillance, a novel background model based on Mart wavelet kernel and a background subtraction technique based on binary discrete wavelet transforms were introduced. The background model kept a sample of intensity values for each pixel in the image and used this sample to estimate the probability density function of the pixel intensity. The density function was estimated using a new Marr wavelet kernel density estimation technique. Since this approach was quite general, the model could approximate any distribution for the pixel intensity without any assumptions about the underlying distribution shape. The background and current frame were transformed in the binary discrete wavelet domain, and background subtraction was performed in each sub-band. After obtaining the foreground, shadow was eliminated by an edge detection method. Experimental results show that the proposed method produces good results with much lower computational complexity and effectively extracts the moving objects with accuracy ratio higher than 90%, indicating that the proposed method is an effective algorithm for intelligent transportation system. 展开更多
关键词 background modeling background subtraction Marr wavelet binary discrete wavelet transform shadow elimination
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Strong Consistency for the Kernal Estimates of the Random Window Width of the Density Function and its Derivatives Under Φ-Mixing Samples
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作者 樊家琨 《Chinese Quarterly Journal of Mathematics》 CSCD 1993年第3期52-56,共5页
In the paper,we study the strong uniform consistency for the kernal estimates of random window w■th of density function and its derivatives under the condition that the sequence{X_n}of the ■ are the identically Φ-m... In the paper,we study the strong uniform consistency for the kernal estimates of random window w■th of density function and its derivatives under the condition that the sequence{X_n}of the ■ are the identically Φ-mixing random variabks. 展开更多
关键词 Φ-mixing sample probability density function random window width kemal estimate strng uniform consistency
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