为检验 X 线视频密度仪计算左心室射血分数的准确性,17例冠心病患者接受了数字减影左室造影。ADAC 数字减影系统可描记左室密度一时间曲线,并可用面积长度公式计算左心室容量。密度法和面积长度公式计算的射血分数进行了相关和对照。结...为检验 X 线视频密度仪计算左心室射血分数的准确性,17例冠心病患者接受了数字减影左室造影。ADAC 数字减影系统可描记左室密度一时间曲线,并可用面积长度公式计算左心室容量。密度法和面积长度公式计算的射血分数进行了相关和对照。结果表明,两法计算的射血分数高度相关,r=0.93,P<0.001。为进一步了解两法射血分数的动态关系,从收缩期开始,每34毫秒记录一次密度值,计算一次左室容量。然后代入公式求得连续射血分数。结果显示,长度面积公式计算的射血分数上升较密度法早而快。结论:X 线视频密度仪可以准确地计算射血分数,但密度值与造影剂的厚度并不呈紧密的线性关系。展开更多
A statistical multimodal background model was described for moving object detection in video surveillance. The solution to some of the problems such as illumination changes, initialization of model with moving objects...A statistical multimodal background model was described for moving object detection in video surveillance. The solution to some of the problems such as illumination changes, initialization of model with moving objects, and shadows suppression was provided. The background samples were chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation was used to estimate the probability density function of background intensity. Pixel's neighbor information was considered to remove noise due to camera jitter and small motion in the scene. The hue-max-min-diff color information was used to detect and suppress moving cast shadows. The effectiveness of the proposed method in the foreground segmentation was demonstrated in the traffic surveillance application.展开更多
Crowd density is an important factor of crowd stability.Previous crowd density estimation methods are highly dependent on the specific video scene.This paper presented a video scene invariant crowd density estimation ...Crowd density is an important factor of crowd stability.Previous crowd density estimation methods are highly dependent on the specific video scene.This paper presented a video scene invariant crowd density estimation method using Geographic Information Systems(GIS) to monitor crowd size for large areas.The proposed method mapped crowd images to GIS.Then we can estimate crowd density for each camera in GIS using an estimation model obtained by one camera.Test results show that one model obtained by one camera in GIS can be adaptively applied to other cameras in outdoor video scenes.A real-time monitoring system for crowd size in large areas based on scene invariant model has been successfully used in 'Jiangsu Qinhuai Lantern Festival,2012'.It can provide early warning information and scientific basis for safety and security decision making.展开更多
Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In...Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.展开更多
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
文摘为检验 X 线视频密度仪计算左心室射血分数的准确性,17例冠心病患者接受了数字减影左室造影。ADAC 数字减影系统可描记左室密度一时间曲线,并可用面积长度公式计算左心室容量。密度法和面积长度公式计算的射血分数进行了相关和对照。结果表明,两法计算的射血分数高度相关,r=0.93,P<0.001。为进一步了解两法射血分数的动态关系,从收缩期开始,每34毫秒记录一次密度值,计算一次左室容量。然后代入公式求得连续射血分数。结果显示,长度面积公式计算的射血分数上升较密度法早而快。结论:X 线视频密度仪可以准确地计算射血分数,但密度值与造影剂的厚度并不呈紧密的线性关系。
文摘A statistical multimodal background model was described for moving object detection in video surveillance. The solution to some of the problems such as illumination changes, initialization of model with moving objects, and shadows suppression was provided. The background samples were chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation was used to estimate the probability density function of background intensity. Pixel's neighbor information was considered to remove noise due to camera jitter and small motion in the scene. The hue-max-min-diff color information was used to detect and suppress moving cast shadows. The effectiveness of the proposed method in the foreground segmentation was demonstrated in the traffic surveillance application.
基金The authors would like to thank the reviewers for their detailed reviews and constructive comments. We are also grateful for Sophie Song＇s help on the improving English. This work was supported in part by the ‘Fivetwelfh＇ National Science and Technology Support Program of the Ministry of Science and Technology of China （No. 2012BAH35B02）, the National Natural Science Foundation of China （NSFC） （No. 41401107, No. 41201402, and No. 41201417）.
文摘Crowd density is an important factor of crowd stability.Previous crowd density estimation methods are highly dependent on the specific video scene.This paper presented a video scene invariant crowd density estimation method using Geographic Information Systems(GIS) to monitor crowd size for large areas.The proposed method mapped crowd images to GIS.Then we can estimate crowd density for each camera in GIS using an estimation model obtained by one camera.Test results show that one model obtained by one camera in GIS can be adaptively applied to other cameras in outdoor video scenes.A real-time monitoring system for crowd size in large areas based on scene invariant model has been successfully used in 'Jiangsu Qinhuai Lantern Festival,2012'.It can provide early warning information and scientific basis for safety and security decision making.
基金supported by the National Natural Science Foundation of China under Grant No. 61175007the National Key Technologies R&D Program under Grant No. 2012BAH07B01the National Key Basic Research Program of China (973 Program) under Grant No. 2012CB316302
文摘Crowd density estimation in wide areas is a challenging problem for visual surveillance. Because of the high risk of degeneration, the safety of public events involving large crowds has always been a major concern. In this paper, we propose a video-based crowd density analysis and prediction system for wide-area surveillance applications. In monocular image sequences, the Accumulated Mosaic Image Difference (AMID) method is applied to extract crowd areas having irregular motion. The specific number of persons and velocity of a crowd can be adequately estimated by our system from the density of crowded areas. Using a multi-camera network, we can obtain predictions of a crowd's density several minutes in advance. The system has been used in real applications, and numerous experiments conducted in real scenes (station, park, plaza) demonstrate the effectiveness and robustness of the proposed method.
基金Project(60772080) supported by the National Natural Science Foundation of ChinaProject(3240120) supported by Tianjin Subway Safety System, Honeywell Limited, China
文摘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.
