目的探讨非综合征性唇腭裂(nonsyndromic cleft lip and palate,NSCL/P)发病的主要危险因素;评估这些主要危险因素在NSCL/P发病中的相对重要性,最终确立NSCL/P发病概率的预测模型,为优生网络的构建奠定基础。方法采用1∶1配对病例对照研...目的探讨非综合征性唇腭裂(nonsyndromic cleft lip and palate,NSCL/P)发病的主要危险因素;评估这些主要危险因素在NSCL/P发病中的相对重要性,最终确立NSCL/P发病概率的预测模型,为优生网络的构建奠定基础。方法采用1∶1配对病例对照研究,病例组来源于2006年9月至2007年9月在潍坊医学院附属医院、潍坊市人民医院、菏泽市立医院、烟台毓璜顶医院口腔科住院,年龄在12岁以下患有NSCL/P的儿童76例;对照组为来源于同一机构门诊或病房或同一居住区符合配对条件的非唇腭裂儿童76名。根据拟定的42项危险因素编制调查表,对病例组患儿与对照组儿童的父母进行调查,数据经审核后录入Excel 2003建立数据库。首先使用条件Logistic回归对资料进行单因素分析,再对单因素筛选的变量结合专业知识进行多因素分析,筛选主要危险因素并建立回归模型,根据危险因素分别建立分类树与LogitBoost算法的发病概率预测模型,采用受试者工作特征曲线(ROC曲线)对两模型进行评价,从而确立本研究中NSCL/P发病概率的预测模型。结果病例组与对照组作对比分析,进入条件Logistic回归模型的变量有:母亲孕期感染史(P=0.010)、家族遗传史(P=0.009)、母孕期饮食是否规律(P=0.007)、胎次(P=0.004)、母亲孕期异常情绪史(P<0.001)、父亲学历(P<0.001)。经ROC曲线评价,确立分类树模型可用来预测NSCL/P的发病概率。结论母亲孕期感染、家族遗传、母亲孕期饮食不规律、胎次、母亲孕期异常情绪是NSCL/P发病的促进因素,且其对NSCL/P发病的影响作用依次增强;父亲学历是该病的保护因素。经ROC曲线评价,最终确立分类树模型为NSCL/P发病概率的预测模型。展开更多
A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifyi...A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifying its yaw angle to determine his vision range and the court situation of the sportsman within his vision range can be further learned. In basketball match videos characterized by cluttered background, fast motion of the sportsmen and low resolution of their head images, and the covariance descriptor, were adopted to fuse multiple visual features of the head region, which can be seen as a point on the Riemannian manifold and then mapped to the tangent space. Then, the classification of head yaw angle was directly completed in this space through the trained multiclass LogitBoost. In order to describe the court situation of all sportsmen within the ball carrier’s vision range, artificial potential field (APF)-based information was introduced. Finally, the behavior of the ball carrier—shooting, passing and dribbling, was predicted using radial basis function (RBF) neural network as the classifier. Experimental results show that the average prediction accuracy of the proposed method can reach 80% on the video recorded in basketball matches, which validates its effectiveness.展开更多
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education, China
文摘A new vision-based approach was presented for predicting the behavior of the ball carrier—shooting, passing and dribbling in basketball matches. It was proposed to recognize the ball carrier’s head pose by classifying its yaw angle to determine his vision range and the court situation of the sportsman within his vision range can be further learned. In basketball match videos characterized by cluttered background, fast motion of the sportsmen and low resolution of their head images, and the covariance descriptor, were adopted to fuse multiple visual features of the head region, which can be seen as a point on the Riemannian manifold and then mapped to the tangent space. Then, the classification of head yaw angle was directly completed in this space through the trained multiclass LogitBoost. In order to describe the court situation of all sportsmen within the ball carrier’s vision range, artificial potential field (APF)-based information was introduced. Finally, the behavior of the ball carrier—shooting, passing and dribbling, was predicted using radial basis function (RBF) neural network as the classifier. Experimental results show that the average prediction accuracy of the proposed method can reach 80% on the video recorded in basketball matches, which validates its effectiveness.
