To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machine...To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.展开更多
Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images cl...Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images classification system, is still low. In this paper, we propose a local texture descriptor termed fan refined local binary pattern, which captures more detailed information by integrating the spatial distribution into the local binary pattern feature. We compare our approach with different methods on a subset of product images on Amazon/e Bay and parts of PI100 and experimental results have demonstrated that our proposed approach is superior to the current existing methods. The highest classification precision is increased by 21% and the average classification time is reduced by 2/3.展开更多
针对铁路异物侵限频繁发生导致的列车运行安全问题,提出一种基于背景感知相关滤波器的铁路异物侵限跟踪方法。利用方向梯度直方图(HOG,Histogram of Oriented Gradient)特征提取铁路侵限异物自身特征,结合剪裁矩阵,以增加视频帧中实际...针对铁路异物侵限频繁发生导致的列车运行安全问题,提出一种基于背景感知相关滤波器的铁路异物侵限跟踪方法。利用方向梯度直方图(HOG,Histogram of Oriented Gradient)特征提取铁路侵限异物自身特征,结合剪裁矩阵,以增加视频帧中实际背景的负样本;使用交替方向乘子法(ADMM,Alternating Direction Method of Multipliers)训练背景感知相关滤波器,减少计算复杂度,在保证跟踪速度的前提下,提升跟踪侵限异物的准确性,从而适应铁路沿线环境中由于侵限异物的形变、快速移动或天气等原因造成的目标丢失及跟踪框漂移等情况。实验结果表明,该方法对铁路侵限异物的跟踪精确度和AUC(Area Under Curve)值分别达到93%和71.9%,均高于SRDCF、KCF、ASLA和CSK等算法,具有更好的准确性。展开更多
The histogram of oriented gradient has been successfully applied in many research fields with excellent performance especially in pedestrian detection. However, the method has rarely been applied to face recognition. ...The histogram of oriented gradient has been successfully applied in many research fields with excellent performance especially in pedestrian detection. However, the method has rarely been applied to face recognition. Aimed to develop a fast and efficient new feature for face recognition, the original HOG and its variations were applied to evaluate the effects of different factors. An information theory-based criterion was also developed to evaluate the potential classification power of different features. Comparative experiments show that even with a relatively simple feature descriptor, the proposed HOG feature achieves almost the same recognition rate with much lower computational time than the widely used Gabor feature on the FRGC and CAS-PEAL databases.展开更多
深度学习中的网络结构设计、特征提取与融合是数据挖掘和模式识别理论和行业应用中的关键问题。文中以相关领域中的典型应用问题手写数字识别和权威数据库MNIST为实验平台(包含七万个手写数字图像),探索了深度学习网络结构的设计和特征...深度学习中的网络结构设计、特征提取与融合是数据挖掘和模式识别理论和行业应用中的关键问题。文中以相关领域中的典型应用问题手写数字识别和权威数据库MNIST为实验平台(包含七万个手写数字图像),探索了深度学习网络结构的设计和特征融合问题,保证研究结果的实用性、代表性和可参考性。所给方案的步骤是:首先,设计非监督深度学习网络,进行非监督高层语义特征学习,提取深度特征(DF),探索特征的高层认知特点;其次,对手写数字数据库进行非监督多特征提取,包括HOG(梯度方向直方图)特征、PCA(主成分分析)特征、LDA(判别分析)特征、像素分布特征、穿越次数特征和投影特征,构建手写数字典型特征库(Library of Typical Features,LTF);最后,构建深度有监督学习网络,有监督地融合深度特征DF和典型特征库。实验结果表明,相比于文献中的典型特征,该方案能够将手写数字识别的错误率有效降低50%。展开更多
针对单幅图像中的行人检测问题,提出了基于自适应增强算法(Adaboost)和支持向量机(Support vector machine,SVM)的两级检测方法,应用粗细结合的思想有效提高检测的精度.粗级行人检测器通过提取四方向特征(Four direction features,FDF)...针对单幅图像中的行人检测问题,提出了基于自适应增强算法(Adaboost)和支持向量机(Support vector machine,SVM)的两级检测方法,应用粗细结合的思想有效提高检测的精度.粗级行人检测器通过提取四方向特征(Four direction features,FDF)和GAB(Gentle Adaboost)级联训练得到,精密级行人检测器用熵梯度直方图(Entropy-histograms of oriented gradients,EHOG)作为特征,通过支持向量机学习得到.本文提出的EHOG特征考虑到熵,通过分布的混乱程度描述,具有分辨行人和类似人的物体能力.实验结果表明,本文提出的EHOG、粗细结合的两级检测方法能准确地检测出复杂背景下不同姿势的直立行人,检测精度优于以往Adaboost方法.展开更多
基金National Natural Science Foundation of China(No.519705449)。
文摘To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.
基金Supported by the National Natural Science Foundation of China（60802061, 11426087） Supported by Key Project of Science and Technology of the Education Department Henan Province（14A120009）+1 种基金 Supported by the Program of Henan Province Young Scholar（2013GGJS-027） Supported by the Research Foundation of Henan University（2013YBZR016）
文摘Two key challenges raised by a product images classification system are classification precision and classification time. In some categories, classification precision of the latest techniques, in the product images classification system, is still low. In this paper, we propose a local texture descriptor termed fan refined local binary pattern, which captures more detailed information by integrating the spatial distribution into the local binary pattern feature. We compare our approach with different methods on a subset of product images on Amazon/e Bay and parts of PI100 and experimental results have demonstrated that our proposed approach is superior to the current existing methods. The highest classification precision is increased by 21% and the average classification time is reduced by 2/3.
文摘针对铁路异物侵限频繁发生导致的列车运行安全问题,提出一种基于背景感知相关滤波器的铁路异物侵限跟踪方法。利用方向梯度直方图(HOG,Histogram of Oriented Gradient)特征提取铁路侵限异物自身特征,结合剪裁矩阵,以增加视频帧中实际背景的负样本;使用交替方向乘子法(ADMM,Alternating Direction Method of Multipliers)训练背景感知相关滤波器,减少计算复杂度,在保证跟踪速度的前提下,提升跟踪侵限异物的准确性,从而适应铁路沿线环境中由于侵限异物的形变、快速移动或天气等原因造成的目标丢失及跟踪框漂移等情况。实验结果表明,该方法对铁路侵限异物的跟踪精确度和AUC(Area Under Curve)值分别达到93%和71.9%,均高于SRDCF、KCF、ASLA和CSK等算法,具有更好的准确性。
基金Supported by the National Key Basic Research and Development(973) Program of China (No. 2007CB311004)the National High-Tech Research and Development (863) Program of China(No. 2006AA01Z115)
文摘The histogram of oriented gradient has been successfully applied in many research fields with excellent performance especially in pedestrian detection. However, the method has rarely been applied to face recognition. Aimed to develop a fast and efficient new feature for face recognition, the original HOG and its variations were applied to evaluate the effects of different factors. An information theory-based criterion was also developed to evaluate the potential classification power of different features. Comparative experiments show that even with a relatively simple feature descriptor, the proposed HOG feature achieves almost the same recognition rate with much lower computational time than the widely used Gabor feature on the FRGC and CAS-PEAL databases.
文摘深度学习中的网络结构设计、特征提取与融合是数据挖掘和模式识别理论和行业应用中的关键问题。文中以相关领域中的典型应用问题手写数字识别和权威数据库MNIST为实验平台(包含七万个手写数字图像),探索了深度学习网络结构的设计和特征融合问题,保证研究结果的实用性、代表性和可参考性。所给方案的步骤是:首先,设计非监督深度学习网络,进行非监督高层语义特征学习,提取深度特征(DF),探索特征的高层认知特点;其次,对手写数字数据库进行非监督多特征提取,包括HOG(梯度方向直方图)特征、PCA(主成分分析)特征、LDA(判别分析)特征、像素分布特征、穿越次数特征和投影特征,构建手写数字典型特征库(Library of Typical Features,LTF);最后,构建深度有监督学习网络,有监督地融合深度特征DF和典型特征库。实验结果表明,相比于文献中的典型特征,该方案能够将手写数字识别的错误率有效降低50%。
文摘针对单幅图像中的行人检测问题,提出了基于自适应增强算法(Adaboost)和支持向量机(Support vector machine,SVM)的两级检测方法,应用粗细结合的思想有效提高检测的精度.粗级行人检测器通过提取四方向特征(Four direction features,FDF)和GAB(Gentle Adaboost)级联训练得到,精密级行人检测器用熵梯度直方图(Entropy-histograms of oriented gradients,EHOG)作为特征,通过支持向量机学习得到.本文提出的EHOG特征考虑到熵,通过分布的混乱程度描述,具有分辨行人和类似人的物体能力.实验结果表明,本文提出的EHOG、粗细结合的两级检测方法能准确地检测出复杂背景下不同姿势的直立行人,检测精度优于以往Adaboost方法.
