The Histograms of Oriented Gradients(HOG)can produce good results in an image target recognition mission,but it requires the same size of the target images for classification of inputs.In response to this shortcoming,...The Histograms of Oriented Gradients(HOG)can produce good results in an image target recognition mission,but it requires the same size of the target images for classification of inputs.In response to this shortcoming,this paper performs spatial pyramid segmentation on target images of any size,gets the pixel size of each image block dynamically,and further calculates and normalizes the gradient of the oriented feature of each block region in each image layer.The new feature is called the Histogram of Spatial Pyramid Oriented Gradients(HSPOG).This approach can obtain stable vectors for images of any size,and increase the target detection rate in the image recognition process significantly.Finally,the article verifies the algorithm using VOC2012 image data and compares the effect of HOG.展开更多
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
基金partly supported by the National Natural Science Foundation of China(No.51802348)。
文摘The Histograms of Oriented Gradients(HOG)can produce good results in an image target recognition mission,but it requires the same size of the target images for classification of inputs.In response to this shortcoming,this paper performs spatial pyramid segmentation on target images of any size,gets the pixel size of each image block dynamically,and further calculates and normalizes the gradient of the oriented feature of each block region in each image layer.The new feature is called the Histogram of Spatial Pyramid Oriented Gradients(HSPOG).This approach can obtain stable vectors for images of any size,and increase the target detection rate in the image recognition process significantly.Finally,the article verifies the algorithm using VOC2012 image data and compares the effect of HOG.
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.
