Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image pro...Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image processing methods have poor adaptability to pollution and complex backgrounds.Although deep learning-based methods can automatically extract features,the bounding boxes cannot entirely fit the contour of the code.Further image processing methods are required for precise positioning,which will reduce efficiency.Because of the above problems,a CenterNet-based DM code key point detection network is proposed,which can directly obtain the four key points of the DM code.Compared with the existing methods,the degree of fitness is higher,which is conducive to direct decoding.To further improve the positioning accuracy,an enhanced loss function is designed,including DM code key point heatmap loss,standard DM code projection loss,and polygon Intersection-over-Union(IoU)loss,which is beneficial for the network to learn the spatial geometric characteristics of DM code.The experiment is carried out on the self-made DM code key point detection dataset,including pollution,complex background,small objects,etc.,which uses the Average Precision(AP)of the common object detection metric as the evaluation metric.AP reaches 95.80%,and Frames Per Second(FPS)gets 88.12 on the test set of the proposed dataset,which can achieve real-time performance in practical applications.展开更多
A privilege flow oriented intrusion detection method based on HSMM (Hidden semi-Markov Model) is discussed. The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS (Intrusi...A privilege flow oriented intrusion detection method based on HSMM (Hidden semi-Markov Model) is discussed. The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS (Intrusion Detection System). Using the dataset of DARPA 1998, our experiment results reveal good detection performance and acceptable computation cost.展开更多
Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to infor...Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.展开更多
Human pose recognition and estimation in video is pervasive.However,the process noise and local occlusion bring great challenge to pose recognition.In this paper,we introduce the Kalman filter into pose recognition to...Human pose recognition and estimation in video is pervasive.However,the process noise and local occlusion bring great challenge to pose recognition.In this paper,we introduce the Kalman filter into pose recognition to reduce noise and solve local occlusion problem.The core of pose recognition in video is the fast detection of key points and the calculation of human steering angles.Thus,we first build a human key point detection model.Frame skipping is performed based on the Hamming distance of the hash value of every two adjacent frames in video.Noise reduction is performed on key point coordinates with the Kalman filter.To calculate the human steering angle,current state information of key points is predicted using the optimal estimation of key points at the previous time.Then human steering angle can be calculated based on current and previous state information.The improved SENet,NLNet and GCNet modules are integrated into key point detection model for improving accuracy.Tests are also given to illustrate the effectiveness of the proposed algorithm.展开更多
The input document images with skew can be a serious problem in the optical character recognition system. A method is proposed for skew detection in binary document images using mathematic morphology. The basic proces...The input document images with skew can be a serious problem in the optical character recognition system. A method is proposed for skew detection in binary document images using mathematic morphology. The basic process of our approach consists of three steps: Firstly, a dilation operation is applied to the binary image; Secondly, the dilated image is thinned; Finally, the skew angle is detected using the Hough transform. The proposed approach with high precision can detect skew with large angle (?90δ-90Δ). The experimental result shows this method is applicable and efficient.展开更多
Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We pres...Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.展开更多
The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in ...The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.展开更多
Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object tar...Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.展开更多
Human pose estimation is an important task in computer vision,which can provide key point detection of human body and obtain bone information.At present,human pose estimation is mainly utilized for detection of large ...Human pose estimation is an important task in computer vision,which can provide key point detection of human body and obtain bone information.At present,human pose estimation is mainly utilized for detection of large targets,and there is no solution for detection of small targets.This paper proposes a multi-channel spatial information feature based human pose(MCSF-Pose)estimation algorithm to address the issue of medium and small targets inaccurate detection of human key points in scenarios involving occlusion and multiple poses.The MCSF-Pose network is a bottom-up regression network.Firstly,an UP-Focus module is designed to expand the feature information while reducing parameter computation during the up-sampling process.Then,the channel segmentation strategy is adopted to cut the features,and the feature information of multiple dimensions is retained through different convolutional groups,which reduces the parameter lightweight network model and makes up for the loss of the feature information associated with the depth of the network.Finally,the three-layer PANet structure is designed to reduce the complexity of the model.With the aid of the structure,it also to improve the detection accuracy and anti-interference ability of human key points.The experimental results indicate that the proposed algorithm outperforms YOLO-Pose and other human pose estimation algorithms on COCO2017 and MPII human pose datasets.展开更多
基金funded by the Youth Project of National Natural Science Foundation of China(52002031)the General Project of Shaanxi Province Science and Technology Development Planned Project(2023-JC-YB-600)+1 种基金Postgraduate Education and Teaching Research University-Level Project of Central University Project(300103131033)the Transportation Research Project of Shaanxi Transport Department(23-108 K).
文摘Data Matrix(DM)codes have been widely used in industrial production.The reading of DM code usually includes positioning and decoding.Accurate positioning is a prerequisite for successful decoding.Traditional image processing methods have poor adaptability to pollution and complex backgrounds.Although deep learning-based methods can automatically extract features,the bounding boxes cannot entirely fit the contour of the code.Further image processing methods are required for precise positioning,which will reduce efficiency.Because of the above problems,a CenterNet-based DM code key point detection network is proposed,which can directly obtain the four key points of the DM code.Compared with the existing methods,the degree of fitness is higher,which is conducive to direct decoding.To further improve the positioning accuracy,an enhanced loss function is designed,including DM code key point heatmap loss,standard DM code projection loss,and polygon Intersection-over-Union(IoU)loss,which is beneficial for the network to learn the spatial geometric characteristics of DM code.The experiment is carried out on the self-made DM code key point detection dataset,including pollution,complex background,small objects,etc.,which uses the Average Precision(AP)of the common object detection metric as the evaluation metric.AP reaches 95.80%,and Frames Per Second(FPS)gets 88.12 on the test set of the proposed dataset,which can achieve real-time performance in practical applications.
文摘A privilege flow oriented intrusion detection method based on HSMM (Hidden semi-Markov Model) is discussed. The privilege flow model and HSMM are incorporated in the implementation of an anomaly detection IDS (Intrusion Detection System). Using the dataset of DARPA 1998, our experiment results reveal good detection performance and acceptable computation cost.
基金Supported by the National Natural Science Foundation of China(No.61374140)Shanghai Pujiang Program(Project No.12PJ1402200)
文摘Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.
基金supported by the National Natural Science Foundation of China(Nos.72101026,61621063)the State Key Laboratory of Intelligent Control and Decision of Complex Systems.
文摘Human pose recognition and estimation in video is pervasive.However,the process noise and local occlusion bring great challenge to pose recognition.In this paper,we introduce the Kalman filter into pose recognition to reduce noise and solve local occlusion problem.The core of pose recognition in video is the fast detection of key points and the calculation of human steering angles.Thus,we first build a human key point detection model.Frame skipping is performed based on the Hamming distance of the hash value of every two adjacent frames in video.Noise reduction is performed on key point coordinates with the Kalman filter.To calculate the human steering angle,current state information of key points is predicted using the optimal estimation of key points at the previous time.Then human steering angle can be calculated based on current and previous state information.The improved SENet,NLNet and GCNet modules are integrated into key point detection model for improving accuracy.Tests are also given to illustrate the effectiveness of the proposed algorithm.
文摘The input document images with skew can be a serious problem in the optical character recognition system. A method is proposed for skew detection in binary document images using mathematic morphology. The basic process of our approach consists of three steps: Firstly, a dilation operation is applied to the binary image; Secondly, the dilated image is thinned; Finally, the skew angle is detected using the Hough transform. The proposed approach with high precision can detect skew with large angle (?90δ-90Δ). The experimental result shows this method is applicable and efficient.
文摘Based on Bayes' theorem we point out that the false positive rate must be lower than the intrusion base rate in order to make the Alarm Credibility Probability of the intrusion detection system exceed 50%. We present the methods that have been used in our developing intrusion detection system AIIDS (artificial immune intrusion detection systems) to increase the creditability of anomaly detection system. These methods include increasing the regularities of the system call trace by use of Hidden Markov Model (HMM), making every antibody or detector has finite lifetime, offering the detector a co-stimulate signal to illustrate whether there is damage in the system according to the integrity, confidentiality, or availability of the system resource.
基金supported by the Medical Special Cultivation Project of Anhui University of Science and Technology(Grant No.YZ2023H2B013)the Anhui Provincial Key Research and Development Project(Grant No.2022i01020015)the Open Project of Key Laboratory of Conveyance Equipment(East China Jiaotong University),Ministry of Education(KLCE2022-01).
文摘The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.
基金This research was supported by a grant(2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation(NRF)funded by the Ministry of Education,Republic of Korea.
文摘Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.
基金supported by the Liaoning Provincial Science and Technology Plan Project 2023JH2/101300205the Shenyang Science and Technology Plan Project 23-407-3-33.
文摘Human pose estimation is an important task in computer vision,which can provide key point detection of human body and obtain bone information.At present,human pose estimation is mainly utilized for detection of large targets,and there is no solution for detection of small targets.This paper proposes a multi-channel spatial information feature based human pose(MCSF-Pose)estimation algorithm to address the issue of medium and small targets inaccurate detection of human key points in scenarios involving occlusion and multiple poses.The MCSF-Pose network is a bottom-up regression network.Firstly,an UP-Focus module is designed to expand the feature information while reducing parameter computation during the up-sampling process.Then,the channel segmentation strategy is adopted to cut the features,and the feature information of multiple dimensions is retained through different convolutional groups,which reduces the parameter lightweight network model and makes up for the loss of the feature information associated with the depth of the network.Finally,the three-layer PANet structure is designed to reduce the complexity of the model.With the aid of the structure,it also to improve the detection accuracy and anti-interference ability of human key points.The experimental results indicate that the proposed algorithm outperforms YOLO-Pose and other human pose estimation algorithms on COCO2017 and MPII human pose datasets.
