Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is proposed...Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is proposed.The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire interferences.Secondly,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved.Then,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire flame.Finally,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were obtained.The video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.展开更多
Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracki...Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so on.Therefore, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space.展开更多
Image restoration is an image processing technology with great practical value in the field of computer vision.It is a computer technology that estimates the image information of the damaged area according to the resi...Image restoration is an image processing technology with great practical value in the field of computer vision.It is a computer technology that estimates the image information of the damaged area according to the residual image information of the damaged image and carries out automatic repair.This article firstly classify and summarize image restoration algorithms,and describe recent advances in the research respectively from three aspects including image restoration based on partial differential equation,based on the texture of image restoration and based on deep learning,then make the brief analysis of digital image restoration of subjective and objective evaluation method,and briefly summarize application of digital image restoration technique in the future and prospects,provide direction for the research on image after repair.展开更多
The accurate and robust unmanned aerial vehicle(UAV)localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground environments.Existing r...The accurate and robust unmanned aerial vehicle(UAV)localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground environments.Existing range-based localization approaches fundamentally rely on the assumption that the environment is relatively ideal,which enables a precise range for localization.However,radio propagation in the underground environments may be dramatically influenced by various equipments,obstacles,and ambient noises.In this case,inaccurate range measurements and intermittent ranging failures inevitably occur,which leads to severe localization performance degradation.To address the challenges,a novel UAV localization scheme is proposed in this paper,which can effectively handle unreliable observations in hostile underground environments.We first propose an adaptive extended Kalman filter(EKF)based on the fusion of ultra-wideband(UWB)and inertial measurement unit(IMU)to detect and adjust the inaccurate range measurements.Aiming to deal with intermittent ranging failures,we further design the constraint condition by limiting the system state.Specifically,the auto-regressive model is proposed to implement the localization in the ranging blind areas by reconstructing the lost measurements.Finally,extensive simulations have been conducted to verify the effectiveness.We carry out field experiments in an underground garage and a coal mine based on P440 UWB sensors.Results show that the localization accuracy is improved by 16.9%compared with the recent methods in the hostile underground environments.展开更多
1 Introduction Boolean functions have important applications in stream ciphers and block ciphers.Over the last decades,the constructions of cryptographic Boolean functions have paid a lot of attention[1,2].Direct sum ...1 Introduction Boolean functions have important applications in stream ciphers and block ciphers.Over the last decades,the constructions of cryptographic Boolean functions have paid a lot of attention[1,2].Direct sum is a well-known secondary construction of cryptographic functions[3].By using the direct sum,a lot of functions with high nonlinearities can be obtained[4,5].However,the direct sum of two functions are decomposable functions,which have numerous null secondorder derivatives(which represents a potential weakness with respect to the higher order differential attack)[6].(In)decomposable functions were also studied in[7]by Zheng and Zhang under the name(non)separable functions.They provided some sufficient conditions that the functions are indecomposable[7].展开更多
基金This works were supported by National Natural Science Foundation of China(Grant No.51874300)the National Natural Science Foundation of China and Shanxi Provincial People’s Government Jointly Funded Project of China for Coal Base and Low Carbon(Grant No.U1510115)+1 种基金the Qing Lan Project,the China Postdoctoral Science Foundation(No.2013T60574)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(Grant No.YJKYYQ20170074).
文摘Aiming at the defects of the traditional fire detection methods,which are caused by false positives and false negatives in large space buildings,a fire identification detection method based on video images is proposed.The algorithm first uses the hybrid Gaussian background modeling method and the RGB color model to perform fire prejudgment on the video image,which can eliminate most non-fire interferences.Secondly,the traditional regional growth algorithm is improved and the fire image segmentation effect is effectively improved.Then,based on the segmented image,the dynamic and static features of the fire flame are further analyzed and extracted in the area of the suspected fire flame.Finally,the dynamic features of the extracted fire flame images were fused and classified by improved fruit fly optimization support vector machine,and the recognition results were obtained.The video-based fire detection method proposed in this paper greatly improves the accuracy of fire detection and is suitable for fire detection and identification in large space scenarios.
基金supported by National Natural Science Foundationof China (Grant No. 51874300)the National Natural Science Foundation of China andShanxi Provincial People’s Government Jointly Funded Project of China for Coal Baseand Low Carbon (Grant No. U1510115)+2 种基金National Natural Science Foundation of China(51104157)the Qing Lan Project, the China Postdoctoral Science Foundation (Grant No.2013T60574)the Scientific Instrument Developing Project of the Chinese Academy ofSciences (Grant No. YJKYYQ20170074).
文摘Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so on.Therefore, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space.
基金The research is supported by National Natural Science Foundation of China(Grant No.51874300)the National Natural Science Foundation of China and Shanxi Provincial People’s Government Jointly Funded Project of China for Coal Base and Low Carbon(Grant No.U1510115)+2 种基金National Natural Science Foundation of China(51104157)the Qing Lan Project,the China Postdoctoral Science Foundation(Grant No.2013T60574)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(Grant No.YJKYYQ20170074).
文摘Image restoration is an image processing technology with great practical value in the field of computer vision.It is a computer technology that estimates the image information of the damaged area according to the residual image information of the damaged image and carries out automatic repair.This article firstly classify and summarize image restoration algorithms,and describe recent advances in the research respectively from three aspects including image restoration based on partial differential equation,based on the texture of image restoration and based on deep learning,then make the brief analysis of digital image restoration of subjective and objective evaluation method,and briefly summarize application of digital image restoration technique in the future and prospects,provide direction for the research on image after repair.
基金supported by the National Natural Science Foundation of China under Grant No.62272462the Natural Science Foundation of Jiangsu Province of China for Distinguished Young Scholars under Grant No.BK20230045the Shenzhen Science and Technology Program under Grant No.JCYJ20230807154300002.
文摘The accurate and robust unmanned aerial vehicle(UAV)localization is significant due to the requirements of safety-critical monitoring and emergency wireless communication in hostile underground environments.Existing range-based localization approaches fundamentally rely on the assumption that the environment is relatively ideal,which enables a precise range for localization.However,radio propagation in the underground environments may be dramatically influenced by various equipments,obstacles,and ambient noises.In this case,inaccurate range measurements and intermittent ranging failures inevitably occur,which leads to severe localization performance degradation.To address the challenges,a novel UAV localization scheme is proposed in this paper,which can effectively handle unreliable observations in hostile underground environments.We first propose an adaptive extended Kalman filter(EKF)based on the fusion of ultra-wideband(UWB)and inertial measurement unit(IMU)to detect and adjust the inaccurate range measurements.Aiming to deal with intermittent ranging failures,we further design the constraint condition by limiting the system state.Specifically,the auto-regressive model is proposed to implement the localization in the ranging blind areas by reconstructing the lost measurements.Finally,extensive simulations have been conducted to verify the effectiveness.We carry out field experiments in an underground garage and a coal mine based on P440 UWB sensors.Results show that the localization accuracy is improved by 16.9%compared with the recent methods in the hostile underground environments.
基金This work was supported by the Fundamental Research Funds for the Central Universities of China(2015QNA38)the Natural Science Foundation of China(Grant No.61972400).
文摘1 Introduction Boolean functions have important applications in stream ciphers and block ciphers.Over the last decades,the constructions of cryptographic Boolean functions have paid a lot of attention[1,2].Direct sum is a well-known secondary construction of cryptographic functions[3].By using the direct sum,a lot of functions with high nonlinearities can be obtained[4,5].However,the direct sum of two functions are decomposable functions,which have numerous null secondorder derivatives(which represents a potential weakness with respect to the higher order differential attack)[6].(In)decomposable functions were also studied in[7]by Zheng and Zhang under the name(non)separable functions.They provided some sufficient conditions that the functions are indecomposable[7].
