Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving.This paper presents a novel 3D object detector...Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving.This paper presents a novel 3D object detector and adaptive space partitioning algorithm to infer traffic accidents quantitatively.Using 2D region proposals in an RGB image,this method generates deformable frustums based on point cloud for each 2D region proposal and then frustum-wisely extracts features based on the farthest point sampling network(FPS-Net)and feature extraction network(FE-Net).Subsequently,the encoder-decoder network(ED-Net)implements 3D-oriented bounding box(OBB)regression.Meanwhile,the adaptive least square regression(ALSR)method is proposed to split 3D OBB.Finally,the reduced OBB intersection test is carried out to detect traffic accidents via separating surface theorem(SST).In the experiments of KITTI benchmark,our proposed 3D object detector outperforms other state-of-theartmethods.Meanwhile,collision detection algorithm achieves the satisfactory performance of 91.8%accuracy on our SHTA dataset.展开更多
The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to...The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.展开更多
In order to solve the linear variable differential transformer (LVDT) displacement sensor nonlinearity of overall range and extend its working range, a novel line-element based adaptively seg- menting method for pie...In order to solve the linear variable differential transformer (LVDT) displacement sensor nonlinearity of overall range and extend its working range, a novel line-element based adaptively seg- menting method for piecewise compensating correction was proposed. According to the mechanical structure of LVDT, the output equation was calculated, and then the theoretic nonlinear source of output was analyzed. By the proposed line-element adaptive segmentation method, the nonlinear output of LVDT was divided into linear and nonlinear regions with a given threshold. Then the com- pensating correction function was designed for nonlinear parts employing polynomial regression tech- nique. The simulation of LVDT validates the feasibility of proposed scheme, and the results of cali- bration and testing experiments fully prove that the proposed method has higher accuracy than the state-of-art correction algorithms.展开更多
Dispersed fringe sensor (DFS) is an important phasing sensor of next-generation optical astronomical telescopes. The measurement errors induced by the measurement noise of three piston estimation methods for the DFS...Dispersed fringe sensor (DFS) is an important phasing sensor of next-generation optical astronomical telescopes. The measurement errors induced by the measurement noise of three piston estimation methods for the DFS including leastsquared fitting (LSF) method, frequency peak location (FPL) method and main peak position (MPP) method, are analyzed theoretically and validated experimentally in this paper. The experimental results coincide well with the theoretical analyses. The MPP, FPL, LSF are used respectively when the DFS operates with broadband light (central wavelength: 706 nm, bandwidth: 23 nm). The corresponding root mean square (RMS) value of estimated piston error can be achieved to be 1 nm, 3 nm, 26 nm, respectively. Additionally, the range of DFS with the FPL can be more than 100 μm at the same time. The FPL method can work well both in coarse and fine phasing stages with acceptable accuracy, compared with LSF method and MPP method.展开更多
A multifeature statistical image segmentation algorithm is described. Multiple features such as grey, edge magnitude and correlation are combined to form a multidimensional space statistics. The statistical algorithm ...A multifeature statistical image segmentation algorithm is described. Multiple features such as grey, edge magnitude and correlation are combined to form a multidimensional space statistics. The statistical algorithm is used to segment an image using the decision curved surface determined by the multidimensional feature function. The segmentation problem which is difficult to solve using the features independently will be readily solved using the same features jointly. An adaptive segmentation algorithm is discussed. Test results of the real-time TV tracker newly developed have shown that the segmentation algorithm discussed here improves effectively the image segmentation quality and system tracking performance.展开更多
The problem of automatic threshold selection is considered.After a brief discussion of several available techniques,an adaptive thresholding technique is proposed.It is based on the local edge information which can be...The problem of automatic threshold selection is considered.After a brief discussion of several available techniques,an adaptive thresholding technique is proposed.It is based on the local edge information which can be computed without histogramming the gray level value of the image.This method is specifically designed to deal with gradual shading situation.The effectiveness of the method is shown on a practical example.展开更多
Segmentation accuracy of dermoscopy images is important in the computer-aided diagnosis of skin cancer and a wide variety of segmentation methods for dermoscopy images have been developed. Considering that each method...Segmentation accuracy of dermoscopy images is important in the computer-aided diagnosis of skin cancer and a wide variety of segmentation methods for dermoscopy images have been developed. Considering that each method has its strengths and weaknesses, a novel adaptive segmentation framework based on multi-classification model is proposed for dermoscopy images. Firstly, five patterns of images are summarized according to the factors influencing segmentation. Then the matching relation is established between each image pattern and its optimal segmentationmethod. Next, the given image is classified into one of the five patterns by the multi-classification model based on BP neural network. Finaily, the optimal segmentation method for this image is selected according to the matching relation, and then the image is effectively segmented. Experiments show that the proposed method delivers better accuracy and more robust segmentation results compared with the other seven state-of-the-art methods.展开更多
During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be solved.In this study,the multi-scale Retinex with color restorati...During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be solved.In this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light conditions.The enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent fruit.After that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold segmentation.Finally,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture features.The users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this study.Compared with the manifold ranking algorithm,the K-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,respectively.Additionally,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared algorithms.The proposed method accurately recognized the green apples under complex illumination conditions and growth environments.Additionally,it provided effective references for intelligent growth monitoring and yield estimation of fruits.展开更多
Ground-layer adaptive optics(GLAO)has shown its potential for use in solar observation owing to its wide field-of-view(FOV)correction.A high-order GLAO system that consists of a multiple direction Shack-Hartmann wavef...Ground-layer adaptive optics(GLAO)has shown its potential for use in solar observation owing to its wide field-of-view(FOV)correction.A high-order GLAO system that consists of a multiple direction Shack-Hartmann wavefront sensor(WFS),a realtime controller with a multi-CPU processor,and a 151-element deformable mirror was developed for the 1-m New Vacuum Solar Telescope at Yunnan Observatories,Chinese Academy of Sciences.A hexagonal microlens with 9×8 subapertures is employed in the WFS.The detection FOV is 42′′×37′′,in which 9(3×3)guide regions are extracted for multiple direction wavefront sensing with a frame rate of up to 2200 Hz.To our knowledge,this is the first professional solar GLAO system used as a regularly operating instrument for scientific observations.Its installation and adjustment were performed in the summer of 2021.In this article,a detailed account of the GLAO system and its first light results and a comprehensive analysis of the performance of the GLAO system are provided.The results show that this system can effectively improve the imaging quality after compensating for the wavefront aberration due to ground-layer turbulence.展开更多
Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene.However,pre-rendering methods in the existing research work are usually us...Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene.However,pre-rendering methods in the existing research work are usually used to implement holographic visualization,which is static display.The above-mentioned methods for static display have many shortcomings,such as poor adaptability,low rendering efficiency and lack of real-time.A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction.Firstly,a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes.Secondly,a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics.Finally,a prototype system was developed,and the corresponding experimental analysis was completed.The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction.The amount of scene data can be reduced to more than 30%,which significantly improves rendering efficiency and reduces glare.展开更多
基金National Natural Science Foundation of China(No.51805312)in part by Shanghai Sailing Program(No.18YF1409400)+4 种基金in part by Training and Funding Program of Shanghai College young teachers(No.ZZGCD15102)in part by Scientific Research Project of Shanghai University of Engineering Science(No.2016-19)in part by Science and Technology Commission of Shanghai Municipality(No.19030501100)in part by the Shanghai University of Engineering Science Innovation Fund for Graduate Students(No.18KY0613)in part by National Key R&D Program of China(No.2016YFC0802900).
文摘Road accident detection plays an important role in abnormal scene reconstruction for Intelligent Transportation Systems and abnormal events warning for autonomous driving.This paper presents a novel 3D object detector and adaptive space partitioning algorithm to infer traffic accidents quantitatively.Using 2D region proposals in an RGB image,this method generates deformable frustums based on point cloud for each 2D region proposal and then frustum-wisely extracts features based on the farthest point sampling network(FPS-Net)and feature extraction network(FE-Net).Subsequently,the encoder-decoder network(ED-Net)implements 3D-oriented bounding box(OBB)regression.Meanwhile,the adaptive least square regression(ALSR)method is proposed to split 3D OBB.Finally,the reduced OBB intersection test is carried out to detect traffic accidents via separating surface theorem(SST).In the experiments of KITTI benchmark,our proposed 3D object detector outperforms other state-of-theartmethods.Meanwhile,collision detection algorithm achieves the satisfactory performance of 91.8%accuracy on our SHTA dataset.
基金Auhui Provincial Key Research and Development Project(No.202004a07020050)National Natural Science Foundation of China Youth Program(No.61901006)。
文摘The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.
基金Supported by National High Technology Research and Development Program of China("863" Program)(2011AA041002)
文摘In order to solve the linear variable differential transformer (LVDT) displacement sensor nonlinearity of overall range and extend its working range, a novel line-element based adaptively seg- menting method for piecewise compensating correction was proposed. According to the mechanical structure of LVDT, the output equation was calculated, and then the theoretic nonlinear source of output was analyzed. By the proposed line-element adaptive segmentation method, the nonlinear output of LVDT was divided into linear and nonlinear regions with a given threshold. Then the com- pensating correction function was designed for nonlinear parts employing polynomial regression tech- nique. The simulation of LVDT validates the feasibility of proposed scheme, and the results of cali- bration and testing experiments fully prove that the proposed method has higher accuracy than the state-of-art correction algorithms.
基金Project supported by the National Natural Science Foundation of China(Grant No.61008038)
文摘Dispersed fringe sensor (DFS) is an important phasing sensor of next-generation optical astronomical telescopes. The measurement errors induced by the measurement noise of three piston estimation methods for the DFS including leastsquared fitting (LSF) method, frequency peak location (FPL) method and main peak position (MPP) method, are analyzed theoretically and validated experimentally in this paper. The experimental results coincide well with the theoretical analyses. The MPP, FPL, LSF are used respectively when the DFS operates with broadband light (central wavelength: 706 nm, bandwidth: 23 nm). The corresponding root mean square (RMS) value of estimated piston error can be achieved to be 1 nm, 3 nm, 26 nm, respectively. Additionally, the range of DFS with the FPL can be more than 100 μm at the same time. The FPL method can work well both in coarse and fine phasing stages with acceptable accuracy, compared with LSF method and MPP method.
文摘A multifeature statistical image segmentation algorithm is described. Multiple features such as grey, edge magnitude and correlation are combined to form a multidimensional space statistics. The statistical algorithm is used to segment an image using the decision curved surface determined by the multidimensional feature function. The segmentation problem which is difficult to solve using the features independently will be readily solved using the same features jointly. An adaptive segmentation algorithm is discussed. Test results of the real-time TV tracker newly developed have shown that the segmentation algorithm discussed here improves effectively the image segmentation quality and system tracking performance.
文摘The problem of automatic threshold selection is considered.After a brief discussion of several available techniques,an adaptive thresholding technique is proposed.It is based on the local edge information which can be computed without histogramming the gray level value of the image.This method is specifically designed to deal with gradual shading situation.The effectiveness of the method is shown on a practical example.
基金Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant Nos. 61471016, 61371134 and 61271436).
文摘Segmentation accuracy of dermoscopy images is important in the computer-aided diagnosis of skin cancer and a wide variety of segmentation methods for dermoscopy images have been developed. Considering that each method has its strengths and weaknesses, a novel adaptive segmentation framework based on multi-classification model is proposed for dermoscopy images. Firstly, five patterns of images are summarized according to the factors influencing segmentation. Then the matching relation is established between each image pattern and its optimal segmentationmethod. Next, the given image is classified into one of the five patterns by the multi-classification model based on BP neural network. Finaily, the optimal segmentation method for this image is selected according to the matching relation, and then the image is effectively segmented. Experiments show that the proposed method delivers better accuracy and more robust segmentation results compared with the other seven state-of-the-art methods.
基金This work was supported by the National High Technology Research and Development Program of China(863 Program)[Grant number 2013AA10230402]Agricultural Science and Technology Project of Shaanxi Province[Grant number 2016NY-157]Fundamental Research Funds of Central Universities[Grant number 2452016077].The authors appreciate the above funding organizations for their financial supports.The authors would also like to thank the helpful comments and suggestions provided by all the authors cited in this article and the anonymous reviewers.
文摘During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be solved.In this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light conditions.The enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent fruit.After that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold segmentation.Finally,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture features.The users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this study.Compared with the manifold ranking algorithm,the K-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,respectively.Additionally,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared algorithms.The proposed method accurately recognized the green apples under complex illumination conditions and growth environments.Additionally,it provided effective references for intelligent growth monitoring and yield estimation of fruits.
基金supported by the National Natural Science Foundation of China(Grant Nos.11727805,and 12103057)Frontier Research Fund of Institute of Optics and Electronics,Chinese Academy of Sciences(Grant No.C21K002)Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant Nos.2021378,2020376,and 2022386)。
文摘Ground-layer adaptive optics(GLAO)has shown its potential for use in solar observation owing to its wide field-of-view(FOV)correction.A high-order GLAO system that consists of a multiple direction Shack-Hartmann wavefront sensor(WFS),a realtime controller with a multi-CPU processor,and a 151-element deformable mirror was developed for the 1-m New Vacuum Solar Telescope at Yunnan Observatories,Chinese Academy of Sciences.A hexagonal microlens with 9×8 subapertures is employed in the WFS.The detection FOV is 42′′×37′′,in which 9(3×3)guide regions are extracted for multiple direction wavefront sensing with a frame rate of up to 2200 Hz.To our knowledge,this is the first professional solar GLAO system used as a regularly operating instrument for scientific observations.Its installation and adjustment were performed in the summer of 2021.In this article,a detailed account of the GLAO system and its first light results and a comprehensive analysis of the performance of the GLAO system are provided.The results show that this system can effectively improve the imaging quality after compensating for the wavefront aberration due to ground-layer turbulence.
基金supported by National Natural Science Foundation of China:[Grant Number U2034202,42271424,42201446]Chengdu Science and Technology Program(Grant No.2021XT00001GX).
文摘Holographic projection technology can provide a more intuitive and efficient visualization effect for a digital twin bridge construction scene.However,pre-rendering methods in the existing research work are usually used to implement holographic visualization,which is static display.The above-mentioned methods for static display have many shortcomings,such as poor adaptability,low rendering efficiency and lack of real-time.A dynamic holographic modelling approach is proposed for the augmented visualization of digital twin scenes for bridge construction.Firstly,a dynamic segmentation algorithm with adaptive screen size was designed to high-efficiently generate holographic scenes.Secondly,a motion blur control method was designed to improve the rendering efficiency of holographic scenes according to human visual characteristics.Finally,a prototype system was developed,and the corresponding experimental analysis was completed.The experimental results show that the method proposed in this article can support adaptive screen size image segmentation and real-time generation of holographic scenes for bridge construction.The amount of scene data can be reduced to more than 30%,which significantly improves rendering efficiency and reduces glare.