The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to impr...The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to improve visual information of the vehicle driver in low visibility conditions is put forward based on infrared and visible image fusion technique.The wavelet image confusion algorithm is adopted to decompose the image into low-frequency approximation components and high-frequency detail components.Low-frequency component contains information representing gray value differences.High-frequency component contains the detail information of the image,which is frequently represented by gray standard deviation to assess image quality.To extract feature information of low-frequency component and high-frequency component with different emphases,different fusion operators are used separately by low-frequency and high-frequency components.In the processing of low-frequency component,the fusion rule of weighted regional energy proportion is adopted to improve the brightness of the image,and the fusion rule of weighted regional proportion of standard deviation is used in all the three high-frequency components to enhance the image contrast.The experiments on image fusion of infrared and visible light demonstrate that this image fusion method can effectively improve the image brightness and contrast,and it is suitable for vision enhancement of the low-visibility images.展开更多
Processing underwater digital images is critical in ocean engineering,biology,and environmental studies,focusing on challenges such as poor lighting,image de-scattering,and color restoration.Due to environmental condi...Processing underwater digital images is critical in ocean engineering,biology,and environmental studies,focusing on challenges such as poor lighting,image de-scattering,and color restoration.Due to environmental conditions on the sea floor,improving image contrast and clarity is essential for underwater navigation and obstacle avoidance.Particularly in turbid,low-visibility waters,we require robust computer vision techniques and algorithms.Over the past decade,various models for underwater image enrichment have been proposed to address quality and visibility issues under dynamic and natural lighting conditions.This research article aims to evaluate various image improvement methods and propose a robust model that improves image quality,addresses turbidity,and enhances color,ultimately improving obstacle avoidance in autonomous systems.The proposed model demonstrates high accuracy compared to traditional models.The result analysis indicates the proposed model produces images with greatly improved visibility and exceptional color accuracy.Furthermore,research can unlock new possibilities for underwater exploration,monitoring,and intervention by advancing the state-of-the-art models in this domain.展开更多
Knowledge of impact conditions is critical to evaluating the terminal impact performance of a projectile.For a small caliber bullet,in-flight velocity has been precisely measured for decades using detection screens,bu...Knowledge of impact conditions is critical to evaluating the terminal impact performance of a projectile.For a small caliber bullet,in-flight velocity has been precisely measured for decades using detection screens,but accurately quantifying the orientation of the bullet on a target has been more challenging.This report introduces the Automated Small-Arms Photogrammetry(ASAP)analysis method used to measure,model,and predict the orientation of a small caliber bullet before reaching an impact surface.ASAP uses advanced hardware developed by Sydor Technologies to record a series of infrared digital photographs.Individual images(four orthogonal pairs)are processed using computer vision algorithms to quantify the orientation of the projectile and re-project its precise position and orientation into a three-dimensional muzzle-fixed coordinate system.An epicyclic motion model is fit to the measured data,and the epicyclic motion is extrapolated to the target location.Analysis results are fairly immediate and may be reviewed during testing.Prove-out demonstrations have shown that the impact-angle prediction capability is less than six hundredths of a degree for the 5.56 mm ball round tested.Keywords:Yaw,Terminal ballistics,Exterior ballistics,Test&evaluation,Computer vision,Image processing,Angle of展开更多
In this study "the leftest trace algorithm " was used to solve the trace of cells edge better. It also overcame the shortage that use sobel operator and laplace operator to detect the edge of wood cells. Thi...In this study "the leftest trace algorithm " was used to solve the trace of cells edge better. It also overcame the shortage that use sobel operator and laplace operator to detect the edge of wood cells. This realized the rapid extraction of the anatologic shape features in across compression and make possible the wood species could be characterized quantitatively.展开更多
基金the Science and Technology Development Program of Beijing Municipal Commission of Education (No.KM201010011002)the National College Students'Scientific Research and Entrepreneurial Action Plan(SJ201401011)
文摘The rise of urban traffic flow highlights the growing importance of traffic safety.In order to reduce the occurrence rate of traffic accidents,and improve front vision information of vehicle drivers,the method to improve visual information of the vehicle driver in low visibility conditions is put forward based on infrared and visible image fusion technique.The wavelet image confusion algorithm is adopted to decompose the image into low-frequency approximation components and high-frequency detail components.Low-frequency component contains information representing gray value differences.High-frequency component contains the detail information of the image,which is frequently represented by gray standard deviation to assess image quality.To extract feature information of low-frequency component and high-frequency component with different emphases,different fusion operators are used separately by low-frequency and high-frequency components.In the processing of low-frequency component,the fusion rule of weighted regional energy proportion is adopted to improve the brightness of the image,and the fusion rule of weighted regional proportion of standard deviation is used in all the three high-frequency components to enhance the image contrast.The experiments on image fusion of infrared and visible light demonstrate that this image fusion method can effectively improve the image brightness and contrast,and it is suitable for vision enhancement of the low-visibility images.
文摘Processing underwater digital images is critical in ocean engineering,biology,and environmental studies,focusing on challenges such as poor lighting,image de-scattering,and color restoration.Due to environmental conditions on the sea floor,improving image contrast and clarity is essential for underwater navigation and obstacle avoidance.Particularly in turbid,low-visibility waters,we require robust computer vision techniques and algorithms.Over the past decade,various models for underwater image enrichment have been proposed to address quality and visibility issues under dynamic and natural lighting conditions.This research article aims to evaluate various image improvement methods and propose a robust model that improves image quality,addresses turbidity,and enhances color,ultimately improving obstacle avoidance in autonomous systems.The proposed model demonstrates high accuracy compared to traditional models.The result analysis indicates the proposed model produces images with greatly improved visibility and exceptional color accuracy.Furthermore,research can unlock new possibilities for underwater exploration,monitoring,and intervention by advancing the state-of-the-art models in this domain.
文摘Knowledge of impact conditions is critical to evaluating the terminal impact performance of a projectile.For a small caliber bullet,in-flight velocity has been precisely measured for decades using detection screens,but accurately quantifying the orientation of the bullet on a target has been more challenging.This report introduces the Automated Small-Arms Photogrammetry(ASAP)analysis method used to measure,model,and predict the orientation of a small caliber bullet before reaching an impact surface.ASAP uses advanced hardware developed by Sydor Technologies to record a series of infrared digital photographs.Individual images(four orthogonal pairs)are processed using computer vision algorithms to quantify the orientation of the projectile and re-project its precise position and orientation into a three-dimensional muzzle-fixed coordinate system.An epicyclic motion model is fit to the measured data,and the epicyclic motion is extrapolated to the target location.Analysis results are fairly immediate and may be reviewed during testing.Prove-out demonstrations have shown that the impact-angle prediction capability is less than six hundredths of a degree for the 5.56 mm ball round tested.Keywords:Yaw,Terminal ballistics,Exterior ballistics,Test&evaluation,Computer vision,Image processing,Angle of
文摘In this study "the leftest trace algorithm " was used to solve the trace of cells edge better. It also overcame the shortage that use sobel operator and laplace operator to detect the edge of wood cells. This realized the rapid extraction of the anatologic shape features in across compression and make possible the wood species could be characterized quantitatively.
