Web-like obstacles,such as safety nets,represent a unique hazard for drones,and especially UAVs(Unmanned Aerial Vehicles).Fencing and netting are particularly difficult to distinguish from the background using either ...Web-like obstacles,such as safety nets,represent a unique hazard for drones,and especially UAVs(Unmanned Aerial Vehicles).Fencing and netting are particularly difficult to distinguish from the background using either computer vision,lidar and sonar.In contrast,animals such as flying insects may detect these web-like obstacles using Optic Flow(OF),and more precisely motion parallax.A netting-avoidance solution was proposed using a OF-based detection method.The netting detection method was based on a signature defined by the shape of the OF magnitude across the visual field.We established that the OF shape depends on the orientation of the netting in relation to the hexarotor’s movement.This paper demonstrates netting detection in real-world experiments,according to any direction flight made by the UAV along the net.The proposed NOWA method(which stands for Netting Optical floW-based distinction Algorithm)separates the OF signatures belonging to these different surfaces-netting or background-whatever their orientations.By extracting the OF signatures of these different surfaces and separating them,the proposed visual method can estimate their relative locations and orientations.In a robotic simulations,the multirotor explores and navigates automatically using this netting detection method,using saccades to avoid obstacles.In the simulations,these saccades are also used to simplify netting detection by orienting itself systematically parallel to these planes,a behavior reminiscent of flying insects.展开更多
The ability to localize moving objects within the environment is critical for autonomous robotic systems. This paper describes a moving object detection and localization system using multiple robots equipped with inex...The ability to localize moving objects within the environment is critical for autonomous robotic systems. This paper describes a moving object detection and localization system using multiple robots equipped with inexpensive optic flow sensors. We demonstrate an architecture capable of detecting motion along a plane by collecting three sets of one-dimensional optic flow data. The detected object is then localized with respect to each of the robots in the system.展开更多
This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Da...This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.展开更多
Current deformation measurement techniques suffer from limited spatial resolution. In this work, a highly accurate and high-resolution Horn Schunck optical flow method is developed and then applied to measuring the st...Current deformation measurement techniques suffer from limited spatial resolution. In this work, a highly accurate and high-resolution Horn Schunck optical flow method is developed and then applied to measuring the static deformation of a birdlike flexible airfoil at a series of angles of attack at Reynolds number 100,000 in a low speed, low noise wind tunnel. To allow relatively large displacements, a nonlinear Horn-Schunck model and a coarse-to-fine warping process are adopted. To preserve optical flow discontinuities, a nonquadratic penalization function, a multi- cue driven bilateral filtering and a principle component analysis of local image patterns are used. First, the accuracy and convergence of this Horn-Schunck technique are verified on a benchmark. Then, the maximum displacement that can be reliably calculated by this technique is studied on synthetic images. Both studies are compared with the performance of a Lucas-Kanade optical flow method. Finally, the Horn-Schunck technique is used to estimate the 3-D deformation of the birdlike airfoil through a stereoscopic camera setup. The results are compared with those computed by Lucas-Kanade optical flow, image correlation and numerical simulation.展开更多
The flapping-wing air vehicle(FWAV)is a kind of bio-inspired robot whose wings can flap up and down like bird and insect wings.A vision-based obstacle avoidance method for FWAVs is proposed in this paper.First,the Far...The flapping-wing air vehicle(FWAV)is a kind of bio-inspired robot whose wings can flap up and down like bird and insect wings.A vision-based obstacle avoidance method for FWAVs is proposed in this paper.First,the Farneback algorithm is used to calculate the optical flow field of the first-view video frames taken by the on-board image transmission camera.Based on the optical flow information,a fuzzy obstacle avoidance controller is then designed to generate the FWAV steering commands.Experimental results show that the proposed obstacle avoidance method can accurately identify obstacles and achieve obstacle avoidance for FWAVs.展开更多
This paper focuses mainly on semi-strapdown image homing guided （SSIHG） system design based on optical flow for a six-degree-of-freedom （6-DOF） axial-symmetric skid-to-turn missile. Three optical flow algorithms s...This paper focuses mainly on semi-strapdown image homing guided （SSIHG） system design based on optical flow for a six-degree-of-freedom （6-DOF） axial-symmetric skid-to-turn missile. Three optical flow algorithms suitable for large displacements are introduced and compared. The influence of different displacements on computational accuracy of the three algorithms is analyzed statistically. The total optical flow of the SSIHG missile is obtained using the Scale Invariant Feature Transform （SIFT） algorithm, which is the best among the three for large displacements. After removing the rotational optical flow caused by rotation of the gimbal and missile body from the total optical flow, the remaining translational optical flow is smoothed via Kalman filtering. The circular navigation guidance （CNG） law with impact angle constraint is then obtained utilizing the smoothed translational optical flow and position of the target image. Simulations are carried out under both disturbed and undisturbed conditions, and results indicate the proposed guidance strategy for SSIHG missiles can result in a precise target hit with a desired impact angle without the need for the time-to-go parameter.展开更多
In this paper,an improved optical flow method for image registration is proposed.It is novel in the way that it improves the optical flow method with an initial motion estimator:extended phase correlation technique(EP...In this paper,an improved optical flow method for image registration is proposed.It is novel in the way that it improves the optical flow method with an initial motion estimator:extended phase correlation technique(EPCT),using merits of the latter to compensate deficiencies of the former.In a more detailed manner,it can be said that the optical flow method can reach the sub-pixel accuracy and calculate complex distortion patterns like chirping and tilting but is weak with large-scale movements.Because EPCT covers measurements of large translations and rotations with pixel level accuracy and is efficient in the calculating load,it can be treated as a good initial motion estimator for optical flow method.Tests have proved that this improved method will significantly enhance the registration performance,especially,for images with large-scale movements and robust against random noises.展开更多
Because of its characteristics of simple algorithm and hardware, optical flow-based motion estimation has become a hot research field, especially in GPS-denied environment. Optical flow could be used to obtain the air...Because of its characteristics of simple algorithm and hardware, optical flow-based motion estimation has become a hot research field, especially in GPS-denied environment. Optical flow could be used to obtain the aircraft motion information, but the six-(degree of freedom)(6-DOF) motion still couldn't be accurately estimated by existing methods. The purpose of this work is to provide a motion estimation method based on optical flow from forward and down looking cameras, which doesn't rely on the assumption of level flight. First, the distribution and decoupling method of optical flow from forward camera are utilized to get attitude. Then, the resulted angular velocities are utilized to obtain the translational optical flow of the down camera, which can eliminate the influence of rotational motion on velocity estimation. Besides, the translational motion estimation equation is simplified by establishing the relation between the depths of feature points and the aircraft altitude. Finally, simulation results show that the method presented is accurate and robust.展开更多
A modification of Horn and Schunk＇s approach is investigated, which leads to a better preservation of flow discontinuities. It improves Horn-Schunk model in three aspects： （1） It replaces the smooth weight coeffic...A modification of Horn and Schunk＇s approach is investigated, which leads to a better preservation of flow discontinuities. It improves Horn-Schunk model in three aspects： （1） It replaces the smooth weight coefficient in the energy equation by the variable weight coefficient. （2） It adopts a novel method to compute the mean velocity. The novel method also reflects the effect of the intensity difference on the image velocity diffusion. （3） It introduces a more efficient iterative method than the Gauss-Seidel method to solve the associated Euler-Lagrange equation. The experiment results validate the better effect of the improved method on preserving discontinuities.展开更多
Globally exponential stability （which implies convergence and uniqueness） of their classical iterative algorithm is established using methods of heat equations and energy integral after embedding the discrete iterat...Globally exponential stability （which implies convergence and uniqueness） of their classical iterative algorithm is established using methods of heat equations and energy integral after embedding the discrete iteration into a continuous flow. The stability condition depends explicitly on smoothness of the image sequence, size of image domain, value of the regularization parameter, and finally discretization step. Specifically, as the discretization step approaches to zero, stability holds unconditionally. The analysis also clarifies relations among the iterative algorithm, the original variation formulation and the PDE system. The proper regularity of solution and natural images is briefly surveyed and discussed. Experimental results validate the theoretical claims both on convergence and exponential stability.展开更多
Automatic Digital Orthophoto Map(DOM)generation plays an important role in many downstream works such as land use and cover detection,urban planning,and disaster assessment.Existing DOM generation methods can generate...Automatic Digital Orthophoto Map(DOM)generation plays an important role in many downstream works such as land use and cover detection,urban planning,and disaster assessment.Existing DOM generation methods can generate promising results but always need ground object filtered DEM generation before otho-rectification;this can consume much time and produce building facade contained results.To address this problem,a pixel-by-pixel digital differential rectification-based automatic DOM generation method is proposed in this paper.Firstly,3D point clouds with texture are generated by dense image matching based on an optical flow field for a stereo pair of images,respectively.Then,the grayscale of the digital differential rectification image is extracted directly from the point clouds element by element according to the nearest neighbor method for matched points.Subsequently,the elevation is repaired grid-by-grid using the multi-layer Locally Refined B-spline(LR-B)interpolation method with triangular mesh constraint for the point clouds void area,and the grayscale is obtained by the indirect scheme of digital differential rectification to generate the pixel-by-pixel digital differentially rectified image of a single image slice.Finally,a seamline network is automatically searched using a disparity map optimization algorithm,and DOM is smartly mosaicked.The qualitative and quantitative experimental results on three datasets were produced and evaluated,which confirmed the feasibility of the proposed method,and the DOM accuracy can reach 1 Ground Sample Distance(GSD)level.The comparison experiment with the state-of-the-art commercial softwares showed that the proposed method generated DOM has a better visual effect on building boundaries and roof completeness with comparable accuracy and computational efficiency.展开更多
A closed form solution to the problem of segmenting multiple 3D motion models was proposed from straight-line optical flow. It introduced the multibody line optical flow constraint (MLOFC), a polynomial equation relat...A closed form solution to the problem of segmenting multiple 3D motion models was proposed from straight-line optical flow. It introduced the multibody line optical flow constraint (MLOFC), a polynomial equation relating motion models and line parameters. The motion models can be obtained analytically as the derivative of the MLOFC at the corresponding line measurement, without knowing the motion model associated with that line. Experiments on real and synthetic sequences were also presented.展开更多
As one of the important sea state parameters for navigation safety and coastal resource management, the ocean wave direction represents the propagation direction of the wave. A novel algorithm based on an optical flow...As one of the important sea state parameters for navigation safety and coastal resource management, the ocean wave direction represents the propagation direction of the wave. A novel algorithm based on an optical flow method is developed for the ocean wave direction inversion of the ocean wave fields imaged by the X-band radar continuously. The proposed algorithm utilizes the echo images received by the X-band wave monitoring radar to estimate the optical flow motion, and then the actual wave propagation direction can be obtained by taking a weighted average of the motion vector for each pixel. Compared with the traditional ocean wave direction inversion method based on frequency-domain, the novel algorithm is fully using a time-domain signal processing method without determination of a current velocity and a modulation transfer function（MTF）. In the meantime,the novel algorithm is simple, efficient and there is no need to do something more complicated here. Compared with traditional ocean wave direction inversion method, the ocean wave direction of derived by using this proposed method matches well with that measured by an in situ buoy nearby and the simulation data. These promising results demonstrate the efficiency and accuracy of the algorithm proposed in the paper.展开更多
This paper presents a new method for robust and accurate optical flow estimation.The sig- nificance of this work is twofold.Firstly,the idea of bi-directional scheme is adopted to reduce the model error of optical flo...This paper presents a new method for robust and accurate optical flow estimation.The sig- nificance of this work is twofold.Firstly,the idea of bi-directional scheme is adopted to reduce the model error of optical flow equation,which allows the second order Taylor's expansion of optical flow equation for accurate solution without much extra computational burden;Secondly,this paper establishs a new optical flow equation based on LSCM (Local Structure Constancy Model) instead of BCM (Brightness Constancy Model),namely the optical flow equation does not act on scalar but on tensor-valued (ma- trix-valued) field,due to the two reason:(1) structure tensor-value contains local spatial structure information,which provides us more useable cues for computation than scalar;(2) local image structure is less sensitive to illumination variation than intensity,which weakens the disturbance of non-uniform illumination in real sequences.Qualitative and quantitative results for synthetic and real-world scenes show that the new method can produce an accurate and robust results.展开更多
In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only ...In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.展开更多
In this study,we developed a novel optical-flow algorithm for determining the wall shear-stress on the surface of objects.The algorithm solves the thin-oil-film equation using a numerical scheme that recovers local fe...In this study,we developed a novel optical-flow algorithm for determining the wall shear-stress on the surface of objects.The algorithm solves the thin-oil-film equation using a numerical scheme that recovers local features neglected by smoothing filters.A variational formulation with a smoothness constraint was applied to extract the global shear-stress fields.The algorithm was then applied to scalar images generated using direct numerical simulation(DNS)method,which revealed that the errors were smaller than those of conventional methods.The application of the proposed algorithm to recover the wall shear-stress on a low-aspect-ratio wing and on an axisymmetric boattail model taken as examples in this study showed a strong potential for analysing shear-stress fields.Compared to the methods used in previous studies,proposed method reveals more local features of separation line and singular points on object surface.展开更多
To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved...To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.展开更多
Optical flow method is one of the most important methods of analyzing motion images. Optical flow field is used to analyze characteristics of motion objects. According to motion features of micro-electronic mechani-ca...Optical flow method is one of the most important methods of analyzing motion images. Optical flow field is used to analyze characteristics of motion objects. According to motion features of micro-electronic mechani-cal system (MEMS) micro-structure, the optical algorithm based on label field and neighborhood optimization is presented to analyze the in-plane micro-motion of micro-structure. Firstly, high speed motion states for each fre-quency segment of micro-structure in cyclic motion are frozen based on stroboscopic principle. Thus a series of dynamic images of micro-structure are obtained. Secondly, the presented optical algorithm is used to analyze the image sequences, and can obtain reliable and precise optical field and reduce computing time. As micro-resonator of testing object, the phase-amplitude curve of micro-structure is derived. Experimental results indicate that the meas-urement precision of the presented algorithm is high, and measurement repeatability reaches 40 nm under the same experiment condition.展开更多
Deformable medical image registration plays a vital role in medical image applications,such as placing different temporal images at the same time point or different modality images into the same coordinate system.Vari...Deformable medical image registration plays a vital role in medical image applications,such as placing different temporal images at the same time point or different modality images into the same coordinate system.Various strategies have been developed to satisfy the increasing needs of deformable medical image registration.One popular registration method is estimating the displacement field by computing the optical flow between two images.The motion field(flow field)is computed based on either gray-value or handcrafted descriptors such as the scale-invariant feature transform(SIFT).These methods assume that illumination is constant between images.However,medical images may not always satisfy this assumption.In this study,we propose a metric learning-based motion estimation method called Siamese Flow for deformable medical image registration.We train metric learners using a Siamese network,which produces an image patch descriptor that guarantees a smaller feature distance in two similar anatomical structures and a larger feature distance in two dissimilar anatomical structures.In the proposed registration framework,the flow field is computed based on such features and is close to the real deformation field due to the excellent feature representation ability of the Siamese network.Experimental results demonstrate that the proposed method outperforms the Demons,SIFT Flow,Elastix,and VoxelMorph networks regarding registration accuracy and robustness,particularly with large deformations.展开更多
基金The participation of X.D.in this research was made possible by the joint PhD grant from the Agence Innovation Défense(AID)and Aix-Marseille UniversityFinancial support for the running costs was provided via a ProxiLearn project grant(ANR-19-ASTR-0009)to F.R.+2 种基金via SpotReturn project grant(ANR-21-ASRO-0001-02)to T.R.and F.R.from the ANR(Astrid Program)X.D.and F.R.were also supported by Aix Marseille University and the CNRS(Life Science,Information Science,and Engineering and Science&technology Institutes)The facilities for the experimental tests has been mainly provided by ROBOTEX 2.0(Grants ROBOTEX ANR-10-EQPX-44-01 and TIRREX ANR-21-ESRE-0015).
文摘Web-like obstacles,such as safety nets,represent a unique hazard for drones,and especially UAVs(Unmanned Aerial Vehicles).Fencing and netting are particularly difficult to distinguish from the background using either computer vision,lidar and sonar.In contrast,animals such as flying insects may detect these web-like obstacles using Optic Flow(OF),and more precisely motion parallax.A netting-avoidance solution was proposed using a OF-based detection method.The netting detection method was based on a signature defined by the shape of the OF magnitude across the visual field.We established that the OF shape depends on the orientation of the netting in relation to the hexarotor’s movement.This paper demonstrates netting detection in real-world experiments,according to any direction flight made by the UAV along the net.The proposed NOWA method(which stands for Netting Optical floW-based distinction Algorithm)separates the OF signatures belonging to these different surfaces-netting or background-whatever their orientations.By extracting the OF signatures of these different surfaces and separating them,the proposed visual method can estimate their relative locations and orientations.In a robotic simulations,the multirotor explores and navigates automatically using this netting detection method,using saccades to avoid obstacles.In the simulations,these saccades are also used to simplify netting detection by orienting itself systematically parallel to these planes,a behavior reminiscent of flying insects.
文摘The ability to localize moving objects within the environment is critical for autonomous robotic systems. This paper describes a moving object detection and localization system using multiple robots equipped with inexpensive optic flow sensors. We demonstrate an architecture capable of detecting motion along a plane by collecting three sets of one-dimensional optic flow data. The detected object is then localized with respect to each of the robots in the system.
基金Scientific Research and Development Project of Hebei Meteorological Bureau(23ky08).
文摘This study proposes a novel radar echo extrapolation algorithm,OF-ConvGRU,which integrates Optical Flow(OF)and Convolutional Gated Recurrent Unit(ConvGRU)methods for improved nowcasting.Using the Standardized Radar Dataset of the Guangdong-Hong Kong-Macao Greater Bay Area,the performance of OF-ConvGRU was evaluated against OF and ConvGRU methods.Threat Score(TS)and Bias Score(BIAS)were employed to assess extrapolation accuracy across various echo intensities(20-50 dBz)and weather phenomena.Results demonstrate that OF-ConvGRU significantly enhances prediction accuracy for moderate-intensity echoes(30-40 dBz),effectively combining OF s precise motion estimation with ConvGRU s nonlinear learning capabilities.However,challenges persist in low-intensity(20 dBz)and high-intensity(50 dBz)echo predictions.The study reveals distinct advantages of each method in specific contexts,highlighting the importance of multi-method approaches in operational nowcasting.OF-ConvGRU shows promise in balancing short-term accuracy with long-term stability,particularly for complex weather systems.
文摘Current deformation measurement techniques suffer from limited spatial resolution. In this work, a highly accurate and high-resolution Horn Schunck optical flow method is developed and then applied to measuring the static deformation of a birdlike flexible airfoil at a series of angles of attack at Reynolds number 100,000 in a low speed, low noise wind tunnel. To allow relatively large displacements, a nonlinear Horn-Schunck model and a coarse-to-fine warping process are adopted. To preserve optical flow discontinuities, a nonquadratic penalization function, a multi- cue driven bilateral filtering and a principle component analysis of local image patterns are used. First, the accuracy and convergence of this Horn-Schunck technique are verified on a benchmark. Then, the maximum displacement that can be reliably calculated by this technique is studied on synthetic images. Both studies are compared with the performance of a Lucas-Kanade optical flow method. Finally, the Horn-Schunck technique is used to estimate the 3-D deformation of the birdlike airfoil through a stereoscopic camera setup. The results are compared with those computed by Lucas-Kanade optical flow, image correlation and numerical simulation.
基金This work was supported in part by the National Natural Science Foundation of China(Nos.61803025,62073031)the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)(No.FRF-IDRY-19010)the Beijing Top Discipline for Artificial Intelligent Science and Engineering,University of Science and Technology Beijing.
文摘The flapping-wing air vehicle(FWAV)is a kind of bio-inspired robot whose wings can flap up and down like bird and insect wings.A vision-based obstacle avoidance method for FWAVs is proposed in this paper.First,the Farneback algorithm is used to calculate the optical flow field of the first-view video frames taken by the on-board image transmission camera.Based on the optical flow information,a fuzzy obstacle avoidance controller is then designed to generate the FWAV steering commands.Experimental results show that the proposed obstacle avoidance method can accurately identify obstacles and achieve obstacle avoidance for FWAVs.
基金supported by the Armament Research Fund of China (No.9020A02010313BQ01)
文摘This paper focuses mainly on semi-strapdown image homing guided （SSIHG） system design based on optical flow for a six-degree-of-freedom （6-DOF） axial-symmetric skid-to-turn missile. Three optical flow algorithms suitable for large displacements are introduced and compared. The influence of different displacements on computational accuracy of the three algorithms is analyzed statistically. The total optical flow of the SSIHG missile is obtained using the Scale Invariant Feature Transform （SIFT） algorithm, which is the best among the three for large displacements. After removing the rotational optical flow caused by rotation of the gimbal and missile body from the total optical flow, the remaining translational optical flow is smoothed via Kalman filtering. The circular navigation guidance （CNG） law with impact angle constraint is then obtained utilizing the smoothed translational optical flow and position of the target image. Simulations are carried out under both disturbed and undisturbed conditions, and results indicate the proposed guidance strategy for SSIHG missiles can result in a precise target hit with a desired impact angle without the need for the time-to-go parameter.
文摘In this paper,an improved optical flow method for image registration is proposed.It is novel in the way that it improves the optical flow method with an initial motion estimator:extended phase correlation technique(EPCT),using merits of the latter to compensate deficiencies of the former.In a more detailed manner,it can be said that the optical flow method can reach the sub-pixel accuracy and calculate complex distortion patterns like chirping and tilting but is weak with large-scale movements.Because EPCT covers measurements of large translations and rotations with pixel level accuracy and is efficient in the calculating load,it can be treated as a good initial motion estimator for optical flow method.Tests have proved that this improved method will significantly enhance the registration performance,especially,for images with large-scale movements and robust against random noises.
基金Project(2012CB720003)supported by the National Basic Research Program of ChinaProjects(61320106010,61127007,61121003,61573019)supported by the National Natural Science Foundation of ChinaProject(2013DFE13040)supported by the Special Program for International Science and Technology Cooperation from Ministry of Science and Technology of China
文摘Because of its characteristics of simple algorithm and hardware, optical flow-based motion estimation has become a hot research field, especially in GPS-denied environment. Optical flow could be used to obtain the aircraft motion information, but the six-(degree of freedom)(6-DOF) motion still couldn't be accurately estimated by existing methods. The purpose of this work is to provide a motion estimation method based on optical flow from forward and down looking cameras, which doesn't rely on the assumption of level flight. First, the distribution and decoupling method of optical flow from forward camera are utilized to get attitude. Then, the resulted angular velocities are utilized to obtain the translational optical flow of the down camera, which can eliminate the influence of rotational motion on velocity estimation. Besides, the translational motion estimation equation is simplified by establishing the relation between the depths of feature points and the aircraft altitude. Finally, simulation results show that the method presented is accurate and robust.
文摘A modification of Horn and Schunk＇s approach is investigated, which leads to a better preservation of flow discontinuities. It improves Horn-Schunk model in three aspects： （1） It replaces the smooth weight coefficient in the energy equation by the variable weight coefficient. （2） It adopts a novel method to compute the mean velocity. The novel method also reflects the effect of the intensity difference on the image velocity diffusion. （3） It introduces a more efficient iterative method than the Gauss-Seidel method to solve the associated Euler-Lagrange equation. The experiment results validate the better effect of the improved method on preserving discontinuities.
基金Foundation item： Projects（60835005, 90820302） supported by the National Natural Science Foundation of China Project（2007CB311001） supported by the National Basic Research Program of China
文摘Globally exponential stability （which implies convergence and uniqueness） of their classical iterative algorithm is established using methods of heat equations and energy integral after embedding the discrete iteration into a continuous flow. The stability condition depends explicitly on smoothness of the image sequence, size of image domain, value of the regularization parameter, and finally discretization step. Specifically, as the discretization step approaches to zero, stability holds unconditionally. The analysis also clarifies relations among the iterative algorithm, the original variation formulation and the PDE system. The proper regularity of solution and natural images is briefly surveyed and discussed. Experimental results validate the theoretical claims both on convergence and exponential stability.
基金supported by the National Natural Science Foundation of China[Grant No.41771479]the National High-Resolution Earth Observation System(the Civil Part)[Grant No.50-H31D01-0508-13/15]the Japan Society for the Promotion of Science[Grant No.22H03573].
文摘Automatic Digital Orthophoto Map(DOM)generation plays an important role in many downstream works such as land use and cover detection,urban planning,and disaster assessment.Existing DOM generation methods can generate promising results but always need ground object filtered DEM generation before otho-rectification;this can consume much time and produce building facade contained results.To address this problem,a pixel-by-pixel digital differential rectification-based automatic DOM generation method is proposed in this paper.Firstly,3D point clouds with texture are generated by dense image matching based on an optical flow field for a stereo pair of images,respectively.Then,the grayscale of the digital differential rectification image is extracted directly from the point clouds element by element according to the nearest neighbor method for matched points.Subsequently,the elevation is repaired grid-by-grid using the multi-layer Locally Refined B-spline(LR-B)interpolation method with triangular mesh constraint for the point clouds void area,and the grayscale is obtained by the indirect scheme of digital differential rectification to generate the pixel-by-pixel digital differentially rectified image of a single image slice.Finally,a seamline network is automatically searched using a disparity map optimization algorithm,and DOM is smartly mosaicked.The qualitative and quantitative experimental results on three datasets were produced and evaluated,which confirmed the feasibility of the proposed method,and the DOM accuracy can reach 1 Ground Sample Distance(GSD)level.The comparison experiment with the state-of-the-art commercial softwares showed that the proposed method generated DOM has a better visual effect on building boundaries and roof completeness with comparable accuracy and computational efficiency.
基金The National Natural Science Foundation of China (No. 60675017) The National Basic Research Program (973) of China (No. 2006CB303103)
文摘A closed form solution to the problem of segmenting multiple 3D motion models was proposed from straight-line optical flow. It introduced the multibody line optical flow constraint (MLOFC), a polynomial equation relating motion models and line parameters. The motion models can be obtained analytically as the derivative of the MLOFC at the corresponding line measurement, without knowing the motion model associated with that line. Experiments on real and synthetic sequences were also presented.
基金The National Key Research and Development Program of China under contract No.2016YFC0800405the Shanghai Municipal Science and Technology Project of China under contract No.15DZ0500600the Specialized Research Fund for the Doctoral Program of Higher Education of China under contract No.2014212020203
文摘As one of the important sea state parameters for navigation safety and coastal resource management, the ocean wave direction represents the propagation direction of the wave. A novel algorithm based on an optical flow method is developed for the ocean wave direction inversion of the ocean wave fields imaged by the X-band radar continuously. The proposed algorithm utilizes the echo images received by the X-band wave monitoring radar to estimate the optical flow motion, and then the actual wave propagation direction can be obtained by taking a weighted average of the motion vector for each pixel. Compared with the traditional ocean wave direction inversion method based on frequency-domain, the novel algorithm is fully using a time-domain signal processing method without determination of a current velocity and a modulation transfer function（MTF）. In the meantime,the novel algorithm is simple, efficient and there is no need to do something more complicated here. Compared with traditional ocean wave direction inversion method, the ocean wave direction of derived by using this proposed method matches well with that measured by an in situ buoy nearby and the simulation data. These promising results demonstrate the efficiency and accuracy of the algorithm proposed in the paper.
基金Supported by the National Natural Science Foundation of China(No.60672153)the Shenzhen Science & Technology Project (No.200424).
文摘This paper presents a new method for robust and accurate optical flow estimation.The sig- nificance of this work is twofold.Firstly,the idea of bi-directional scheme is adopted to reduce the model error of optical flow equation,which allows the second order Taylor's expansion of optical flow equation for accurate solution without much extra computational burden;Secondly,this paper establishs a new optical flow equation based on LSCM (Local Structure Constancy Model) instead of BCM (Brightness Constancy Model),namely the optical flow equation does not act on scalar but on tensor-valued (ma- trix-valued) field,due to the two reason:(1) structure tensor-value contains local spatial structure information,which provides us more useable cues for computation than scalar;(2) local image structure is less sensitive to illumination variation than intensity,which weakens the disturbance of non-uniform illumination in real sequences.Qualitative and quantitative results for synthetic and real-world scenes show that the new method can produce an accurate and robust results.
文摘In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.
文摘In this study,we developed a novel optical-flow algorithm for determining the wall shear-stress on the surface of objects.The algorithm solves the thin-oil-film equation using a numerical scheme that recovers local features neglected by smoothing filters.A variational formulation with a smoothness constraint was applied to extract the global shear-stress fields.The algorithm was then applied to scalar images generated using direct numerical simulation(DNS)method,which revealed that the errors were smaller than those of conventional methods.The application of the proposed algorithm to recover the wall shear-stress on a low-aspect-ratio wing and on an axisymmetric boattail model taken as examples in this study showed a strong potential for analysing shear-stress fields.Compared to the methods used in previous studies,proposed method reveals more local features of separation line and singular points on object surface.
基金National Natural Science Foundation of China(61701029)。
文摘To improve the detection accuracy and robustness of crowd anomaly detection,especially crowd emergency evacuation detection,the abnormal crowd behavior detection method is proposed.This method is based on the improved statistical global optical flow entropy which can better describe the degree of chaos of crowd.First,the optical flow field is extracted from the video sequences and a 2D optical flow histogram is gained.Then,the improved optical flow entropy,combining information theory with statistical physics is calculated from 2D optical flow histograms.Finally,the anomaly can be detected according to the abnormality judgment formula.The experimental results show that the detection accuracy achieved over 95%in three public video datasets,which indicates that the proposed algorithm outperforms other state-of-the-art algorithms.
基金Supported by Youth Natural Science Foundation of Beijing University of Chemical Technology (No.QN0734).
文摘Optical flow method is one of the most important methods of analyzing motion images. Optical flow field is used to analyze characteristics of motion objects. According to motion features of micro-electronic mechani-cal system (MEMS) micro-structure, the optical algorithm based on label field and neighborhood optimization is presented to analyze the in-plane micro-motion of micro-structure. Firstly, high speed motion states for each fre-quency segment of micro-structure in cyclic motion are frozen based on stroboscopic principle. Thus a series of dynamic images of micro-structure are obtained. Secondly, the presented optical algorithm is used to analyze the image sequences, and can obtain reliable and precise optical field and reduce computing time. As micro-resonator of testing object, the phase-amplitude curve of micro-structure is derived. Experimental results indicate that the meas-urement precision of the presented algorithm is high, and measurement repeatability reaches 40 nm under the same experiment condition.
基金This study was supported in part by the Sichuan Science and Technology Program(2019YFH0085,2019ZDZX0005,2019YFG0196)in part by the Foundation of Chengdu University of Information Technology(No.KYTZ202008).
文摘Deformable medical image registration plays a vital role in medical image applications,such as placing different temporal images at the same time point or different modality images into the same coordinate system.Various strategies have been developed to satisfy the increasing needs of deformable medical image registration.One popular registration method is estimating the displacement field by computing the optical flow between two images.The motion field(flow field)is computed based on either gray-value or handcrafted descriptors such as the scale-invariant feature transform(SIFT).These methods assume that illumination is constant between images.However,medical images may not always satisfy this assumption.In this study,we propose a metric learning-based motion estimation method called Siamese Flow for deformable medical image registration.We train metric learners using a Siamese network,which produces an image patch descriptor that guarantees a smaller feature distance in two similar anatomical structures and a larger feature distance in two dissimilar anatomical structures.In the proposed registration framework,the flow field is computed based on such features and is close to the real deformation field due to the excellent feature representation ability of the Siamese network.Experimental results demonstrate that the proposed method outperforms the Demons,SIFT Flow,Elastix,and VoxelMorph networks regarding registration accuracy and robustness,particularly with large deformations.
