Deformation behavior of non-rigid airships in wind tunnel tests is studied by considering three factors, including internal pressure, flow velocity and angle of attack. Fiber Bragg grating strain sensors are used to m...Deformation behavior of non-rigid airships in wind tunnel tests is studied by considering three factors, including internal pressure, flow velocity and angle of attack. Fiber Bragg grating strain sensors are used to measure the deformation of non-rigid airships. Wind tunnel tests in the case of different flow velocities and angles of attack are conducted. The measurement results reveal that the airship deformation is in proportion to internal pressure. For the tensile region,the airship deformation is in proportion to flow velocity. Effects of angle of attack on structural deformation are more complicated and there is no clear relationship existing between airship deformation and angle of attack.展开更多
Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual informa...Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.展开更多
Due to the importance of vibration effects on the functional accuracy of mechanical systems,this research aims to develop a precise model of a nonlinearly vibrating single-link mobile flexible manipulator.The manipula...Due to the importance of vibration effects on the functional accuracy of mechanical systems,this research aims to develop a precise model of a nonlinearly vibrating single-link mobile flexible manipulator.The manipulator consists of an elastic arm,a rotary motor,and a rigid carrier,and undergoes general in-plane rigid body motion along with elastic transverse deformation.To accurately model the elastic behavior,Timoshenko’s beam theory is used to describe the flexible arm,which accounts for rotary inertia and shear deformation effects.By applying Newton’s second law,the nonlinear governing equations of motion for the manipulator are derived as a coupled system of ordinary differential equations(ODEs)and partial differential equations(PDEs).Then,the assumed mode method(AMM)is used to solve this nonlinear system of governing equations with appropriate shape functions.The assumed modes can be obtained after solving the characteristic equation of a Timoshenko beam with clamped boundary conditions at one end and an attached mass/inertia at the other.In addition,the effect of the transverse vibration of the inextensible arm on its axial behavior is investigated.Despite the axial rigidity,the effect makes the rigid body dynamics invalid for the axial behavior of the arm.Finally,numerical simulations are conducted to evaluate the performance of the developed model,and the results are compared with those obtained by the finite element approach.The comparison confirms the validity of the proposed dynamic model for the system.According to the mentioned features,this model can be reliable for investigating the system’s vibrational behavior and implementing vibration control algorithms.展开更多
Optical flow estimation in human facial video,which provides 2D correspondences between adjacent frames,is a fundamental pre-processing step for many applications,like facial expression capture and recognition.However...Optical flow estimation in human facial video,which provides 2D correspondences between adjacent frames,is a fundamental pre-processing step for many applications,like facial expression capture and recognition.However,it is quite challenging as human facial images contain large areas of similar textures,rich expressions,and large rotations.These characteristics also result in the scarcity of large,annotated realworld datasets.We propose a robust and accurate method to learn facial optical flow in a self-supervised manner.Specifically,we utilize various shape priors,including face depth,landmarks,and parsing,to guide the self-supervised learning task via a differentiable nonrigid registration framework.Extensive experiments demonstrate that our method achieves remarkable improvements for facial optical flow estimation in the presence of significant expressions and large rotations.展开更多
The traditional pipeline for non-rigid registration is to iteratively update the correspondence and alignment such that the transformed source surface aligns well with the target surface.Among the pipeline,the corresp...The traditional pipeline for non-rigid registration is to iteratively update the correspondence and alignment such that the transformed source surface aligns well with the target surface.Among the pipeline,the correspondence construction and iterative manner are key to the results,while existing strategies might result in local optima.In this paper,we adopt the widely used deformation graph-based representation,while replacing some key modules with neural learning-based strategies.Specifically,we design a neural network to predict the correspondence and its reliability confidence rather than the strategies like nearest neighbor search and pair rejection.Besides,we adopt the GRU-based recurrent network for iterative refinement,which is more robust than the traditional strategy.The model is trained in a self-supervised manner and thus can be used for arbitrary datasets without ground-truth.Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art methods by a large margin.展开更多
A mutual information-based non-rigid medical image registration algorithm is presented. An approximate function of Hanning windowed sinc is used as kernel function of partial volume (PV) interpolation to estimate the ...A mutual information-based non-rigid medical image registration algorithm is presented. An approximate function of Hanning windowed sinc is used as kernel function of partial volume (PV) interpolation to estimate the joint histogram, which is the key to calculating the mutual information. And a new method is proposed to compute the gradient of mutual information with respect to the model parameters. The transformation of object is modeled by a free-form deformation (FFD) based on B-splines. The experiments on 3D synthetic and real image data show that the algorithm can converge at the global optimum and restrain the emergency of local extreme.展开更多
Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how t...Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how to build them automatically.Therefore,in this paper,we propose a robust method to compute such priors automatically,where a global and local combined strategy is adopted.These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences.To further utilize the matches,this paper also proposes a novel registration method based on the Coherent Point Drift framework.This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations.Qualitative and quantitative experiments demonstrate the advantages of the proposed method.展开更多
The purpose of the study was to evaluate the effect of motion compensation by non-rigid registration combined with the Karhunen-Loeve Transform (KLT) filter on the signal to noise (SNR) and contrast-to-noise ratio (CN...The purpose of the study was to evaluate the effect of motion compensation by non-rigid registration combined with the Karhunen-Loeve Transform (KLT) filter on the signal to noise (SNR) and contrast-to-noise ratio (CNR) of hybrid gradient-echo echoplanar (GRE-EPI) first-pass myocardial perfusion imaging. Twenty one consecutive first-pass adenosine stress perfusion MR data sets interpreted positive for ischemia or infarction were processed by non-rigid Registration followed by KLT filtering. SNR and CNR were measured in abnormal and normal myocardium in unfiltered and KLT filtered images following nonrigid registration to compensate for respiratory and other motions. Image artifacts introduced by filtering in registered and nonregistered images were evaluated by two observers. There was a statistically sig- nificant increase in both SNR and CNR between normal and abnormal myocardium with KLT filtering (mean SNR increased by 62.18% ± 21.05% and mean CNR increased by 58.84% ± 18.06%;p = 0.01). Motion correction prior to KLT filtering reduced significantly the occurrence of filter induced artifacts (KLT only-artifacts in 42 out of 55 image series vs. registered plus KLT-artifacts in 3 out of 55 image series). In conclusion the combination of non-rigid registration and KLT filtering was shown to increase the SNR and CNR of GRE-EPI perfusion images. Subjective evaluation of image artifacts revealed that prior motion compensation significantly reduced the artifacts introduced by the KLT filtering process.展开更多
Accuracy validation is essential to clinical application of medical image registration techniques. Registration validation remains a challenging problem in practice mainly due to lack of 'ground truth'. In thi...Accuracy validation is essential to clinical application of medical image registration techniques. Registration validation remains a challenging problem in practice mainly due to lack of 'ground truth'. In this paper, an overview of current validation methods for medical image registration is presented with detailed discussion of their benefits and drawbacks. Special focus is on non-rigid registration validation. Promising solution is also discussed.展开更多
基金supported by the National Natural Science Foundation of China(Nos.11472276,11332011 and 11502268)
文摘Deformation behavior of non-rigid airships in wind tunnel tests is studied by considering three factors, including internal pressure, flow velocity and angle of attack. Fiber Bragg grating strain sensors are used to measure the deformation of non-rigid airships. Wind tunnel tests in the case of different flow velocities and angles of attack are conducted. The measurement results reveal that the airship deformation is in proportion to internal pressure. For the tensile region,the airship deformation is in proportion to flow velocity. Effects of angle of attack on structural deformation are more complicated and there is no clear relationship existing between airship deformation and angle of attack.
文摘Mutual information is widely used in medical image registration, because it does not require preprocessing the image. However, the local maximum problem in the registration is insurmountable. We combine mutual information and gradient information to solve this problem and apply it to the non-rigid deformation image registration. To improve the accuracy, we provide some implemental issues, for example, the Powell searching algorithm, gray interpolation and consideration of outlier points. The experimental results show the accuracy of the method and the feasibility in non-rigid medical image registration.
文摘Due to the importance of vibration effects on the functional accuracy of mechanical systems,this research aims to develop a precise model of a nonlinearly vibrating single-link mobile flexible manipulator.The manipulator consists of an elastic arm,a rotary motor,and a rigid carrier,and undergoes general in-plane rigid body motion along with elastic transverse deformation.To accurately model the elastic behavior,Timoshenko’s beam theory is used to describe the flexible arm,which accounts for rotary inertia and shear deformation effects.By applying Newton’s second law,the nonlinear governing equations of motion for the manipulator are derived as a coupled system of ordinary differential equations(ODEs)and partial differential equations(PDEs).Then,the assumed mode method(AMM)is used to solve this nonlinear system of governing equations with appropriate shape functions.The assumed modes can be obtained after solving the characteristic equation of a Timoshenko beam with clamped boundary conditions at one end and an attached mass/inertia at the other.In addition,the effect of the transverse vibration of the inextensible arm on its axial behavior is investigated.Despite the axial rigidity,the effect makes the rigid body dynamics invalid for the axial behavior of the arm.Finally,numerical simulations are conducted to evaluate the performance of the developed model,and the results are compared with those obtained by the finite element approach.The comparison confirms the validity of the proposed dynamic model for the system.According to the mentioned features,this model can be reliable for investigating the system’s vibrational behavior and implementing vibration control algorithms.
基金This work was supported by National Natural Science Foundation of China(No.62122071)the Youth Innovation Promotion Association CAS(No.2018495)+1 种基金the Fundamental Research Funds for the Central Universities(No.WK3470000021)through the Alibaba Innovation Research Program(AIR).
文摘Optical flow estimation in human facial video,which provides 2D correspondences between adjacent frames,is a fundamental pre-processing step for many applications,like facial expression capture and recognition.However,it is quite challenging as human facial images contain large areas of similar textures,rich expressions,and large rotations.These characteristics also result in the scarcity of large,annotated realworld datasets.We propose a robust and accurate method to learn facial optical flow in a self-supervised manner.Specifically,we utilize various shape priors,including face depth,landmarks,and parsing,to guide the self-supervised learning task via a differentiable nonrigid registration framework.Extensive experiments demonstrate that our method achieves remarkable improvements for facial optical flow estimation in the presence of significant expressions and large rotations.
基金supported by National Natural Science Foundation of China(No.62122071)the Youth Innovation Promotion Association CAS(No.2018495)“the Fundamental Research Funds for the Central Universities”(No.WK3470000021).
文摘The traditional pipeline for non-rigid registration is to iteratively update the correspondence and alignment such that the transformed source surface aligns well with the target surface.Among the pipeline,the correspondence construction and iterative manner are key to the results,while existing strategies might result in local optima.In this paper,we adopt the widely used deformation graph-based representation,while replacing some key modules with neural learning-based strategies.Specifically,we design a neural network to predict the correspondence and its reliability confidence rather than the strategies like nearest neighbor search and pair rejection.Besides,we adopt the GRU-based recurrent network for iterative refinement,which is more robust than the traditional strategy.The model is trained in a self-supervised manner and thus can be used for arbitrary datasets without ground-truth.Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art methods by a large margin.
基金Supported bythe National Basic Research Programof China ("973"Program) (No2003CB716103)Key Project of Shanghai Scienceand Technology Committee(No05DZ19509)
文摘A mutual information-based non-rigid medical image registration algorithm is presented. An approximate function of Hanning windowed sinc is used as kernel function of partial volume (PV) interpolation to estimate the joint histogram, which is the key to calculating the mutual information. And a new method is proposed to compute the gradient of mutual information with respect to the model parameters. The transformation of object is modeled by a free-form deformation (FFD) based on B-splines. The experiments on 3D synthetic and real image data show that the algorithm can converge at the global optimum and restrain the emergency of local extreme.
基金supported by Natural Science Foundation of Anhui Province (2108085MF210,1908085MF187)Key Natural Science Fund of Department of Eduction of Anhui Province (KJ2021A0042)Natural Social Science Foundation of China (19BTY091).
文摘Non-rigid registration of point clouds is still far from stable,especially for the largely deformed one.Sparse initial correspondences are often adopted to facilitate the process.However,there are few studies on how to build them automatically.Therefore,in this paper,we propose a robust method to compute such priors automatically,where a global and local combined strategy is adopted.These priors in different degrees of deformation are obtained by the locally geometrical-consistent point matches from the globally structural-consistent region correspondences.To further utilize the matches,this paper also proposes a novel registration method based on the Coherent Point Drift framework.This method takes both the spatial proximity and local structural consistency of the priors as supervision of the registration process and thus obtains a robust alignment for clouds with significantly different deformations.Qualitative and quantitative experiments demonstrate the advantages of the proposed method.
文摘The purpose of the study was to evaluate the effect of motion compensation by non-rigid registration combined with the Karhunen-Loeve Transform (KLT) filter on the signal to noise (SNR) and contrast-to-noise ratio (CNR) of hybrid gradient-echo echoplanar (GRE-EPI) first-pass myocardial perfusion imaging. Twenty one consecutive first-pass adenosine stress perfusion MR data sets interpreted positive for ischemia or infarction were processed by non-rigid Registration followed by KLT filtering. SNR and CNR were measured in abnormal and normal myocardium in unfiltered and KLT filtered images following nonrigid registration to compensate for respiratory and other motions. Image artifacts introduced by filtering in registered and nonregistered images were evaluated by two observers. There was a statistically sig- nificant increase in both SNR and CNR between normal and abnormal myocardium with KLT filtering (mean SNR increased by 62.18% ± 21.05% and mean CNR increased by 58.84% ± 18.06%;p = 0.01). Motion correction prior to KLT filtering reduced significantly the occurrence of filter induced artifacts (KLT only-artifacts in 42 out of 55 image series vs. registered plus KLT-artifacts in 3 out of 55 image series). In conclusion the combination of non-rigid registration and KLT filtering was shown to increase the SNR and CNR of GRE-EPI perfusion images. Subjective evaluation of image artifacts revealed that prior motion compensation significantly reduced the artifacts introduced by the KLT filtering process.
基金Supported by National Basic Research Program of China Grant(2011CB707701)National Natural Science Foundation of China(81127003)
文摘Accuracy validation is essential to clinical application of medical image registration techniques. Registration validation remains a challenging problem in practice mainly due to lack of 'ground truth'. In this paper, an overview of current validation methods for medical image registration is presented with detailed discussion of their benefits and drawbacks. Special focus is on non-rigid registration validation. Promising solution is also discussed.
