The success of robot-assisted pelvic fracture reduction surgery heavily relies on the accuracy of 3D/3D feature-based registration.This process involves extracting anatomical feature points from pre-operative 3D image...The success of robot-assisted pelvic fracture reduction surgery heavily relies on the accuracy of 3D/3D feature-based registration.This process involves extracting anatomical feature points from pre-operative 3D images which can be challenging because of the complex and variable structure of the pelvis.PointMLP_RegNet,a modified PointMLP,was introduced to address this issue.It retains the feature extraction module of PointMLP but replaces the classification layer with a regression layer to predict the coordinates of feature points instead of conducting regular classification.A flowchart for an automatic feature points extraction method was presented,and a series of experiments was conducted on a clinical pelvic dataset to confirm the accuracy and effectiveness of the method.PointMLP_RegNet extracted feature points more accurately,with 8 out of 10 points showing less than 4 mm errors and the remaining two less than 5 mm.Compared to PointNettt and PointNet,it exhibited higher accuracy,robustness and space efficiency.The proposed method will improve the accuracy of anatomical feature points extraction,enhance intra-operative registration precision and facilitate the widespread clinical application of robot-assisted pelvic fracture reduction.展开更多
Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more compl...Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more complex.Secondly,the quality impairment generally involves both geometric attributes and color properties,where the measurement of the geometric distortion becomes more complex.We propose a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System(HVS)and the intrinsic characteristics of the point cloud.The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling,and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors(KNN)clustering.For geometric distortion,the Point Feature Histogram(PFH)is extracted as the feature descriptor,and the Earth Mover’s Distance(EMD)between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement.For color distortion,the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement.Finally,the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement.The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time.Meanwhile,the experimental results also demonstrate the robustness of the proposed method across various distortion types.The source codes are available at https://github.com/llsurreal919/Point Cloud Quality Assessment.展开更多
In photogrammetry and remote sensing,image matching is a basic and crucial process for automatic DEM generation.In this paper we presented a image relaxation matching method based on feature points.This method can be ...In photogrammetry and remote sensing,image matching is a basic and crucial process for automatic DEM generation.In this paper we presented a image relaxation matching method based on feature points.This method can be considered as an extention of regular grid point based matching.It avoids the shortcome of grid point based matching.For example,with this method,we can avoid low or even no texture area where errors frequently appear in cross correlaton matching.In the mean while,it makes full use of some mature techniques such as probability relaxation,image pyramid and the like which have already been successfully used in grid point matching process.Application of the technique to DEM generaton in different regions proved that it is more reasonable and reliable.展开更多
An algorithm for automatically extracting feature points is developed after the area of feature points in 2-dimensional (2D) imagebeing located by probability theory, correlated methods and criterion for abnormity. Fe...An algorithm for automatically extracting feature points is developed after the area of feature points in 2-dimensional (2D) imagebeing located by probability theory, correlated methods and criterion for abnormity. Feature points in 2D image can be extracted only by calculating standard deviation of gray within sampled pixels area in our approach statically. While extracting feature points, the limitation to confirm threshold by tentative method according to some a priori information on processing image can be avoided. It is proved that the proposed algorithm is valid and reliable by extracting feature points on actual natural images with abundant and weak texture, including multi-object with complex background, respectively. It can meet the demand of extracting feature points of 2D image automatically in machine vision system.展开更多
<div style="text-align:justify;"> This paper is aiming to obtain an arm-root curve function performing the human arm-root size and shape realistically. A gypsum replica of upper arm for young male was ...<div style="text-align:justify;"> This paper is aiming to obtain an arm-root curve function performing the human arm-root size and shape realistically. A gypsum replica of upper arm for young male was made and scanned for extracting the 3D coordinates of 4 feature points of shoulder point, the anterior/posterior armpit point and the axillary point describing the real arm-root shape under the normalized definitions, and the 5 landmarks were confirmed additionally for improving the fitting precision. Then, the wholly and piecewise fitting of arm-root curve with 9 feature points and mark points in total were generated respectively based on least square polynomial fitting method. Comparing to the wholly fitting, the piecewise fitted function segmented by the line between anterior and posterior axillary points showed a high fitting degree of arm-root morphology with R-square of 1, the length difference between fitted curve and gypsum curve is 0.003 cm within error range. And it provided a basic curve model with standard feature points to simulate arm-root morphology realistically by curve fitting for accurate body measurement extraction. </div>展开更多
The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor l...The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.展开更多
Current research of binocular vision systems mainly need to resolve the camera’s intrinsic parameters before the reconstruction of three-dimensional(3D)objects.The classical Zhang’calibration is hardly to calculate ...Current research of binocular vision systems mainly need to resolve the camera’s intrinsic parameters before the reconstruction of three-dimensional(3D)objects.The classical Zhang’calibration is hardly to calculate all errors caused by perspective distortion and lens distortion.Also,the image-matching algorithm of the binocular vision system still needs to be improved to accelerate the reconstruction speed of welding pool surfaces.In this paper,a preset coordinate system was utilized for camera calibration instead of Zhang’calibration.The binocular vision system was modified to capture images of welding pool surfaces by suppressing the strong arc interference during gas metal arc welding.Combining and improving the algorithms of speeded up robust features,binary robust invariant scalable keypoints,and KAZE,the feature information of points(i.e.,RGB values,pixel coordinates)was extracted as the feature vector of the welding pool surface.Based on the characteristics of the welding images,a mismatch-elimination algorithm was developed to increase the accuracy of image-matching algorithms.The world coordinates of matching feature points were calculated to reconstruct the 3D shape of the welding pool surface.The effectiveness and accuracy of the reconstruction of welding pool surfaces were verified by experimental results.This research proposes the development of binocular vision algorithms that can reconstruct the surface of welding pools accurately to realize intelligent welding control systems in the future.展开更多
Aimed at the problems of a traditional ant colony algorithm,such as the path search direction and field of view,an inability to find the shortest path,a propensity toward deadlock and an unsmooth path,an ant colony al...Aimed at the problems of a traditional ant colony algorithm,such as the path search direction and field of view,an inability to find the shortest path,a propensity toward deadlock and an unsmooth path,an ant colony algorithm for use in a new environment is proposed.First,the feature points of an obstacle are extracted to preprocess the grid map environment,which can avoid entering a trap and solve the deadlock problem.Second,these feature points are used as pathfinding access nodes to reduce the node access,with more moving directions to be selected,and the locations of the feature points to be selected determine the range of the pathfinding field of view.Then,based on the feature points,an unequal distribution of pheromones and a two-way parallel path search are used to improve the construction efficiency of the solution,an improved heuristic function is used to enhance the guiding role of the path search,and the pheromone volatilization coefficient is dynamically adjusted to avoid a premature convergence of the algorithm.Third,a Bezier curve is used to smooth the shortest path obtained.Finally,using grid maps with a different complexity and different scales,a simulation comparing the results of the proposed algorithm with those of traditional and other improved ant colony algorithms verifies its feasibility and superiority.展开更多
Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body imag...Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90 %.The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7 %. Our method processed a frame in an average time of 71.1 ms.展开更多
With the rapid development of the machining and manufacturing industry,welding has been widely used in forming connections of structural parts.At present,manual methods are often used for welding and quality inspectio...With the rapid development of the machining and manufacturing industry,welding has been widely used in forming connections of structural parts.At present,manual methods are often used for welding and quality inspection,with low efficiency and unstable product quality.Due to the requirements of visual inspection of weld feature size,a visual inspection system for weld feature size based on line structured light(LSL)is designed and built in this paper.An adaptive light stripe sub-pixel center extraction algorithm and a feature point extraction algorithm for welding light stripe are proposed.The experiment results show that the detection error of the weld width is 0.216 mm,the detection error of the remaining height is 0.035 mm,the single measurement costs 109 ms,and the inspection stability and repeatability of the system is 1%.Our approach can meet the online detection requirements of practical applications.展开更多
To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machine...To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.展开更多
A new method for iris recognition using a multi-matching system based on a simplified deformable model of the human iris was proposed. The method defined iris feature points and formed the feature space based on a wa...A new method for iris recognition using a multi-matching system based on a simplified deformable model of the human iris was proposed. The method defined iris feature points and formed the feature space based on a wavelet transform. In the matching stage it worked in a crude manner. Driven by a simplified deformable iris model, the crude matching was refined. By means of such multi-matching system, the task of iris recognition was accomplished. This process can preserve the elastic deformation between an input iris image and a template and improve precision for iris recognition. The experimental results indicate the va- lidity of this method.展开更多
Remote sensing image analysis is a basic and practical research hotspot in remote sensing science.Remote sensing images contain abundant ground object information and it can be used in urban planning,agricultural moni...Remote sensing image analysis is a basic and practical research hotspot in remote sensing science.Remote sensing images contain abundant ground object information and it can be used in urban planning,agricultural monitoring,ecological services,geological exploration and other aspects.In this paper,we propose a lightweight model combining vgg-16 and u-net network.By combining two convolutional neural networks,we classify scenes of remote sensing images.While ensuring the accuracy of the model,try to reduce the memory of themodel.According to the experimental results of this paper,we have improved the accuracy of the model to 98%.The memory size of the model is 3.4 MB.At the same time,The classification and convergence speed of the model are greatly improved.We simultaneously take the remote sensing scene image of 64×64 as input into the designed model.As the accuracy of the model is 97%,it is proved that the model designed in this paper is also suitable for remote sensing images with few target feature points and low accuracy.Therefore,the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.展开更多
Simultaneous location and mapping(SLAM)plays the crucial role in VR/AR application,autonomous robotics navigation,UAV remote control,etc.The traditional SLAM is not good at handle the data acquired by camera with fast...Simultaneous location and mapping(SLAM)plays the crucial role in VR/AR application,autonomous robotics navigation,UAV remote control,etc.The traditional SLAM is not good at handle the data acquired by camera with fast movement or severe jittering,and the efficiency need to be improved.The paper proposes an improved SLAM algorithm,which mainly improves the real-time performance of classical SLAM algorithm,applies KDtree for efficient organizing feature points,and accelerates the feature points correspondence building.Moreover,the background map reconstruction thread is optimized,the SLAM parallel computation ability is increased.The color images experiments demonstrate that the improved SLAM algorithm holds better realtime performance than the classical SLAM.展开更多
The visual inspection is an economical and effective method for welding. For measuring the feature sizes of grooves,a method based on line structured light is presented. Firstly,an adaptive algorithm to extract the su...The visual inspection is an economical and effective method for welding. For measuring the feature sizes of grooves,a method based on line structured light is presented. Firstly,an adaptive algorithm to extract the subpixel centerline of structured light stripes is introduced to deal with the uneven width and grayscale distributions of laser stripes,which is based on the quadratic weighted grayscale centroid. By means of region-of-interest(ROI)division and image difference,an image preprocessing algorithm is developed for filtering noise and improving image quality. Furthermore,to acquire geometrical dimensions of various grooves and groove types precisely,the subpixel feature point extraction algorithm of grooves is designed. Finally, experimental results of feature size measuring show that the absolute error of measurement is 0.031—0.176 mm,and the relative error of measurement is 0.2%—3.6%.展开更多
In this paper, a new content-based image watermarking scheme is proposed. The Harris-Laplace detector is adopted to extract feature points, which can survive a variety of attacks. The local characteristic regions （L...In this paper, a new content-based image watermarking scheme is proposed. The Harris-Laplace detector is adopted to extract feature points, which can survive a variety of attacks. The local characteristic regions （LCRs） are adaptively constructed based on scale-space theory. Then, the LCRs are mapped to geometrically invariant space by using image normalization technique. Finally, several copies of the digital watermark are embedded into the nonoverlapped LCRs by quantizing the magnitude vectors of discrete Fourier transform （DFT） coefficients. By binding a watermark with LCR, resilience against desynchronization attacks can be readily obtained. Simulation results show that the proposed scheme is invisible and robust against various attacks which includes common signals processing and desynchronization attacks.展开更多
Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced ...Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts;off-road condition,obstacles and extreme operating condition could still trigger NVH problems unexpectedly.This paper proposes a vehicular electronic image stabilization(EIS)system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles.Firstly,feature point detection and matching based on an oriented FAST and rotated BRIEF(ORB)algorithm are implemented to match images in the process of EIS.Furthermore,a novel improved random sampling consensus algorithm(i-RANSAC)is proposed to eliminate mismatched feature points and increase the matching accuracy significantly.And an adaptive Kalman filter(AKF)is applied to improve the adaptability of the vehicular EIS.Finally,an experimental platform based on a gasoline model car was established to validate its performance.The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.展开更多
Isogeometric analysis(IGA)is introduced to establish the direct link between computer-aided design and analysis.It is commonly implemented by Galerkin formulations(isogeometric Galerkin,IGA-G)through the use of nonuni...Isogeometric analysis(IGA)is introduced to establish the direct link between computer-aided design and analysis.It is commonly implemented by Galerkin formulations(isogeometric Galerkin,IGA-G)through the use of nonuniform rational B-splines(NURBS)basis functions for geometric design and analysis.Another promising approach,isogeometric collocation(IGA-C),working directly with the strong form of the partial differential equation(PDE)over the physical domain defined by NURBS geometry,calculates the derivatives of the numerical solution at the chosen collocation points.In a typical IGA,the knot vector of the NURBS numerical solution is only determined by the physical domain.A new perspective on the IGAmethod is proposed in this study to improve the accuracy and convergence of the solution.Solving the PDE with IGA can be regarded as fitting the load function defined on the NURBS geometry(right-hand side)with derivatives of the NURBS numerical solution(left-hand side).Moreover,the design of the knot vector has a close relationship to theNURBS functions to be fitted in the area of data fitting in geometric design.Therefore,the detected feature points of the load function are integrated into the initial knot vector of the physical domainto construct thenewknot vector of thenumerical solution.Then,they are connected seamlessly with the IGA-C framework for its great potential combining the accuracy and smoothness merits with the computational efficiency,which we call isogeometric collocation by fitting load function(IGACL).In numerical experiments,we implement our method to solve 1D,2D,and 3D PDEs and demonstrate the improvement in accuracy by comparing it with the standard IGA-C method.We also verify the superiority in the accuracy of our knot selection scheme when employed in the IGA-G method,which we call isogeometric Galerkin by fitting load function(IGA-GL).展开更多
Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speed...Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2020YFB1313800)the National Science Foundation of China(Grant No.NSFC62373259)+1 种基金the Natural Science Foundation of Top Talent of SZTU(Grant No.GDRC202303)the Education Promotion Foundation of Guangdong Province(Grant No.2022ZDJS115).
文摘The success of robot-assisted pelvic fracture reduction surgery heavily relies on the accuracy of 3D/3D feature-based registration.This process involves extracting anatomical feature points from pre-operative 3D images which can be challenging because of the complex and variable structure of the pelvis.PointMLP_RegNet,a modified PointMLP,was introduced to address this issue.It retains the feature extraction module of PointMLP but replaces the classification layer with a regression layer to predict the coordinates of feature points instead of conducting regular classification.A flowchart for an automatic feature points extraction method was presented,and a series of experiments was conducted on a clinical pelvic dataset to confirm the accuracy and effectiveness of the method.PointMLP_RegNet extracted feature points more accurately,with 8 out of 10 points showing less than 4 mm errors and the remaining two less than 5 mm.Compared to PointNettt and PointNet,it exhibited higher accuracy,robustness and space efficiency.The proposed method will improve the accuracy of anatomical feature points extraction,enhance intra-operative registration precision and facilitate the widespread clinical application of robot-assisted pelvic fracture reduction.
基金supported in part by the National Natural Science Foundation of China under Grant(62171257,U22B2001,U19A2052,62020106011,62061015)in part by the Natural Science Foundation of Chongqing under Grant(2023NSCQMSX2930)+1 种基金in part by the Youth Innovation Group Support Program of ICE Discipline of CQUPT under Grant(SCIE-QN-2022-05)in part by the Graduate Scientifc Research and Innovation Project of Chongqing under Grant(CYS22469).
文摘Perceptual quality assessment for point cloud is critical for immersive metaverse experience and is a challenging task.Firstly,because point cloud is formed by unstructured 3D points that makes the topology more complex.Secondly,the quality impairment generally involves both geometric attributes and color properties,where the measurement of the geometric distortion becomes more complex.We propose a perceptual point cloud quality assessment model that follows the perceptual features of Human Visual System(HVS)and the intrinsic characteristics of the point cloud.The point cloud is first pre-processed to extract the geometric skeleton keypoints with graph filtering-based re-sampling,and local neighboring regions around the geometric skeleton keypoints are constructed by K-Nearest Neighbors(KNN)clustering.For geometric distortion,the Point Feature Histogram(PFH)is extracted as the feature descriptor,and the Earth Mover’s Distance(EMD)between the PFHs of the corresponding local neighboring regions in the reference and the distorted point clouds is calculated as the geometric quality measurement.For color distortion,the statistical moments between the corresponding local neighboring regions are computed as the color quality measurement.Finally,the global perceptual quality assessment model is obtained as the linear weighting aggregation of the geometric and color quality measurement.The experimental results on extensive datasets show that the proposed method achieves the leading performance as compared to the state-of-the-art methods with less computing time.Meanwhile,the experimental results also demonstrate the robustness of the proposed method across various distortion types.The source codes are available at https://github.com/llsurreal919/Point Cloud Quality Assessment.
基金Funded by the Open Researeh Fund Program of the Geomatics and Applications Laboratory,Liaoning Technical University(No.2004010).
文摘In photogrammetry and remote sensing,image matching is a basic and crucial process for automatic DEM generation.In this paper we presented a image relaxation matching method based on feature points.This method can be considered as an extention of regular grid point based matching.It avoids the shortcome of grid point based matching.For example,with this method,we can avoid low or even no texture area where errors frequently appear in cross correlaton matching.In the mean while,it makes full use of some mature techniques such as probability relaxation,image pyramid and the like which have already been successfully used in grid point matching process.Application of the technique to DEM generaton in different regions proved that it is more reasonable and reliable.
文摘An algorithm for automatically extracting feature points is developed after the area of feature points in 2-dimensional (2D) imagebeing located by probability theory, correlated methods and criterion for abnormity. Feature points in 2D image can be extracted only by calculating standard deviation of gray within sampled pixels area in our approach statically. While extracting feature points, the limitation to confirm threshold by tentative method according to some a priori information on processing image can be avoided. It is proved that the proposed algorithm is valid and reliable by extracting feature points on actual natural images with abundant and weak texture, including multi-object with complex background, respectively. It can meet the demand of extracting feature points of 2D image automatically in machine vision system.
文摘<div style="text-align:justify;"> This paper is aiming to obtain an arm-root curve function performing the human arm-root size and shape realistically. A gypsum replica of upper arm for young male was made and scanned for extracting the 3D coordinates of 4 feature points of shoulder point, the anterior/posterior armpit point and the axillary point describing the real arm-root shape under the normalized definitions, and the 5 landmarks were confirmed additionally for improving the fitting precision. Then, the wholly and piecewise fitting of arm-root curve with 9 feature points and mark points in total were generated respectively based on least square polynomial fitting method. Comparing to the wholly fitting, the piecewise fitted function segmented by the line between anterior and posterior axillary points showed a high fitting degree of arm-root morphology with R-square of 1, the length difference between fitted curve and gypsum curve is 0.003 cm within error range. And it provided a basic curve model with standard feature points to simulate arm-root morphology realistically by curve fitting for accurate body measurement extraction. </div>
文摘The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.
基金Supported by National Natural Science Foundation of China(Grant No.51775313)Major Program of Shandong Province Natural Science Foundation(Grant No.ZR2018ZC1760)Young Scholars Program of Shandong University(Grant No.2017WLJH24).
文摘Current research of binocular vision systems mainly need to resolve the camera’s intrinsic parameters before the reconstruction of three-dimensional(3D)objects.The classical Zhang’calibration is hardly to calculate all errors caused by perspective distortion and lens distortion.Also,the image-matching algorithm of the binocular vision system still needs to be improved to accelerate the reconstruction speed of welding pool surfaces.In this paper,a preset coordinate system was utilized for camera calibration instead of Zhang’calibration.The binocular vision system was modified to capture images of welding pool surfaces by suppressing the strong arc interference during gas metal arc welding.Combining and improving the algorithms of speeded up robust features,binary robust invariant scalable keypoints,and KAZE,the feature information of points(i.e.,RGB values,pixel coordinates)was extracted as the feature vector of the welding pool surface.Based on the characteristics of the welding images,a mismatch-elimination algorithm was developed to increase the accuracy of image-matching algorithms.The world coordinates of matching feature points were calculated to reconstruct the 3D shape of the welding pool surface.The effectiveness and accuracy of the reconstruction of welding pool surfaces were verified by experimental results.This research proposes the development of binocular vision algorithms that can reconstruct the surface of welding pools accurately to realize intelligent welding control systems in the future.
基金the National Natural Science Founda-tion(Nos.62063019 and 61763026)the Gansu Nat-ural Science Foundation Project(No.20JR10RA152)the Gansu Provincial Department of Educa-tion:Excellent Graduate“Innovation Star”Project(No.2021CXZX-507)。
文摘Aimed at the problems of a traditional ant colony algorithm,such as the path search direction and field of view,an inability to find the shortest path,a propensity toward deadlock and an unsmooth path,an ant colony algorithm for use in a new environment is proposed.First,the feature points of an obstacle are extracted to preprocess the grid map environment,which can avoid entering a trap and solve the deadlock problem.Second,these feature points are used as pathfinding access nodes to reduce the node access,with more moving directions to be selected,and the locations of the feature points to be selected determine the range of the pathfinding field of view.Then,based on the feature points,an unequal distribution of pheromones and a two-way parallel path search are used to improve the construction efficiency of the solution,an improved heuristic function is used to enhance the guiding role of the path search,and the pheromone volatilization coefficient is dynamically adjusted to avoid a premature convergence of the algorithm.Third,a Bezier curve is used to smooth the shortest path obtained.Finally,using grid maps with a different complexity and different scales,a simulation comparing the results of the proposed algorithm with those of traditional and other improved ant colony algorithms verifies its feasibility and superiority.
文摘Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90 %.The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7 %. Our method processed a frame in an average time of 71.1 ms.
基金supported by the National Natural Science Foundation of China(No. 51975293)the Aeronautical Science Foundation of China(No. 2019ZD052010)
文摘With the rapid development of the machining and manufacturing industry,welding has been widely used in forming connections of structural parts.At present,manual methods are often used for welding and quality inspection,with low efficiency and unstable product quality.Due to the requirements of visual inspection of weld feature size,a visual inspection system for weld feature size based on line structured light(LSL)is designed and built in this paper.An adaptive light stripe sub-pixel center extraction algorithm and a feature point extraction algorithm for welding light stripe are proposed.The experiment results show that the detection error of the weld width is 0.216 mm,the detection error of the remaining height is 0.035 mm,the single measurement costs 109 ms,and the inspection stability and repeatability of the system is 1%.Our approach can meet the online detection requirements of practical applications.
基金National Natural Science Foundation of China(No.519705449)。
文摘To automatically detecting whether a person is wearing mask properly,we propose a face mask detection algorithm based on hue-saturation-value(HSV)+histogram of oriented gradient(HOG)features and support vector machines(SVM).Firstly,human face and five feature points are detected with RetinaFace face detection algorithm.The feature points are used to locate to mouth and nose region,and HSV+HOG features of this region are extracted and input to SVM for training to realize detection of wearing masks or not.Secondly,RetinaFace is used to locate to nasal tip area of face,and YCrCb elliptical skin tone model is used to detect the exposure of skin in the nasal tip area,and the optimal classification threshold can be found to determine whether the wear is properly according to experimental results.Experiments show that the accuracy of detecting whether mask is worn can reach 97.9%,and the accuracy of detecting whether mask is worn correctly can reach 87.55%,which verifies the feasibility of the algorithm.
文摘A new method for iris recognition using a multi-matching system based on a simplified deformable model of the human iris was proposed. The method defined iris feature points and formed the feature space based on a wavelet transform. In the matching stage it worked in a crude manner. Driven by a simplified deformable iris model, the crude matching was refined. By means of such multi-matching system, the task of iris recognition was accomplished. This process can preserve the elastic deformation between an input iris image and a template and improve precision for iris recognition. The experimental results indicate the va- lidity of this method.
基金This researchwas supported byNationalKeyResearch andDevelopment Program sub-topics[2018YFF0213606-03(Mu Y.,Hu T.L.,Gong H.,Li S.J.and Sun Y.H.)http://www.most.gov.cn]Jilin Province Science and Technology Development Plan(focuses on research and development projects)[20200402006NC(Mu Y.,Hu T.L.,Gong H.and Li S.J.)http://kjt.jl.gov.cn]+1 种基金Science and Technology Support Project for Key Industries in Southern Xinjiang[2018DB001(Gong H.,and Li S.J.)http://kjj.xjbt.gov.cn]Key technology R&D project of Changchun Science and Technology Bureau of Jilin Province[21ZGN29(Mu Y.,Bao H.P.,Wang X.B.)http://kjj.changchun.gov.cn].
文摘Remote sensing image analysis is a basic and practical research hotspot in remote sensing science.Remote sensing images contain abundant ground object information and it can be used in urban planning,agricultural monitoring,ecological services,geological exploration and other aspects.In this paper,we propose a lightweight model combining vgg-16 and u-net network.By combining two convolutional neural networks,we classify scenes of remote sensing images.While ensuring the accuracy of the model,try to reduce the memory of themodel.According to the experimental results of this paper,we have improved the accuracy of the model to 98%.The memory size of the model is 3.4 MB.At the same time,The classification and convergence speed of the model are greatly improved.We simultaneously take the remote sensing scene image of 64×64 as input into the designed model.As the accuracy of the model is 97%,it is proved that the model designed in this paper is also suitable for remote sensing images with few target feature points and low accuracy.Therefore,the model has a good application prospect in the classification of remote sensing images with few target feature points and low pixels.
基金This work is supported by the National Natural Science Foundation of China(Grant No.61672279)Project of“Six Talents Peak”in Jiangsu(2012-WLW-023)Open Foundation of State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Nanjing Hydraulic Research Institute,China(2016491411).
文摘Simultaneous location and mapping(SLAM)plays the crucial role in VR/AR application,autonomous robotics navigation,UAV remote control,etc.The traditional SLAM is not good at handle the data acquired by camera with fast movement or severe jittering,and the efficiency need to be improved.The paper proposes an improved SLAM algorithm,which mainly improves the real-time performance of classical SLAM algorithm,applies KDtree for efficient organizing feature points,and accelerates the feature points correspondence building.Moreover,the background map reconstruction thread is optimized,the SLAM parallel computation ability is increased.The color images experiments demonstrate that the improved SLAM algorithm holds better realtime performance than the classical SLAM.
基金supported by the National Natural Science Foundation of China(No. 51975293)the Aeronautical Science Foundation of China (No. 2019ZD052010)。
文摘The visual inspection is an economical and effective method for welding. For measuring the feature sizes of grooves,a method based on line structured light is presented. Firstly,an adaptive algorithm to extract the subpixel centerline of structured light stripes is introduced to deal with the uneven width and grayscale distributions of laser stripes,which is based on the quadratic weighted grayscale centroid. By means of region-of-interest(ROI)division and image difference,an image preprocessing algorithm is developed for filtering noise and improving image quality. Furthermore,to acquire geometrical dimensions of various grooves and groove types precisely,the subpixel feature point extraction algorithm of grooves is designed. Finally, experimental results of feature size measuring show that the absolute error of measurement is 0.031—0.176 mm,and the relative error of measurement is 0.2%—3.6%.
基金This work was supported by Natural Science Foundation of Liaoning Province of China (No.20032100)Open Foundation of State Key Laboratory of Vision and Auditory Information Processing (Peking University) (No.0503)+2 种基金Natural Science Foundation of Dalian City of China (No.2006J23JH020)Open Foundation of Jiangsu Province Key Laboratory for Computer Information Processing Technology (Soocbow University)(No.KJS0602)Open Foundation of Key Laboratory of Image Processing and Image Communication (Nanjing University of Posts and Communications)(No.ZK205014).
文摘In this paper, a new content-based image watermarking scheme is proposed. The Harris-Laplace detector is adopted to extract feature points, which can survive a variety of attacks. The local characteristic regions （LCRs） are adaptively constructed based on scale-space theory. Then, the LCRs are mapped to geometrically invariant space by using image normalization technique. Finally, several copies of the digital watermark are embedded into the nonoverlapped LCRs by quantizing the magnitude vectors of discrete Fourier transform （DFT） coefficients. By binding a watermark with LCR, resilience against desynchronization attacks can be readily obtained. Simulation results show that the proposed scheme is invisible and robust against various attacks which includes common signals processing and desynchronization attacks.
基金National Natural Science Foundation of China(Grant Nos.52072072,52025121 and 51605087).
文摘Noise,vibration and harshness(NVH)problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles.Although high accuracy manufacturing,modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts;off-road condition,obstacles and extreme operating condition could still trigger NVH problems unexpectedly.This paper proposes a vehicular electronic image stabilization(EIS)system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles.Firstly,feature point detection and matching based on an oriented FAST and rotated BRIEF(ORB)algorithm are implemented to match images in the process of EIS.Furthermore,a novel improved random sampling consensus algorithm(i-RANSAC)is proposed to eliminate mismatched feature points and increase the matching accuracy significantly.And an adaptive Kalman filter(AKF)is applied to improve the adaptability of the vehicular EIS.Finally,an experimental platform based on a gasoline model car was established to validate its performance.The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.
基金supported by the National Natural Science Foundation of China under Grant Nos.61872316,62272406,61932018the National Key R&D Plan of China under Grant No.2020YFB1708900.
文摘Isogeometric analysis(IGA)is introduced to establish the direct link between computer-aided design and analysis.It is commonly implemented by Galerkin formulations(isogeometric Galerkin,IGA-G)through the use of nonuniform rational B-splines(NURBS)basis functions for geometric design and analysis.Another promising approach,isogeometric collocation(IGA-C),working directly with the strong form of the partial differential equation(PDE)over the physical domain defined by NURBS geometry,calculates the derivatives of the numerical solution at the chosen collocation points.In a typical IGA,the knot vector of the NURBS numerical solution is only determined by the physical domain.A new perspective on the IGAmethod is proposed in this study to improve the accuracy and convergence of the solution.Solving the PDE with IGA can be regarded as fitting the load function defined on the NURBS geometry(right-hand side)with derivatives of the NURBS numerical solution(left-hand side).Moreover,the design of the knot vector has a close relationship to theNURBS functions to be fitted in the area of data fitting in geometric design.Therefore,the detected feature points of the load function are integrated into the initial knot vector of the physical domainto construct thenewknot vector of thenumerical solution.Then,they are connected seamlessly with the IGA-C framework for its great potential combining the accuracy and smoothness merits with the computational efficiency,which we call isogeometric collocation by fitting load function(IGACL).In numerical experiments,we implement our method to solve 1D,2D,and 3D PDEs and demonstrate the improvement in accuracy by comparing it with the standard IGA-C method.We also verify the superiority in the accuracy of our knot selection scheme when employed in the IGA-G method,which we call isogeometric Galerkin by fitting load function(IGA-GL).
基金Supported by the Key Research Program of the Chinese Academy of Sciences(ZDRE-KT-2021-3)。
文摘Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms.
