Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching ...Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching is widely used in target recognition and tracking,indoor positioning and navigation.Local features missing,however,often occurs in color images taken in dark light,making the extracted feature points greatly reduced in number,so as to affect image matching and even fail the target recognition.An unsharp masking(USM)based denoising model is established and a local adaptive enhancement algorithm is proposed to achieve feature point compensation by strengthening local features of the dark image in order to increase amount of image information effectively.Fast library for approximate nearest neighbors(FLANN)and random sample consensus(RANSAC)are image matching algorithms.Experimental results show that the number of effective feature points obtained by the proposed algorithm from images in dark light environment is increased,and the accuracy of image matching can be improved obviously.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
A novel and effective self-calibration approach for robot vision is presented, which can effectively estimate both the camera intrinsic parameters and the hand-eye transformation at the same time. The proposed calibra...A novel and effective self-calibration approach for robot vision is presented, which can effectively estimate both the camera intrinsic parameters and the hand-eye transformation at the same time. The proposed calibration procedure is based on two arbitrary feature points of the environment, and three pure translational motions and two rotational motions of robot endeffector are needed. New linear solution equations are deduced,and the calibration parameters are finally solved accurately and effectively. The proposed algorithm has been verified by simulated data with different noise and disturbance. Because of the need of fewer feature points and robot motions, the proposed method greatly improves the efficiency and practicality of the calibration procedure.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
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
The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion,which leads to a false matching,with an...The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion,which leads to a false matching,with an inaccurate pose estimation or failed tracking.To address the challenge above,a new method of feature point matching is proposed in this paper,which combines the variable velocity model with the reverse optical flow method.First,the constant velocity model is extended to a new variable velocity model,and the expanded variable velocity model is used to provide the initial pixel shifting for the reverse optical flow method.Then the search range of feature points is accurately determined according to the results of the reverse optical flow method,thereby improving the accuracy and reliability of feature matching,with strengthened interframe tracking effects.Finally,we tested on TUM data set based on the RGB-D camera.Experimental results show that this method can reduce the probability of tracking failure and improve localization accuracy on SLAM(Simultaneous Localization and Mapping)systems.Compared with the traditional ORB-SLAM2,the test error of this method on each sequence in the TUM data set is significantly reduced,and the root mean square error is only 63.8%of the original system under the optimal condition.展开更多
The point pair feature(PPF)is widely used for 6D pose estimation.In this paper,we propose an efficient 6D pose estimation method based on the PPF framework.We introduce a well-targeted down-sampling strategy that focu...The point pair feature(PPF)is widely used for 6D pose estimation.In this paper,we propose an efficient 6D pose estimation method based on the PPF framework.We introduce a well-targeted down-sampling strategy that focuses on edge areas for efficient feature extraction for complex geometry.A pose hypothesis validation approach is proposed to resolve ambiguity due to symmetry by calculating the edge matching degree.We perform evaluations on two challenging datasets and one real-world collected dataset,demonstrating the superiority of our method for pose estimation for geometrically complex,occluded,symmetrical objects.We further validate our method by applying it to simulated punctures.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
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
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
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
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
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
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
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
Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median...Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.展开更多
Over the last 20 years road pavement imaging has become a routine output from annual pavement assessment survey regimes across the world. Hitherto the traditional use of road pavement images in road condition assessme...Over the last 20 years road pavement imaging has become a routine output from annual pavement assessment survey regimes across the world. Hitherto the traditional use of road pavement images in road condition assessment has been crack detection, rather than direct analysis of image features such as aggregate loss, changes in surface texture or deterioration of road markings. Any attempt to assess pavement condition change from features in a sequence of such images captured months or years apart requires image registration. A method for registering road pavement images is presented that makes use of an affine transformation based on pseudo-features within images. An affine trans- formation is considered suitable for registering road pavement images because of the linear way in which pavements are surveyed. Pseudo feature points are found using a modified corner detector, and then matching points between reference and template im- ages established via a correlation analysis of pavement image texture. With 4 such points it is possible to establish an affine transformation between the images. The method is tested on pavement images captured on three UK sites between winter 2014/15 and 2015/16. The method successfully registered 98% of images captured on sites typical of the UK's strategic road network, and 65% of images captured on a site typical of the UK's minor road network.展开更多
A model of autonomous positioning through associating environment memory information is presented for unmanned combat aerial vehicle(UCAV).The representation strategy of environment by constructing place cells is used...A model of autonomous positioning through associating environment memory information is presented for unmanned combat aerial vehicle(UCAV).The representation strategy of environment by constructing place cells is used to produce the memory information,and the landmarks in memory are retrieved through perceiving and processing the environment.During UCAV′s flight,the landmarks are obtained in real-time and are matched with the landmarks in memory.Then,the idea of ranging positioning is adopted to calculate UCAV′s location based on the corresponding relationship between current obtained landmarks and the memorized landmarks.Simulation shows that the proposed model can realize autonomous positioning in the memorized environment,and the positioning performance is well when the sensor has a high precision.展开更多
An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utiliz...An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the leastsquares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.展开更多
基金Supported by the National Natural Science Foundation of China(No.61771186)the Heilongjiang Provincial Natural Science Foundation of China(No.YQ2020F012)the University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province(No.UNPYSCT-2017125).
文摘Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching is widely used in target recognition and tracking,indoor positioning and navigation.Local features missing,however,often occurs in color images taken in dark light,making the extracted feature points greatly reduced in number,so as to affect image matching and even fail the target recognition.An unsharp masking(USM)based denoising model is established and a local adaptive enhancement algorithm is proposed to achieve feature point compensation by strengthening local features of the dark image in order to increase amount of image information effectively.Fast library for approximate nearest neighbors(FLANN)and random sample consensus(RANSAC)are image matching algorithms.Experimental results show that the number of effective feature points obtained by the proposed algorithm from images in dark light environment is increased,and the accuracy of image matching can be improved obviously.
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(61379097,61401463,61100098)Youth Innovation Promotion Association CAS
文摘A novel and effective self-calibration approach for robot vision is presented, which can effectively estimate both the camera intrinsic parameters and the hand-eye transformation at the same time. The proposed calibration procedure is based on two arbitrary feature points of the environment, and three pure translational motions and two rotational motions of robot endeffector are needed. New linear solution equations are deduced,and the calibration parameters are finally solved accurately and effectively. The proposed algorithm has been verified by simulated data with different noise and disturbance. Because of the need of fewer feature points and robot motions, the proposed method greatly improves the efficiency and practicality of the calibration procedure.
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>
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.
基金This work was supported by The National Natural Science Foundation of China under Grant No.61304205 and NO.61502240The Natural Science Foundation of Jiangsu Province under Grant No.BK20191401 and No.BK20201136Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.SJCX21_0364 and No.SJCX21_0363.
文摘The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion,which leads to a false matching,with an inaccurate pose estimation or failed tracking.To address the challenge above,a new method of feature point matching is proposed in this paper,which combines the variable velocity model with the reverse optical flow method.First,the constant velocity model is extended to a new variable velocity model,and the expanded variable velocity model is used to provide the initial pixel shifting for the reverse optical flow method.Then the search range of feature points is accurately determined according to the results of the reverse optical flow method,thereby improving the accuracy and reliability of feature matching,with strengthened interframe tracking effects.Finally,we tested on TUM data set based on the RGB-D camera.Experimental results show that this method can reduce the probability of tracking failure and improve localization accuracy on SLAM(Simultaneous Localization and Mapping)systems.Compared with the traditional ORB-SLAM2,the test error of this method on each sequence in the TUM data set is significantly reduced,and the root mean square error is only 63.8%of the original system under the optimal condition.
基金This work was supported in part by the National Key R&D Program of China(2018AAA0102200)National Natural Science Foundation of China(62132021,62102435,61902419,62002375,62002376)+2 种基金Natural Science Foundation of Hunan Province of China(2021JJ40696)Huxiang Youth Talent Support Program(2021RC3071)NUDT Research Grants(ZK19-30,ZK22-52).
文摘The point pair feature(PPF)is widely used for 6D pose estimation.In this paper,we propose an efficient 6D pose estimation method based on the PPF framework.We introduce a well-targeted down-sampling strategy that focuses on edge areas for efficient feature extraction for complex geometry.A pose hypothesis validation approach is proposed to resolve ambiguity due to symmetry by calculating the edge matching degree.We perform evaluations on two challenging datasets and one real-world collected dataset,demonstrating the superiority of our method for pose estimation for geometrically complex,occluded,symmetrical objects.We further validate our method by applying it to simulated punctures.
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.
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 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.
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 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The work was supported by National Natural Science Foundation of China (No. 50975195).
文摘Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.
文摘Over the last 20 years road pavement imaging has become a routine output from annual pavement assessment survey regimes across the world. Hitherto the traditional use of road pavement images in road condition assessment has been crack detection, rather than direct analysis of image features such as aggregate loss, changes in surface texture or deterioration of road markings. Any attempt to assess pavement condition change from features in a sequence of such images captured months or years apart requires image registration. A method for registering road pavement images is presented that makes use of an affine transformation based on pseudo-features within images. An affine trans- formation is considered suitable for registering road pavement images because of the linear way in which pavements are surveyed. Pseudo feature points are found using a modified corner detector, and then matching points between reference and template im- ages established via a correlation analysis of pavement image texture. With 4 such points it is possible to establish an affine transformation between the images. The method is tested on pavement images captured on three UK sites between winter 2014/15 and 2015/16. The method successfully registered 98% of images captured on sites typical of the UK's strategic road network, and 65% of images captured on a site typical of the UK's minor road network.
基金supported by the National Natural Science Foundation of China(No.61273048)
文摘A model of autonomous positioning through associating environment memory information is presented for unmanned combat aerial vehicle(UCAV).The representation strategy of environment by constructing place cells is used to produce the memory information,and the landmarks in memory are retrieved through perceiving and processing the environment.During UCAV′s flight,the landmarks are obtained in real-time and are matched with the landmarks in memory.Then,the idea of ranging positioning is adopted to calculate UCAV′s location based on the corresponding relationship between current obtained landmarks and the memorized landmarks.Simulation shows that the proposed model can realize autonomous positioning in the memorized environment,and the positioning performance is well when the sensor has a high precision.
文摘An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the leastsquares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.