Image Feature Points Research Articles

Geometric Parameter Self-Calibration Based on Projection Feature Matching for X-Ray Nanotomography

The mismatch of geometric parameters in a nanotomography system bears a significant impact on the reconstructed images. Moreover, projection image noise is increased due to limitations of the X-ray power source. The accuracy of the existing self-calibration method, which uses only the grayscale information of the projected image, is easily affected by noise and leads to reduced accuracy. This paper proposes a geometric parameter self-calibration method based on feature matching of mirror projection images. Firstly, the fast extraction and matching feature points in the mirror projection image are performed by speeded-up robust features (SURF). The feature triangle is then designed according to the stable position of the system’s rotation axis to further filter the feature points. In turn, the influence of the mismatched points on the calculation accuracy is reduced. Finally, the straight line where the rotation axis is located is fitted by the midpoint coordinates of the filtered feature points, thereby realizing geometric parameter calibration of the system. Simulation and actual data from the experimental results show that the proposed method effectively realizes the calibration of geometric parameters, and the blurring and ghosting caused by geometric artifacts are corrected. Compared with existing methods, the image clarity can be improved by up to 14.4%.

Tensor Affinity Learning for Hyperorder Graph Matching

Hypergraph matching has been attractive in the application of computer vision in recent years. The interference of external factors, such as squeezing, pulling, occlusion, and noise, results in the same target displaying different image characteristics under different influencing factors. After extracting the image feature point description, the traditional method directly measures the feature description using distance measurement methods such as Euclidean distance, cosine distance, and Manhattan distance, which lack a sufficient generalization ability and negatively impact the accuracy and effectiveness of matching. This paper proposes a metric-learning-based hypergraph matching (MLGM) approach that employs metric learning to express the similarity relationship between high-order image descriptors and learns a new metric function based on scene requirements and target characteristics. The experimental results show that our proposed method performs better than state-of-the-art algorithms on both synthetic and natural images.

An Aerial and Ground Multi-Agent Cooperative Location Framework in GNSS-Challenged Environments

In order to realize the cooperative localization of multi-unmanned platforms in the GNSS-denied environment, this paper proposes a collaborative SLAM (simultaneous localization and mapping, SLAM) framework based on image feature point matching. Without GNSS, a single unmanned platform UGV and UAV (unmanned ground vehicle, UGV; unmanned aerial vehicle, UAV) equipped with vision and IMU (inertial measurement unit, IMU) sensors can exchange information through data communication to jointly build a three-dimensional visual point map, and determine the relative position of each other through visual-based position re- identification and PnP (Perspective-n-Points, PnP) methods. When any agent can receive reliable GNSS signals, GNSS positioning information will greatly improve the positioning accuracy without changing the positioning algorithm framework. In order to achieve this function, we designed a set of two-stage position estimation algorithms. In the first stage, we used the modified ORB-SLAM3 algorithm for position estimation by fusing visual and IMU information. In the second stage, we integrated GNSS positioning and cooperative positioning information using the factor graph optimization (FGO) algorithm. Our framework consists of an UGV as the central server node and three UAVs carried by the UGV, that will collaborate on space exploration missions. Finally, we simulated the influence of different visibility and lighting conditions on the framework function on the virtual simulation experiment platform built based on ROS (robot operating system, ROS) and Unity3D. The accuracy of the cooperative localization algorithm and the single platform localization algorithm was evaluated. In the two cases of GNSS-denied and GNSS-challenged, the error of co-location reduced by 15.5% and 19.7%, respectively, compared with single-platform independent positioning.

An automatic extraction method on medical feature points based on PointNet++ for robot-assisted knee arthroplasty.

Image registration is a crucial technology in robot-assisted knee arthroplasty, which provides real-time patient information by registering the pre-operative image data with data acquired during the operation. The existing registration method requires surgeons to manually pick up medical feature points (i.e. anatomical points) in pre-operative images, which is time-consuming and relied on surgeons experience. Moreover, different doctors have different preferences in preoperative planning, which may influence the consistency of surgical results. A medical feature points automatic extraction method based on PointNet++ named Point_RegNet is proposed to improve the efficiency of preoperative preparation and ensure the consistency of surgical results. The proposed method replaces the classification and segmentation layer of PointNet++ with a regression layer to predict the position of feature points. The comparative experiment is adopted to determine the optimal set of abstraction layers in PointNet++. The proposed network with three set abstraction layers is more suitable for extracting feature points. The feature points predictions mean error of our method is less than 5mm, which is 1mm less than the manual marking method. Ultimately, our method only requires less than 3s to extract all medical feature points in practical application. It is much faster than the manual extraction way which usually requires more than half an hour to mark all necessary feature points. Our deep learning-based method can improve the surgery accuracy and reduce the preoperative preparation time. Moreover, this method can also be applied to other surgical navigation systems.

Joint Detection and Matching of Feature Points in Multimodal Images.

In this work, we propose a novel Convolutional Neural Network (CNN) architecture for the joint detection and matching of feature points in images acquired by different sensors using a single forward pass. The resulting feature detector is tightly coupled with the feature descriptor, in contrast to classical approaches (SIFT, etc.), where the detection phase precedes and differs from computing the descriptor. Our approach utilizes two CNN subnetworks, the first being a Siamese CNN and the second, consisting of dual non-weight-sharing CNNs. This allows simultaneous processing and fusion of the joint and disjoint cues in the multimodal image patches. The proposed approach is experimentally shown to outperform contemporary state-of-the-art schemes when applied to multiple datasets of multimodal images. It is also shown to provide repeatable feature points detections across multi-sensor images, outperforming state-of-the-art detectors. To the best of our knowledge, it is the first unified approach for the detection and matching of such images.

Underwater multi-frame target images mosaic method based on adaptive image enhancement

The severe attenuation and scattering of light in the water reduces the effective field of view of the underwater camera, making the scene information contained in a single image limited, and it is difficult to meet the application requirements of the large-scale underwater scenes. To solve this problem, an underwater multi-frame target images mosaic method based on adaptive image enhancement is proposed in this paper. Firstly, the image blur prior is used to achieve the adaptive enhancement of underwater images to suppress the blur and colour distortion of underwater images. Then, the feature point matching method based on the improved SURF realizes the extraction and matching of the feature points of underwater multi-frame target images. Finally, combining with the fusion strategy of gradual in and out, the seamless splicing and fusion of underwater multi-frame target images is realized. The pool and shallow sea tests were carried out respectively, and the results show that the method proposed in this paper increases the number of effective feature point matching and improves the splicing effect.

A Rapid Temporal Bone Localization Method Based on Machine Visual Detection Markers

The number of hearing-impaired people is increasing year by year; robotic cochlear drilling surgery is one of the safest methods to treat deafness. Looking at the issue of low efficiency of temporal bone posture positioning in cochlear implantation robotic drilling, a novel auxiliary ring marker temporal bone positioning method was proposed to improve temporal bone posture positioning efficiency, optimize the operation time, and reduce auxiliary injuries caused by the surgery. First, the temporal bone visual positioning assistant ring was designed based on the requirements for cochlear robotic drilling surgery. The target detection was conducted on the auxiliary ring and image processing and feature point extraction methods were designed. Then, the three-dimensional coordinates of the measured feature points were obtained by binocular vision, and the auxiliary ring and temporal bone postures were estimated. Finally, the auxiliary ring and temporal bone localization methods were validated. The experiment results indicated that the temporal bone was located quickly and effectively in a total time of about 33 ms, which was faster and more accurate than traditional visual localization methods and could satisfy real-time temporal bone localization during surgery. This study can reduce the time of temporal bone visual positioning in cochlear implant drilling operations, greatly improving the robot’s capabilities to extract visual information during the operation, which has a better auxiliary role for future research and applications of the cochlear implant drilling operation.

Real-time detection of crop rows in maize fields based on autonomous extraction of ROI

The current crop rows detection based on machine vision generally has the problems of low detection accuracy and poor real-time performance. Moreover, crop rows detection remains a challenging problem in complex field conditions, such as high weeds pressure, poor illumination conditions, and vegetation foliage shading. We propose a crop rows detection algorithm based on autonomous extraction of ROI (Region of interest). The prior method computes the feature points of the entire image and groups them into the crop rows which they belong to. Instead, we consider the core of crop rows detection problem to be the extraction of the travelling area of agricultural machinery in maize fields. A YOLO (You Only Look Once) neural network is employed to predict the travelling area of the agricultural machinery end-to-end. The prediction boxes are unified into ROI and the crop and soil background are segmented in the ROI by Excess Green operator and Otsu’s method. Then, the feature points of crops are extracted using FAST (Features from Accelerated Segment Test) corner point detection, and finally the detection lines of crop rows are fitted with least squares method. Because image recognition is limited to a valid region after the ROI is extracted, the processing speed of our algorithm is remarkably fast. It takes only about 25 ms to process a single image (640*360 pixels) and the frame rate of video stream exceeds 40FPS. Meanwhile, it can achieve high accuracy and robust extraction of ROI in various maize fields. The average error angle of the detection lines is 1.88°, which can meet the real-time and accuracy requirements of field navigation. The proposed algorithm can provide a new solution to the current machine vision-based navigation technology for agricultural machinery. Code is available at: https://github.com/WoodratTradeCo/crop-rows-detection.

Real-time and high precision feature matching between blur aerial images.

When aerial cameras get aerial remote sensing images, the defocus will occur because of reasons such as air pressure, temperature and ground elevation changes, resulting in different image sharpness of continual aerial remote sensing images. Nowadays, the rapidly developing feature matching algorithm will rapidly reduce the registration rate between images with different image sharpness. Therefore, in order to enable aerial cameras to get image sharpness parameters according to the locations of aerial image feature points with inconsistent sharpness, this paper proposes a feature matching algorithm between aerial images with different sharpness by using DEM data and multiple constraints. In this paper, the feature matching range is extended according to the modified aerial imaging model and the nonlinear soft margin support vector machine. Then the relative moving speed and its variation of the feature points in the image are obtained by using the extended L-k optical flow, and finally the epipolar geometric constraint is introduced. To locate the feature points is obtained under multiple constraints, there is no need to calculate the feature point descriptors, and some mismatched point pairs are corrected, which improves the matching efficiency and precision. The experimental results show the feature matching precision of this algorithm is more than 90%, and the running time and matching precision can meet various application needs of aerial cameras.

Planning and Design Method of Multiangle Ecological Building Edge Space under the Background of Rural Revitalization

Under the background of rural revitalization, in order to realize the planning and design of ecological building edge space, a multi-perspective ecological building edge space planning and design method based on remote sensing image edge segmentation is proposed. The remote sensing visual detection of ecological buildings is realized by fusing multiscale features and multisource scene remote sensing images, and the extracted remote sensing image feature points are calibrated to extract the location information, texture features, super-resolution edge information features, and different levels of change features of the spatial distribution of the edge of ecological buildings. The background difference detection model of an ecological building remote sensing image is established, and the distance of the centroid of the corresponding level is calculated through frame dynamic planning and differential image clustering. Combined with the edge contour detection method of ecological building remote sensing image, the edge space planning and design are realized. The simulation results show that this method has higher accuracy in planning and better accuracy in detecting the contour of ecological building edge space and improves the dynamic planning and positioning ability of multi-perspective ecological building edge space distribution.

3D Semantic VSLAM of Dynamic Environment Based on YOLACT

A Visual Simultaneous Localization and Mapping (VSLAM) method is proposed to construct a 3D environment map in the creation of dynamic noise information which leads to significant errors in camera pose estimation and a substantial number of noise points. For this problem, this paper proposed a method based on YOLACT, which combined optical flow and ViBe+ semantic map construction algorithm. First, our approach uses LK optical flow method to estimate the overall motion trajectory of adjacent frames. Then the trajectory data are employed to intercept the relative position of the current frame to the previous frame. Afterward, we combine ViBe+ algorithm to accurately detect and eliminate dynamic noise. Secondly, image semantic segmentation is performed based on YOLACT model. Image feature points are extracted from MAPLAB algorithm, pose estimation of the camera is performed and movement trajectory is recorded to complete a semantic map. Finally, through ablations study with common algorithms, the experimental results show that the proposed algorithm effectively avoids interference of dynamic noise information on VSLAM. Additionally, the constructed semantic map provides higher precision, fewer noise points, and pretty robustness.

Horticultural Image Feature Matching Algorithm Based on Improved ORB and LK Optical Flow

To solve the low accuracy of image feature matching in horticultural robot visual navigation, an innovative and effective image feature matching algorithm was proposed combining the improved Oriented FAST and Rotated BRIEF (ORB) and Lucas–Kanade (LK) optical flow algorithm. First, image feature points were extracted according to the adaptive threshold calculated using the Michelson contrast. Then, the extracted feature points were uniformed by the quadtree structure, which can reduce the calculated amount of feature matching, and the uniform ORB feature points were roughly matched to estimate the position of the feature points in the matched image using the improved LK optical flow. Finally, the Hamming distance between rough matching points was calculated for precise matching. Feature extraction and matching experiments were performed in four typical scenes: normal light, low light, high texture, and low texture. Compared with the traditional algorithm, the uniformity and accuracy of the feature points extracted by the proposed algorithm were enhanced by 0.22 and 50.47%, respectively. Meanwhile, the results revealed that the matching accuracy of the proposed algorithm increased by 14.59%, whereas the matching time and total time decreased by 39.18% and 44.79%, respectively. The proposed algorithm shows great potential for application in the visual simultaneous localization and mapping (V-SLAM) of horticultural robots to achieve higher accuracy of real-time positioning and map construction.

Feature Extraction of Color Symbol Elements in Interior Design Based on Extension Data Mining

In order to improve the indoor color symbol elements in the design of visual communication ability, the interior design color symbol element feature extraction process is needed. We need to put forward a kind of extension data mining based on the interior design color symbol element feature extraction methods, build the visualization visual expression and big data characteristics of the distribution of the interior design of texture color symbol elements in intelligent information collection model, indoor color symbol elements in the design of the texture image and character segmentation, and extraction of texture and color symbol element in interior design edge feature point, based on the texture and edge profile characteristics of the distribution in the design of indoor color symbol elements of texture segmentation and matching, and extract the edge of the color symbol elements in interior design outline feature points. The extracted edge contour feature points of color image in interior design are used as the extended data feature components for texture matching, and the color symbol element feature extraction is realized according to the prior data distribution and extension data mining results. Simulation results show that this method has good adaptive performance in texture matching and feature extraction of color symbol elements in interior design and has a strong texture discrimination ability, which improves the visual communication ability of color symbol elements in interior design.

(Retracted) Computer-aided three-dimensional animation design and enhancement based on spatial variation and convolution algorithm

Computer-aided technology is a system of personal confidential interaction between computer and users under the guidance of people. Due to the continuous development of computer technology, this technology has been used in many fields. Computer-aided design is the use of computer systems to assist designers in engineering or product design. It is a technology to achieve the best design effect. Three-dimensional (3D) animation is not limited by time and space. It uses various forms of expression to express complex and abstract program content, scientific principles, and abstract concepts in centralized and simplified forms. The creative feature of animation is that the author does not use the advanced 3D technology to present the real world to the audience. Here, we aim to study the 3D animation image design and image enhancement based on computer-aided technology, hoping to make the 3D animation more realistic and make the film and television picture clearer with the help of computer-aided technology. We expound the current situation of 3D graphics research. We analyze the current development of the 3D animation industry. We propose an extreme value constraint based on spatial variation and a convolution algorithm based on learning a ringing images. The experimental results show that after Retinex method processes the image, the matching feature points of the target image in the scale-invariant feature transformation algorithm are 55. The correct matching rate of feature points after homomorphic filtering is 0.85. The number of correct matching points of the image processed by Retinex method changes greatly, and the change of homomorphic filtering is small.

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks.

In order to solve the research on the extraction of ceramic sculpture artwork patterns, and, in the process of image reproduction, the problem of too few feature points in the object image, the author proposes an image stitching algorithm that combines SIFT and MSER algorithms. After comprehensively analyzing the principles, advantages, and disadvantages of the current main image stitching methods, in terms of feature matching, based on the K-D tree search algorithm, the improved BBF algorithm is used to improve the search efficiency of feature points. In order to remove the possible cracks in the stitching process, an improved multiband fusion algorithm is used to seamlessly stitch the registered images. The results show that the feature points detected by the one-dimensional normal distribution algorithm are on average 0.1%, 0.5%, 1.7%, 4.4%, and 9.2%. The algorithm combining SIFT and MSER to extract feature points can reach 3.6%, 4.6%, 8.4%, 15%, and 19.1%. The experimental results show that the algorithm proposed by the author can extract more image feature points to facilitate later image registration. The image blur phenomenon in the original image fusion algorithm is solved, and a complete and clear two-dimensional plane pattern is finally obtained.

Application of Deep Learning and Speech Recognition Technology for Pedestrian Face Recognition in health sectors

Aiming at the problems of traditional face recognition methods, this paper proposes the application of deep learning and speech recognition technology in pedestrian face recognition. Firstly, the pedestrian face image information is collected, and the face image is decomposed by wavelet scale. The improved detail enhanced face image is obtained, and Harris adaptive threshold corner detection is performed on the enhanced face image. The feature points of pedestrian face image is extracted and matched, and the local radial transformation of points and lines and the Epipolar constraint between multiple planes are adopted. Combined with the constraints of the angle and gray approximation measure of the line features of the face image, the line matching of the face close range image is completed. The 3D line features of the pedestrian face image are extracted and fitted by using the principle of face to face intersection. Combined with the pedestrian face image recognition algorithm, the pedestrian face recognition is realized. The experimental results show that the pedestrian face recognition method based on deep learning and speech recognition technology has better performance.

Recognition and Depth Estimation of Ships Based on Binocular Stereo Vision

To improve the navigation safety of inland river ships and enrich the methods of environmental perception, this paper studies the recognition and depth estimation of inland river ships based on binocular stereo vision (BSV). In the stage of ship recognition, considering the computational pressure brought by the huge network parameters of the classic YOLOv4 model, the MobileNetV1 network was proposed as the feature extraction module of the YOLOv4 model. The results indicate that the mAP value of the MobileNetV1-YOLOv4 model reaches 89.25%, the weight size of the backbone network was only 47.6 M, which greatly reduced the amount of computation while ensuring the recognition accuracy. In the stage of depth estimation, this paper proposes a feature point detection and matching algorithm based on the ORB algorithm at sub-pixel level, that is, firstly, the FSRCNN algorithm was used to perform super-resolution reconstruction of the original image, to further increase the density of image feature points and detection accuracy, which was more conducive to the calculation of the image parallax value. The ships’ depth estimation results indicate that when the distance to the target is about 300 m, the depth estimation error is less than 3%, which meets the depth estimation needs of inland ships. The ship target recognition and depth estimation technology based on BSV proposed in this paper makes up for the shortcomings of the existing environmental perception methods, improves the navigation safety of ships to a certain extent, and greatly promotes the development of intelligent ships in the future.

3D Simulation Landscape Design Based on Image Sensor

In order to solve the problem of heavy workload of landscape plant modeling, the lack of efficient auxiliary or automatic methods for establishing three-dimensional models for various landscape plants, and the general three-dimensional models of landscape plants which cannot reflect the natural growth of plants and the interaction between the environment, this paper proposes a method of three-dimensional simulation of landscape design based on image sensors. This method includes the construction of three-dimensional image simulation landscape feature analysis function and rationality judgment model, so as to provide theoretical support for landscape design. The experimental results show that the matching number and matching rate of landscape feature points obtained by the traditional deep evaluation method are lower than those obtained by the 3D image simulation method used in this paper, and the steps of image feature points matching are relatively simple. With the gradual expansion of the scope, the accuracy of the three-dimensional image simulation judgment method used in this paper is gradually improved, up to 89%, while the traditional method is always maintained at about 40%. Conclusion. The 3D simulation landscape design method based on image sensor has higher accuracy and wider application prospect.

3D Reconstruction Method based on Medical Image Feature Point Matching.

Medical 3D image reconstruction is an important image processing step in medical image analysis. How to speed up the speed while improving the accuracy in 3D reconstruction is an important issue. To solve this problem, this paper proposes a 3D reconstruction method based on image feature point matching. By improving SIFT, the initial matching of feature points is realized by using the neighborhood voting method, and then the initial matching points are optimized by the improved RANSAC algorithm, and a new SFM reconstruction method is obtained. The experimental results show that the feature matching rate of this algorithm on Fountain data is 95.42% and the matching speed is 4.751 s. It can be seen that this algorithm can shorten the reconstruction time and obtain sparse point clouds with more reasonable distribution and better reconstruction effect.

Vibration compensation for railway track displacement monitoring system using biomedical image processing concept

Vibration compensation in track displacement monitoring is explored by using rail image feature points. The key point is to compare the image of the rail to be tested with the image of the standard rail. A downstream section of Metro Line A is selected for real time analysis of image. The sampling frequency of the detection system is 0.5m/point, and the driving speed of the detection vehicle is about 40km/h, take the distance of 100m between K23+000-K22+900 of kilometer mark, total 200 points, and analyze the image data by grey scale conversion concept. In order to verify the accuracy of the vibration compensation data, one point is taken at an interval of 5m, and a total of 20 points are measured, and compare and analyze with dynamic real-time detection data. After vibration compensation, the geometric parameters of the contact rail are closer to the expected value, and the maximum error can be controlled within ±3mm. Using biomedical image-based processing concept to realize the track displacement monitoring method, which meets the requirements of non-contact measurement, and the degree of automation is high, it can be monitored and alarmed in real time, and it has a good application prospect.