Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

CMMCAN:Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention

In minimally invasive surgery,endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities.However,in clinical operating environments,endoscopic images often suffer from challenges such as low texture,uneven illumination,and non-rigid structures,which affect feature observation and extraction.This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images,leading to treatment and postoperative recovery issues for patients.To address these challenges,this paper introduces,for the first time,a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion(ASFF)module based on the lightweight architecture of EfficientViT.Additionally,a novel lightweight feature extraction and matching network based on attention mechanism is proposed.This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network.It extracts static and dynamic information features ranging from low-level to high-level,and from local to global,ensuring robust feature extraction across different widths,noise levels,and blur scenarios.Global and local matching are performed through a multi-level cascaded attention mechanism,with cross-channel attention introduced to simultaneously extract low-level and high-level features.Extensive ablation experiments and comparative studies are conducted on the HyperKvasir,EAD,M2caiSeg,CVC-ClinicDB,and UCL synthetic datasets.Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4%in accuracy(Acc),while also enhancing runtime performance and storage efficiency.When compared with the complex DenseDescriptor feature extraction network,the difference in Acc is less than 7.22%,and IoU calculation results on specific datasets outperform complex dense models.Furthermore,this method increases the F1 score by 33.2%and accelerates runtime by 70.2%.It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models,with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness.

Soft Tissue Feature Tracking Based on Deep Matching Network

Research in the field ofmedical image is an important part of themedical robot to operate human organs.Amedical robot is the intersection ofmulti-disciplinary research fields,in whichmedical image is an important direction and has achieved fruitful results.In this paper,amethodof soft tissue surface feature tracking basedonadepthmatching network is proposed.This method is described based on the triangular matching algorithm.First,we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm.The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set.After the training,the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame.From this matrix,the inter-frame feature matching results can be obtained.In this way,the inter-frame speckle tracking is completed.On this basis,the results of this method are compared with the matching results based on the convolutional neural network.The experimental results show that the proposed method has higher matching accuracy.In particular,the accuracy of the MNIST handwritten data set has reached more than 90%.

Image Feature Extraction and Matching of Augmented Solar Images in Space Weather

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.

FeatureMatching Combining Variable Velocity Model with Reverse Optical Flow

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.

Oncological features and prognosis of colorectal cancer in human immunodeficiency virus-positive patients: A retrospective study

BACKGROUND Due to the prolonged life expectancy and increased risk of colorectal cancer(CRC)among patients with human immunodeficiency virus(HIV)infection,the prognosis and pathological features of CRC in HIV-positive patients require examination.AIM To compare the differences in oncological features,surgical safety,and prognosis between patients with and without HIV infection who have CRC at the same tumor stage and site.METHODS In this retrospective study,we collected data from HIV-positive and-negative patients who underwent radical resection for CRC.Using random stratified sampling,24 HIV-positive and 363 HIV-negative patients with colorectal adenocarcinoma after radical resection were selected.Using propensity score matching,we selected 72 patients,matched 1:2(HIV-positive:negative=24:48).Differences in basic characteristics,HIV acquisition,perioperative serological indicators,surgical safety,oncological features,and long-term prognosis were compared between the two groups.RESULTS Fewer patients with HIV infection underwent chemotherapy compared to patients without.HIV-positive patients had fewer preoperative and postoperative leukocytes,fewer preoperative lymphocytes,lower carcinoembryonic antigen levels,more intraoperative blood loss,more metastatic lymph nodes,higher node stage,higher tumor node metastasis stage,shorter overall survival,and shorter progression-free survival compared to patients who were HIV-negative.CONCLUSION Compared with CRC patients who are HIV-negative,patients with HIV infection have more metastatic lymph nodes and worse long-term survival after surgery.Standard treatment options for HIV-positive patients with CRC should be explored.

Ground target localization of unmanned aerial vehicle based on scene matching

In order to improve target localization precision,accuracy,execution efficiency,and application range of the unmanned aerial vehicle(UAV)based on scene matching,a ground target localization method for unmanned aerial vehicle based on scene matching(GTLUAVSM)is proposed.The sugges-ted approach entails completing scene matching through a feature matching algorithm.Then,multi-sensor registration is optimized by robust estimation based on homologous registration.Finally,basemap generation and model solution are utilized to improve basemap correspondence and accom-plish aerial image positioning.Theoretical evidence and experimental verification demonstrate that GTLUAVSM can improve localization accuracy,speed,and precision while minimizing reliance on task equipment.

Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

A Sequence Image Matching Method Based on Improved High-Dimensional Combined Features

Image matching technology is theoretically significant and practically promising in the field of autonomous navigation.Addressing shortcomings of existing image matching navigation technologies,the concept of high-dimensional combined feature is presented based on sequence image matching navigation.To balance between the distribution of high-dimensional combined features and the shortcomings of the only use of geometric relations,we propose a method based on Delaunay triangulation to improve the feature,and add the regional characteristics of the features together with their geometric characteristics.Finally,k-nearest neighbor(KNN)algorithm is adopted to optimize searching process.Simulation results show that the matching can be realized at the rotation angle of-8°to 8°and the scale factor of 0.9 to 1.1,and when the image size is 160 pixel×160 pixel,the matching time is less than 0.5 s.Therefore,the proposed algorithm can substantially reduce computational complexity,improve the matching speed,and exhibit robustness to the rotation and scale changes.

A fast, accurate and dense feature matching algorithm for aerial images

Three-dimensional(3D)reconstruction based on aerial images has broad prospects,and feature matching is an important step of it.However,for high-resolution aerial images,there are usually problems such as long time,mismatching and sparse feature pairs using traditional algorithms.Therefore,an algorithm is proposed to realize fast,accurate and dense feature matching.The algorithm consists of four steps.Firstly,we achieve a balance between the feature matching time and the number of matching pairs by appropriately reducing the image resolution.Secondly,to realize further screening of the mismatches,a feature screening algorithm based on similarity judgment or local optimization is proposed.Thirdly,to make the algorithm more widely applicable,we combine the results of different algorithms to get dense results.Finally,all matching feature pairs in the low-resolution images are restored to the original images.Comparisons between the original algorithms and our algorithm show that the proposed algorithm can effectively reduce the matching time,screen out the mismatches,and improve the number of matches.

Speeded-Up Robust Feature Matching Algorithm Based on Image Improvement Technology

Due to requirements and necessities in digital image research, image matching is considered as a key, essential and complicating point especially for machine learning. According to its convenience and facility, the most applied algorithm for image feature point extraction and matching is Speeded-Up Robust Feature (SURF). The enhancement for scale invariant feature transform (SIFT) algorithm promotes the effectiveness of the algorithm as well as facilitates the possibility, while the application of the algorithm is being applied in a present time computer vision system. In this research work, the aim of SURF algorithm is to extract image features, and we have incorporated RANSAC algorithm to filter matching points. The images were juxtaposed and asserted experiments utilizing pertinent image improvement methods. The idea based on merging improvement technology through SURF algorithm is put forward to get better quality of feature points matching the efficiency and appropriate image improvement methods are adopted for different feature images which are compared and verified by experiments. Some results have been explained there which are the effects of lighting on the underexposed and overexposed images.

Image Relaxation Matching Based on Feature Points for DSM Generation

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.

A Fast Image Matching Algorithm Using a Combination of Line Segment Features

The scene matching navigation is a research focus in the field of autonomous navigation,but the real-time performance of image matching algorithm is difficult to meet the needs of real navigation systems.Therefore,this paper proposes a fast image matching algorithm.The algorithm improves the traditional line segment extraction algorithm and combines with the Delaunay triangulation method.By combining the geometric features of points and lines,the image feature redundancy is reduced.Then,the error with confidence criterion is analyzed and the matching process is completed.The simulation results show that the proposed algorithm can still work within 3°rotation and small scale variation.In addition,the matching time is less than 0.5 s when the image size is 256 pixel×256 pixel.The proposed algorithm is suitable for autonomous navigation systems with multiple feature distribution and higher real-time requirements.

A Multisource Contour Matching Method Considering the Similarity of Geometric Features

The existing multi-source contour matching studies have focused on the matching methods with consideration of topological relations and similarity measurement based on spatial Euclidean distance,while it is lack of taking the contour geometric features into account,which may lead to mismatching in map boundaries and areas with intensive contours or extreme terrain changes.In light of this,it is put forward that a matching strategy from coarse to precious based on the contour geometric features.The proposed matching strategy can be described as follows.Firstly,the point sequence is converted to feature sequence according to a feature descriptive function based on curvature and angle of normal vector.Then the level of similarity among multi-source contours is calculated by using the longest common subsequence solution.Accordingly,the identical contours could be matched based on the above calculated results.In the experiment for the proposed method,the reliability and efficiency of the matching method are verified using simulative datasets and real datasets respectively.It has been proved that the proposed contour matching strategy has a high matching precision and good applicability.

Fast feature matching based on r-nearest k-means searching

Feature matching has been frequently applied in computer vision and pattern recognition.In this paper,the authors propose a fast feature matching algorithm for vector-based feature.Their algorithm searches r-nearest neighborhood clusters for the query point after a k-means clustering,which shows higher efficiency in three aspects.First,it does not reformat the data into a complex tree,so it shortens the construction time twice.Second,their algorithm adopts the r-nearest searching strategy to increase the probability to contain the exact nearest neighbor(NN)and take this NN as the global one,which can accelerate the searching speed by 170 times.Third,they set up a matching rule with a variant distance threshold to eliminate wrong matches.Their algorithm has been tested on large SIFT databases with different scales and compared with two widely applied algorithms,priority search km-tree and random kd-tree.The results show that their algorithm outperforms both algorithms in terms of speed up over linear search,and consumes less time than km-tree.Finally,they carry out the CFI test based on ISKLRS database using their algorithm.The test results show that their algorithm can greatly improve the recognition speed without affecting the recognition rate.

Feature extension and matching for mobile robot global localization

This paper introduces an indoor global localization method by extending and matching features. In the proposed method, the environment is partitioned into convex subdivisions. Local extended maps of the subdivisions are then built by exten- ding features to constitute the local extended map set. While the robot is moving in the environment, the local extended map of the current local environment is established and then matched with the local extended map set. Therefore, global localization in an indoor environment can be achieved by integrating the position and ori- entation matching rates. Both theoretical analysis and comparison experimental result are provided to verify the effectiveness of the proposed method for global localization.

Method of weld recognition based on textural feature matching

In this paper an automatic visual method of seam recognizing and seam tracking based on textural feature matching was proposed, in order to recognize the weld of multi-layer or multi-pass welding in which the weld is difficult to be recognized by conventional visual methods. This method focuses on the obvious difference of image textural feature between the weld region and the base metal region, as well as the similarity of the textural features along the welding direction. The method consists of the following steps ： setting image template and choosing the edge region as ROI （ region of interest ）, extracting the image textural feature of the template and the edge region, feature matching, and recognition of weld region. Experiment showed that the method proposed was effective for weld seam recognition in multi-layer welding.

Trifocal Tensor Based Feature Matching Algorithm

Feature matching is of significance in the field of computer vision.In this paper,a trifocal tensor based feature matching algorithm is proposed for three views,including a trinocular vision system.Initial matching point-pairs can be determined according to generic matching algorithms,on which an initial trifocal tensor of three views can be confirmed.Then the initial matching point-pairs should be re-selected.Meanwhile,the trifocal tensor will be recomputed.Iteratively,the optimized trifocal tensor can be obtained.Compatible fundamental matrix of every two views can be determined.Furthermore,in the trinocular vision sensor,the trifocal tensor can be calculated based on the intrinsic parameter matrix of each camera.With the strict constraint provided by the trifocal tensor,feature matching results will be optimized.Experiments show that our proposed algorithm has the characteristics of feasibility and precision.

A Template Matching Based Feature Extraction for Activity Recognition

Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of prominent features.Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition performance.In this paper,we propose a robust feature extraction method for HAR systems based on template matching.Essentially,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette.In this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame.This process is replicated for the whole frame,and the pixel is directed to the optimum match.The best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the frame.In this way,the feature vector is generated.After feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming activity.We utilized different publicly available standard datasets for experiments.The proposed method achieved the best accuracy against existing state-of-the-art systems.

A calculation method for low dynamic vehicle velocity based on fusion of optical flow and feature point matching

Aming at the problem of the low accuracy of low dynamic vehicle velocity under the environment of uneven distribution of light intensity,an improved adaptive Kalman filter method for the velocity error estimate by the fusion of optical flow tracking and scale mvaiant feature transform(SIFT)is proposed.The algorithm introduces anonlinear fuzzy membership function and the filter residual for the noise covariance matrix in the adaptive adjustment process.In the process of calculating the velocity of the vehicle,the tracking and matching of the inter-frame displacement a d the vehicle velocity calculation a e carried out by using the optical fow tracing and the SIF'T methods,respectively.Meanwhile,the velocity difference between theoutputs of thesetwo methods is used as the observation of the improved adaptive Kalman filter.Finally,the velocity calculated by the optical fow method is corrected by using the velocity error estimate of the output of the modified adaptive Kalman filter.The results of semi-physical experiments show that the maximum velocityeror of the fusion algorithm is decreased by29%than that of the optical fow method,and the computation time is reduced by80%compared with the SIFT method.