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.

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.

Design of a coated thinly clad chalcogenide long-period fiber grating refractive index sensor based on dual-peak resonance near the phase matching turning point

A novel method for designing chalcogenide long-period fiber grating(LPFG) sensors based on the dual-peak resonance effect of the LPFG near the phase matching turning point(PMTP) is presented. Refractive index sensing in a high-refractive-index chalcogenide fiber is achieved with a coated thinly clad film. The dual-peak resonant characteristics near the PMTP and the refractive index sensing properties of the LPFG are analyzed first by the phase-matching condition of the LPFG. The effects of film parameters and cladding radius on the sensitivity of refractive index sensing are further discussed. The sensor is optimized by selecting the appropriate film parameters and cladding radius. Simulation results show that the ambient refractive index sensitivity of a dual-peak coated thinly clad chalcogenide LPFG at the PMTP can be 2400 nm/RIU, which is significantly higher than that of non-optimized gratings. It has great application potential in the field of chemical sensing and biosensors.

A Visual Indoor Localization Method Based on Efficient Image Retrieval

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.

POINT PATTERN MATCHING ALGORITHM BASED ON POINT PAIR TOPOLOGICAL CHARACTERISTICS AND SPECTRAL MATCHING

Most of the Point Pattern Matching (PPM) algorithm performs poorly when the noise of the point's position and outliers exist. This paper presents a novel and robust PPM algorithm which combined Point Pair Topological Characteristics (PPTC) and Spectral Matching (SM) together to solve the afore mentioned issues. In which PPTC, a new shape descriptor, is firstly proposed. A new comparability measurement based on PPTC is defined as the matching probability. Finally, the correct matching results are achieved by the spectral matching method. The synthetic data experiments show its robustness by comparing with the other state-of-art algorithms and the real world data experiments show its effectiveness.

Three-Dimensional Reconstruction of Welding Pool Surface by Binocular Vision

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.

A Review of Point Feature Based Medical Image Registration

Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms(PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However,to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of pointfeaturebased methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.

Fast Scene Reconstruction Based on Improved SLAM

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.

Surface matching method for profile inspection with touch probe

This paper presents an efficient method for rigid registration of 3-D point sets,which intends to match the feature points inspected using touch probe with the points on designed CAD surface.The alignment error is defined as the least square problem,and the sphere radius of the inspection probe is considered.In this framework,the matching problem is converted into acquiring six Euler variables problem by solving nonlinear equations.Thus,a matrix transformation of parameter separation is presented to get the approximate resolution.Finally,iterative cycles are provided to calculate the nearest points on designed surface corresponding to the inspection points.Experiment verification is realized for the presented surface matching method of robot inspection system.

A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence

In this paper,a novel compression framework based on 3D point cloud data is proposed for telepresence,which consists of two parts.One is implemented to remove the spatial redundancy,i.e.,a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box.The other part is applied to remove the temporal redundancy of the 3D point cloud data.The temporal redundancy between point clouds is removed by using the motion vector,i.e.,the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame.The hrst,the B-SHOT(binary signatures of histograms orientation)descriptor is applied to represent the point feature for matching the corresponding point between two frames.The second,the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame.The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames.Finally,the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the curren t and the motion vectors are transmit ted into the rem ote end.In order to reduce calculation time of the B-SHOT descriptor,we introduce an octree structure into the B-SHOT descriptor.In particular,in order to improve the robustness of the matching operation,we design the cluster feature to estimate the similarity bet ween two clusters.Experimen tai results have shown the bet ter performance of the proposed method due to the lower calculation time and the higher compression ratio.The proposed met hod achieves the compression ratio of 8.42 and the delay time of 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.