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.

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.

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.

Relaxation labeling for non-rigid point matching under neighbor preserving

Non-rigid point matching has received more and more attention.Recently,many works have been developed to discover global relationships in the point set which is treated as an instance of a joint distribution.However,the local relationship among neighboring points is more effective under non-rigid transformations.Thus,a new algorithm taking advantage of shape context and relaxation labeling technique,called SC-RL,is proposed for non-rigid point matching.It is a strategy that joints estimation for correspondences as well as the transformation.In this work,correspondence assignment is treated as a soft-assign process in which the matching probability is updated by relaxation labeling technique with a newly defined compatibility coefficient.The compatibility coefficient is one or zero depending on whether neighboring points preserving their relative position in a local coordinate system.The comparative analysis has been performed against four state-of-the-art algorithms including SC,ICP,TPS-RPM and RPM-LNS,and the results denote that SC-RL performs better in the presence of deformations,outliers and noise.

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.

AUTOMATED IMAGE MATCHING WITH CODED POINTS IN STEREOVISION MEASUREMENT

A coding-based method to solve the image matching problems in stereovision measurement is presented. The solution is to add and append an identity ID to the retro-reflect point, so it can be identified efficiently under the complicated circumstances and has the characteristics of rotation, zooming, and deformation independence. Its design architecture and implementation process in details based on the theory of stereovision measurement are described. The method is effective on reducing processing data time, improving accuracy of image matching and automation of measuring system through experiments.

A Fast Aerial Image Matching Method Using Airborne LiDAR Point Cloud and POS Data

A novel tie point matching algorithm of aerial images with the assistance of airborne LiDAR point clouds and POS data is proposed Firstly,the conjugate point searching strategy used in traditional correlation coefficient matching is improved and a fast algorithm is presented.Secondly,an automatic camera boresight misalignment calibration method based on virtual ground control points is proposed,and then the searching range of image matching is adaptively determined and applied to the image matching of the entire surveying area.Test results verified that the fast correlation coefficient matching algorithm proposed in this paper can reduce approximately 25% of the matching time without the loss of matching accuracy.The camera boresight misalignment calibration method can effectively increase the accuracy of exterior orientation elements of images calculated from POS data,and thus can significantly improve the predicted position of conjugate point for tie point matching.Our proposed image matching algorithm can achieve superior matching accuracy with multi-scale,multi-view,and cross-flight aerial images.

A KY FAN MATCHING THEOREM FOR TRANSFER COMPACTLY OPEN COVERS AND THE APPLICATION TO THE FIXED POINT

In this article, a Ky Fan matching theorem for transfer compactly open covers is established. As applications, a Fan-Browder coincidence theorem, a Ky Fan best approximation theorem and a Brouwer-Schauder-Rothe type fixed point theorem are obtained.

Modified SIFT descriptor and key-point matching for fast and robust image mosaic

To improve the performance of the scale invariant feature transform （ SIFT）, a modified SIFT （M-SIFT） descriptor is proposed to realize fast and robust key-point extraction and matching. In descriptor generation, 3 rotation-invariant concentric-ring grids around the key-point location are used instead of 16 square grids used in the original SIFT. Then, 10 orientations are accumulated for each grid, which results in a 30-dimension descriptor. In descriptor matching, rough rejection mismatches is proposed based on the difference of grey information between matching points. The per- formance of the proposed method is tested for image mosaic on simulated and real-worid images. Experimental results show that the M-SIFT descriptor inherits the SIFT＇ s ability of being invariant to image scale and rotation, illumination change and affine distortion. Besides the time cost of feature extraction is reduced by 50% compared with the original SIFT. And the rough rejection mismatches can reject at least 70% of mismatches. The results also demonstrate that the performance of the pro- posed M-SIFT method is superior to other improved SIFT methods in speed and robustness.

GRADIENT OF REFERENCE DIFFERENCE BASED MATCHING ALGORITHM FOR IMAGE FEATURE POINT

During matching on feature point, gray correlation matching technology is utilized to extract multi-peaks as a coarse matching set. A pair of given corresponding reference points within the left and right images is used to calculate gradients of reference difference between the reference points and each feature point within the multi-peaks set. The unique correspondence is determined by criterion of minimal gradients of reference difference. The obtained correspondence is taken as a new pair of reference points to update the reference points continuously until all feature points in the left (or right) image being matched with the right (or left) image. The gradients of reference difference can be calculated easily by means of pre-setting a pair of obvious feature points in the left and right images as a pair of corresponding reference points. Besides, the efficiency of matching can be improved greatly by taking the obtained matching point as a new pair of reference points, and by updating the reference point continuously. It is proved that the proposed algorithm is valid and reliable by 3D reconstruction on two pairs of actual natural images with abundant and weak texture, respectively.

Stereo Matching of Planar Curves Composed of Time Stamped Points

Matching features such as curve segments in stereo images play a very important role in scene recomtruction. In this paper, a stereo matching algorithm for the trajectories composed of time stamped points is proposed. Based on time stamped points, planar curve match measurements are given first, such as time constraint, cross-ratio invariant constraint and eplpolar geometry constraint; then, a trajectory matching method is proposed based on epipolar geometry constraint and cross-ratio invariant constraint. In order to match the planar curve segments projected by perspective projection system, the curve start time and end time are selected first to prepare match candidates. Then, the epipolar equation is used to discard the unmatched curve segment candidates. At last, a cross ratio invariant constxaint is used to find the most matched curve segments. If their match measurement is higher than the specialized threshold, a candidate with the least cross ratio difference is then selected as the match result; otherwise, no match is found. Unlike the conventional planar curve segments matching algorithm, this paper presents a weakly calibrated binocular stereo vision system which is based on wide baseline. The stamped points are obtained by targets detecting method of flying objects from image sequences. Due to wide baseline, there must exist the projection not in epipolar monotonic order or the curve segments located in very short distance and keeping the epipolar monotonic order. By using the method mentioned above, experiments are made to match planar curve segments not only in epipolar monotonic order but also not in epipolar monotonic order. The results show that the performance of our curve matching algorithm is effective for matching the arc-like planar trajectories composed of time stamped points.

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.

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.

Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming

An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points （GCPs） is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.

基于改进YOLOv8和GMM图像点集匹配的双目测距方法

针对无人塔吊系统的研究需求,提出一种基于改进YOLOv8和GMM图像点集匹配的双目测距方法,对驾驶室外环境中的塔吊吊钩进行检测识别并测距。通过双目摄像头进行图像采集,引入FasterNet骨干网络和Slim-neck颈部连接层,对YOLOv8目标检测算法进行改进,有效检测画面中的塔吊吊钩并获取检测框的二维坐标信息;采用局部敏感哈希方法,并融合分阶段匹配策略,提升GMM图像点集匹配模型的匹配效率,针对检测框中的塔吊吊钩,进行特征点匹配;最后通过双目相机三角测量原理计算得出塔吊吊钩的深度信息。实验结果表明,改进后的YOLOv8算法与原算法相比,精确率P提高了2.9%,平均精度AP50提高了2.2%,模型复杂度降低了10.01 GFLops,参数量减少了3.37 M,在提升检测精度的同时实现了模型的轻量化。改进后的图像点集匹配算法与原算法相比,各个指标表现出更加良好的鲁棒性。最后在工程现场对塔吊吊钩进行识别与测距,误差可接受范围内有效完成了塔吊吊钩的检测识别与测距任务,验证了本方法的可行性。

基于AIGC的用户个性化需求服务匹配模型研究

目的为解决网购推送内容相关度较低、信息滞后而造成的用户疲劳等问题,优化推送系统的个性化推荐能力,对AIGC推荐算法下的交互模式进行可行性探索。方法基于扎根理论获取用户关联属性并对其排序,进而利用AIGC大模型分别对用户需求信息和产品特征图文进行处理,最终建立基于AIGC的“用户-产品点对点匹配”(User-Product Point-To-Point Matching)推送模型。结果以水杯网购过程为例,通过对比实验进行验证。实验结果表明,该模式在用户选择信息层和深度搜索等方面可提升用户满意度。结论将AIGC引入推送服务行业,通过“预训练语言模型+微调”的模式实现推荐,能够进一步完善“用户-产品”的个性化推送服务,在电子商务、社交媒体、在线教育服务等领域具有重要意义。

基于深度学习的图像匹配:方法、应用与挑战

图像匹配旨在建立图像之间的点对应关系,是许多计算机视觉任务的关键环节.近年来,随着深度学习技术的发展,图像匹配方法已从以手工设计特征为主转变为基于深度网络的方法,基于深度学习的图像匹配方法在多个标准数据集上展现出卓越的性能,推动着多个相关应用的发展围绕图像匹配涉及的若干关键问题,如:特征点检测、特征点描述、稠密点匹配、误匹配去除,本文对深度学习图像匹配方法进行了系统性总结.首先分析了领域内基于深度学习的典型方法和关键技术,随后介绍了与图像匹配密切相关的几个典型应用并给出其现状分析,最后,根据对图像匹配领域技术发展的分析总结,结合作者在该领域的长期研究积累,本文给出了目前图像匹配所面临的主要挑战以及未来发展趋势。