Existing Matching Methods Research Articles

Three-Dimensional Measurement Method for High Dynamic Range Surfaces Based on Adaptive Fringe Projection

As an optical measurement method, fringe projection three-dimensional (3D) measurement technology will have poor measurement effect due to overexposure or too dark of the fringes when measuring high dynamic range (HDR) surfaces. The adaptive fringe projection method is one of the effective methods to solve the HDR surfaces measurement, but pixel matching is the difficulty. The existing matching methods generally use phase for matching, which requires at least 24 additional patterns to be projected, seriously affecting the measurement efficiency. In order to solve these problems, an adaptive fringe projection measurement method using speckle pattern for pixel matching is proposed in this paper. First, two grayscale patterns are projected to calculate the adaptive projection intensity. Then a speckle pattern is projected to match the pixels of the captured pattern and the projected pattern. Finally, the adaptive fringe patterns are generated. Better than the methods before, only three additional patterns are needed to be projected to realize the measurement of the HDR surfaces. The experimental results show that the proposed method can reduce the number of projected patterns by more than 59.5% while achieving similar measurement accuracy, which greatly improves the measurement efficiency.

Deep learning method for Chinese multisource point of interest matching

Multisource point of interest (POI) matching refers to the pairing of POIs that refer to the same geographic entity in different data sources. This also constitutes the core issue in geospatial data fusion and update. The existing methods cannot effectively capture the complex semantic information from a text, and the manually defined rules largely affect matching results. This study developed a multisource POI matching method based on deep learning that transforms the POI pair matching problem into a binary classification problem. First, we used three different Chinese word segmentation methods to segment the POI text attributes and used the segmentation results to train the Word2Vec model to generate the corresponding word vector representation. Then, we used the text convolutional neural network (Text-CNN) and multilayer perceptron (MLP) to extract the POI attributes' features and generate the corresponding feature vector representation. Finally, we used the enhanced sequential inference model (ESIM) to perform local inference and inference combination on each attribute to realize the classification of POI pairs. We used the POI dataset containing Baidu Map, Tencent Map, and Gaode Map from Chengdu to train, verify, and test the model. The experimental results show that the matching precision, recall rate, and F1 score of the proposed method exceed 98% on the test set, and it is significantly better than the existing matching methods.

Rigid Shape Matching for 3-D Robotic Grinding Measurement With Applications to Blades

Robotic grinding is a promising method to form the surfaces of industrial components, which improves product quality, lowers machining costs, and reduces processing time. Allowance optimization is a surface matching process, which is important to grinding measurement. The grinding efficiency highly depends on the results of the surface matching between measured points and the design model. However, existing matching methods are usually based on distance and variance minimization, ignoring the grinding requirements to different surfaces, which leads to high processing time and unstable grinding quality. In this article, we proposed a novel surface matching method for robotic grinding. The objective function is constructed via time minimization and involving weights for allowance requirements to different surfaces. This method also balances all measured points’ contributions, avoiding matching distortion due to uneven points’ distribution. Experimental results and comparisons verify the effectiveness of our methods. Comparing to existing matching methods, our method is robust to uneven point distribution and has advantages in reducing the grinding time.

Accurate estimation of feature points based on individual projective plane in video sequence

The stability and quantity of feature matching in video sequence is one of the key issues for feature tracking and some relevant applications. The existing matching methods are based on feature detection, which is usually affected by illumination conditions, noise or occlusions, and this will directly influence matching results. In this paper, we propose an accurate prediction method for interest point estimation in video sequence by extracting the stable mapping for each undetected point in its suitable projective plane, which is based on coplanar feature points that have already been detected in adjacent frames. The proposed prediction method breaks the limitation of the previous approaches that largely rely on feature detection. Our experiments show that our method not only predicts features accurately, but also enriches the correspondences, which prolongs the track length of features.

Real-time ride-sharing framework with dynamic timeframe and anticipation-based migration

The emergence of large-scale online ride-sharing platforms has substantially reformed the way people travel. Ride-sharing plays a very positive role in alleviating traffic congestion, reducing carbon emissions, and improving travel efficiency by sharing transportation resources. However, it is challenging to design an effective real-time ride-sharing framework due to the complex dynamics of a real-life environment. Most proposed models have difficulties in following the density variation of commuters in different time periods. Moreover, the existing matching methods have limitations related to large-scale instances. Therefore, in this paper, we propose a real-time ride-sharing framework with a dynamic timeframe and anticipation-based migration. The problem is formally modeled and two concrete approaches are introduced to dynamically segment timeframes and migrate commuters to future timeframes based on historical data. To solve this problem, we propose a multi-strategy solution graph search heuristic that can easily deal with large-scale instances and provide high-quality solutions. We also conduct extensive experiments on real-world datasets to demonstrate the efficiency and effectiveness of the proposed framework.

Matching Method of Cultural Relic Fragments Constrained by Thickness and Contour Feature

The virtual restoration of cultural relics by adopting computer technology is the trend of cultural relics protection and value mining. In order to solve the problem of large error in the existing matching methods of non-thin-walled cultural relic fragments, in this paper, a matching method of cultural relic fragments is proposed, when the thickness feature and the contour feature are combined, and the strategy of coarse matching followed by fine matching is adopted. Firstly, the broken contour of cultural relic fragments is extracted, and the upper and lower contour lines are identified; secondly, the thickness feature is calculated and the thickness histogram is constructed. After that, the initial matching of fragments is completed according to the similarity of the histogram. Thirdly, the fracture contour is segmented, and the fragments of the mismatching relationship in the initial matching are also eliminated by the modified Hausdorff distance to optimize the coarse matching results. Finally, the SVD method is adopted to solve the rigid body transformation parameters, and the ICP algorithm is employed to accurately align the fine matching. The experimental results show that with the method in this paper, excellent matching results have been obtained regarding the fragments of the Terracotta Warriors. Compared with matching methods of other cultural relic fragments, the accuracy and algorithm cost of this method are better.

Feature Matching Based on Triangle Guidance and Constraints

For images with distortions or repetitive patterns, the existing matching methods usually work well just on one of the two kinds of images. In this paper, we present novel triangle guidance and constraints (TGC)-based feature matching method, which can achieve good results on both kinds of images. We first extract stable matched feature points and combine these points into triangles as the initial matched triangles, and triangles combined by feature points are as the candidates to be matched. Then, triangle guidance based on the connection relationship via the shared feature point between the matched triangles and the candidates is defined to find the potential matching triangles. Triangle constraints, specially the location of a vertex relative to the inscribed circle center of the triangle, the scale represented by the ratio of corresponding side lengths of two matching triangles and the included angles between the sides of two triangles with connection relationship, are subsequently used to verify the potential matches and obtain the correct ones. Comparative experiments show that the proposed TGC can increase the number of the matched points with high accuracy under various image transformations, especially more effective on images with distortions or repetitive patterns due to the fact that the triangular structure are not only stable to image transformations but also provides more geometric constraints.

Available pressure amplitude of linear compressor based on phasor triangle model

The linear compressor for cryocoolers possess the advantages of long-life operation, high efficiency, low vibration and compact structure. It is significant to study the match mechanisms between the compressor and the cold finger, which determines the working efficiency of the cryocooler. However, the output characteristics of linear compressor are complicated since it is affected by many interacting parameters. The existing matching methods are simplified and mainly focus on the compressor efficiency and output acoustic power, while neglecting the important output parameter of pressure amplitude. In this study, a phasor triangle model basing on analyzing the forces of the piston is proposed. It can be used to predict not only the output acoustic power, the efficiency, but also the pressure amplitude of the linear compressor. Calculated results agree well with the measurement results of the experiment. By this phasor triangle model, the theoretical maximum output pressure amplitude of the linear compressor can be calculated simply based on a known charging pressure and operating frequency. Compared with the mechanical and electrical model of the linear compressor, the new model can provide an intuitionistic understanding on the match mechanism with faster computational process. The model can also explain the experimental phenomenon of the proportional relationship between the output pressure amplitude and the piston displacement in experiments. By further model analysis, such phenomenon is confirmed as an expression of the unmatched design of the compressor. The phasor triangle model may provide an alternative method for the compressor design and matching with the cold finger.

A Novel Semantic Matching Method for Indoor Trajectory Tracking

The rapid development of smartphone sensors has provided rich indoor pedestrian trajectory data for indoor location-based applications. To improve the quality of these collected trajectory data, map matching methods are widely used to correct trajectories. However, these existing matching methods usually cannot achieve satisfactory accuracy and efficiency and have difficulty in exploiting the rich information contained in the obtained trajectory data. In this study, we proposed a novel semantic matching method for indoor pedestrian trajectory tracking. Similar to our previous work, pedestrian dead reckoning (PDR) and human activity recognition (HAR) are used to obtain the raw user trajectory data and the corresponding semantic information involved in the trajectory, respectively. To improve the accuracy and efficiency for user trajectory tracking, a semantic-rich indoor link-node model is then constructed based on the input floor plan, in which navigation-related semantics are extracted and formalized for the following trajectory matching. PDR and HAR are further utilized to segment the trajectory and infer the semantics (e.g., “Turn left”, “Turn right”, and “Go straight”). Finally, the inferred semantic information is matched with the semantic-rich indoor link-node model to derive the correct user trajectory. To accelerate the matching process, the semantics inferred from the trajectory are also assigned weights according to their relative importance. The experiments confirm that the proposed method achieves accurate trajectory tracking results while guaranteeing a high matching efficiency. In addition, the resulting semantic information has great application potential in further indoor location-based services.

Estimating Aquifer Parameters from Early Drawdowns in Large-Diameter Wells

The existing equation applicable for large diameter wells in confined aquifers is transformed into a convenient form and a set of semilogarithmic diagnostic curves is developed for identifying the aquifer parameters (storage coefficient and transmissivity) from early drawdowns in large diameter wells. A scaled well function is proposed for the diagnostic curves. The aquifer parameters are estimated by matching the diagnostically plotted drawdowns to one of the diagnostic curves by a parallel shift of only one axis. The substantial curvature of the diagnostic curves and shifting of only one axis facilitate matching and reduce subjectivity. The proposed method is an improvement over the existing matching methods. The new method can reliably identify the aquifer parameters from only early drawdowns and would result in a 100-fold saving in time and money. It is hoped that this method would be helpful to field engineers and practitioners.