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The Gaofen-1 (GF-1) and Gaofen-6 (GF-6) satellites have acquired many GF-1 and GF-6 wide-field-view (WFV) images. These images have been made available for free use globally. The GF-1 WFV (GF-1) and GF-6 WFV (GF-6) images have rational polynomial coefficients (RPCs). In practical applications, RPC corrections of GF-1 and GF-6 images need to be completed using the rational function model (RFM). However, can the accuracy of the rational function model satisfy practical application requirements? To address this issue, a geometric accuracy method was proposed in this paper to evaluate the accuracy of the RFM of GF-1 and GF-6 images. First, RPC corrections were completed using the RFM and refined RFM, respectively. The RFM was constructed using the RPCs and Shuttle Radar Topography Mission (SRTM) 90 m DEM. The RFM was refined via affine transformation based on control points (CPs), which resulted in a refined RFM. Then, an automatic matching method was proposed to complete the automatic matching of GF-1/GF-6 images and reference images, which enabled us to obtain many uniformly distributed CPs. Finally, these CPs were used to evaluate the geometric accuracy of the RFM and refined RFM. The 14th-layer Google images of the corresponding area were used as reference images. In the experiments, the advantages and disadvantages of BRIEF, SIFT, and the proposed method were first compared. Then, the values of the root mean square error (RSME) of 10,561 Chinese, French, and Brazilian GF-1 and GF-6 images were calculated and statistically analyzed, and the local geometric distortions of the GF-1 and GF-6 images were evaluated; these were used to evaluate the accuracy of the RFM. Last, the accuracy of the refined RFM was evaluated using the eight GF-1 and GF-6 images. The experimental results indicate that the accuracy of the RFM for most GF-1 and GF-6 images cannot meet the actual use requirement of being better than 1.0 pixel, the accuracy of the refined RFM for GF-1 images cannot meet practical requirement of being better than 1.0 pixel, and the accuracy of the refined RFM for most GF-6 images meets the practical requirement of being better than 1.0 pixel. However, the RMSE values that meet the requirements are between 0.9 and 1.0, and the geometric accuracy can be further improved.

Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively.

The rapidly developing computer technology has been extensively applied in music teaching. The computer automatic matching technology supports the automatic generation of randomized teaching contents, providing a good tool to develop the musical thinking of students. This paper tentatively introduces this technology to music teaching, and derives a new music teaching mode. The results show that, computer technology can effectively assist in music teaching; the inclusion of computer technology in music teaching arouses students’ interest in music activities; the application of computer automatic matching technology has improved students’ professional skills, live music performance, as well as music ability. The research results greatly promote the reform of music teaching modes and methods.

The continuous development of machine learning procedures and the development of new ways of mapping based on the integration of spatial data from heterogeneous sources have resulted in the automation of many processes associated with cartographic production such as positional accuracy assessment (PAA). The automation of the PAA of spatial data is based on automated matching procedures between corresponding spatial objects (usually building polygons) from two geospatial databases (GDB), which in turn are related to the quantification of the similarity between these objects. Therefore, assessing the capabilities of these automated matching procedures is key to making automation a fully operational solution in PAA processes. The present study has been developed in response to the need to explore the scope of these capabilities by means of a comparison with human capabilities. Thus, using a genetic algorithm (GA) and a group of human experts, two experiments have been carried out: (i) to compare the similarity values between building polygons assigned by both and (ii) to compare the matching procedure developed in both cases. The results obtained showed that the GA—experts agreement was very high, with a mean agreement percentage of 93.3% (for the experiment 1) and 98.8% (for the experiment 2). These results confirm the capability of the machine-based procedures, and specifically of GAs, to carry out matching tasks.

The automatic registration of LiDAR data and optical images, which are heterogeneous data sources, has been a major research challenge in recent years. In this paper, a novel hierarchical method is proposed in which the least amount of interaction of a skilled operator is required. Thereby, two shadow extraction schemes, one from LiDAR and the other from high-resolution satellite images, were used, and the obtained 2D shadow maps were then considered as prospective matching entities. Taken as the base, the reconstructed LiDAR shadows were transformed to image shadows using a four-step hierarchical method starting from a coarse 2D registration model and leading to a fine 3D registration model. In the first step, a general matching was performed in the frequency domain that yielded a rough 2D similarity model that related the LiDAR and image shadow masks. This model was further improved by modeling and compensating for the local geometric distortions that existed between the two heterogeneous data sources. In the third step, shadow masks, which were organized as segmented matched patches, were the subjects of a coinciding procedure that resulted in a coarse 3D registration model. In the last hierarchical step, that model was ultimately reinforced via a precise matching between the LiDAR and image edges. The evaluation results, which were conducted on six datasets and from different relative and absolute aspects, demonstrated the efficiency of the proposed method, which had a very promising accuracy on the order of one pixel.

Aim:The study aimed to assess the clinical feasibility of employing an automatic match during cone beam computed tomography (CBCT) imaging using prostatic calcifications within the 95% isodose set as the region of interest.Materials and methods:CBCT images were analysed on the 5th fraction in 34 patients evaluating the difference between standard manual soft tissue anatomy matching versus auto calcification matching. An assessment of the clinical feasibility of using prostatic calcifications during matching alongside considering the effect a more automated matching process has been conducted on interobserver variability.Results:The standard deviation values of the difference between the soft tissue match (baseline) versus automatic calcification matches fluctuated around 1 mm in all three axes for all of the matches carried out. The interobserver variability observed between the two radiographers was 0·055, 0·065 and 0·045 cm in the vertical, longitudinal and lateral axes, respectively.Findings:The clarity of the calcifications on the CBCT images might explain the low interobserver variability displayed by the two matching radiographers. A calcification provides a clear starting point for image matching before commencing a check of volumetric coverage, if the matching process begins in the same place, it can allow for a standardisation of matching technique between radiographers.

Nowadays, designing and building computer systems has become increasingly difficult; this is essentially due to the great number of existing solutions. This article proposes a hybrid heuristic allowing the connection between meta-models of different systems, which will allow the generation between models conforming to these connected meta-models. First, this article presents the architecture of the generative matching approach named generative automatic matching (GAM), then is introduced an important part of this approach, a hybrid heuristic allowing the matching between the meta-models. Finally, the authors conclude by a multiple criteria evaluation of this approach.

The passengers' luggage checking not noly often make the airport staff embarrassed, but also not easy to locate accurately, which often affects passenger evacuation and causes unnecessary disputes. Most of the airport use fixed manual sorting or the technology of barcode, using these methods, however, which greatly affect the quality of services at the airport. This paper presents a system based on radio frequency identification technology (RFID) to achieve luggage matching automatically. In the system,RFID luggage tags will be attached to the target luggage, and the RFID tags which match with the target luggage will be built into a special boarding cards. Only the two RFID tags matching, passengers can freely leave the door in the exit. In order to solve the problem of overcrowding in the luggage claim, this paper proposes to install the LED prompt lights and luggage tracking devices in the U-shaped turntable to improve the accuracy of the luggage claiming. The system provides a new method for airport luggage positioning and management, which be proved of high efficiency, low error rate with some advanced and practical.

The fractured-vuggy carbonate oil resources in the western basin of China are extremely rich. The connectivity of carbonate reservoirs is complex, and there is still a lack of clear understanding of the development and topological structure of the pore space in fractured-vuggy reservoirs. Thus, effective prediction of fractured-vuggy reservoirs is difficult. In view of this, this work employs adaptive point cloud technology to reproduce the shape and capture the characteristics of a fractured-vuggy reservoir. To identify the complex connectivity among pores, fractures, and vugs, a simplified one-dimensional connectivity model is established by using the meshless connection element method (CEM). Considering that different types of connection units have different flow characteristics, a sequential coupling calculation method that can efficiently calculate reservoir pressure and saturation is developed. By automatic history matching, the dynamic production data is fitted in real-time, and the characteristic parameters of the connection unit are inverted. Simulation results show that the three-dimensional connectivity model of the fractured-vuggy reservoir built in this work is as close as 90% of the fine grid model, while the dynamic simulation efficiency is much higher with good accuracy.

The ontology matching process has become a vital part of the (semantic) web, enabling interoperability among heterogeneous data. To enable interoperability, similar entity pairs across heterogeneous data are discovered using a static set of matchers consisting of linguistic, structural and/or instance matchers that discover similar entities. Numerous sets of matchers exist in the literature; however, none of the matcher sets are capable of achieving good results across all data. In addition, it is both tedious and painstaking for domain experts to select the best set of matchers for the given data to be matched. In this paper, we propose two bootstrapping-based approaches, Bottom-up and Top-down, to automatically select the best set of matchers for the given ontologies to be matched. The selection is processed, based on the characteristics of the ontologies which are quantified by a set of quality metrics. Two new structural quality metrics, the Concept External Structural Richness (CESR) and the Concept Internal Structural Richness (CISR), have also been proposed to better quantify the structural characteristics of the ontology. The best set of matchers is chosen using the sets of patterns learned through the proposed Bottom-up and Top-down bootstrapping approaches. The proposed metrics and the patterns constructed using these approaches are evaluated using the COMA matching tool with existing benchmark ontologies (Benchmark, Conference and Benchmark2 tracks of the OAEI 2011). The proposed Bottom-up based patterns, along with the two proposed quality metrics, achieved better effectiveness (F-measure) in selecting the best set of matchers in comparison with the static set of matching, supervised ML algorithms and the existing automatic matching. Specifically, the proposed Bottom-up patterns achieve a 14.6% Average Gain/Task and a significant improvement of 129% in comparison with the existing KNN model’s Average Gain/Task.