Correct Matches Research Articles

Analysis of Automotive Paint Smears Using Attenuated Total Reflection Infrared Microscopy.

Paint smears represent a type of automotive paint sample found at a crime scene that is problematic for forensic automotive paint examiners to analyze as there are no reference materials present in automotive paint databases to generate hit-lists of potential suspect vehicles. Realistic paint smears are difficult to create in a laboratory and have also proven challenging to analyze because of the mixing of the various automotive paint layers. A procedure based on an impact tester has been developed to create smears to simulate paint transfer between vehicles during a collision. Data collected from 24 original equipment manufacturer (OEM) paints in simulated collisions using an impact tester with a steel (inert) substrate to simulate vehicle to vehicle collisions shows that attenuated total reflection infrared microscopy can isolate individual paint layers. For each OEM paint sample, the corresponding smear obtained was dependent upon the conditions used. By varying these conditions, the number of distinct layers obtained could be tuned for each of the OEM paints investigated. Furthermore, the IR spectrum of each layer extracted from the paint smear using alternating least squares was found to compare favorably to an in-house OEM paint infrared spectral library for each layer as the correct match (make and model of the vehicle from which the smear originated) was always found as a top five hit in the hit-list. The results of this study indicate that paint smears developed using an impactor can serve as the basis of realistic proficiency tests for forensic laboratories.

Penalized Wall Function Method for Turbulent Flow Modeling

A novel penalized wall function method for simulations of wall-bounded compressible turbulent flows is proposed. The new approach is based on the Reynolds-averaged Navier–Stokes (RANS) equations to model the outer region of the turbulent boundary layer, while the inner part is approximated by the equilibrium wall function model. The differential formulation to match the external and the wall function solutions is reformulated in a form of the generalized characteristic-based volume penalization method to model the transfer of the shear stress from the outer region of the boundary layer to the wall and to impose the wall-stress boundary conditions on the RANS solution. The exchange location is specified implicitly through a localized source term in the boundary layer equation, written as a function of the normalized distance from the wall. The wall-stress condition is determined by solving an auxiliary equation for the wall-stress, ensuring the correct matching of the RANS and the wall function solutions at the exchange layer. The proposed method noticeably reduces the near-wall mesh resolution requirements without significant modification of the RANS solver and removes the ill-defined explicit matching procedure, commonly used by traditional wall function-based methods. The penalized wall function approach is implemented using the vertex-centered control volume method on unstructured computational grids. The effectiveness of the developed penalized wall function method is demonstrated for twodimensional bump-in-channel flow for the Spalart–Allmaras turbulence model.

Tracklet-to-object Matching for Climbing Starlink Satellites through Recursive Orbit Determination and Prediction

Concerns for the collision risk involving Starlink satellites have motivated the interest in obtaining their accurate orbit knowledge. However, accurate orbit determination (OD) and prediction (OP) of Starlink satellites confront two main challenges: mismatching or missed matching of sparse tracklets to maneuvering satellites, and unknown or unmodeled orbit maneuvers. How to exactly associate a tracklet to the right satellite is the primary issue, since a maneuvering satellite does not follow the naturally evolving orbit during the maneuvering, while more tracklets are needed for developing an accurate orbit maneuver model. If these two challenges are not well addressed, it may lead to catalog maintenance failure or even loss of objects. This paper proposes a method to correctly match tracklets to the climbing Starlink satellites. It is based on the recursive OD and OP, in which the orbit maneuver is modeled and the thrust is estimated, such that the subsequent OP accuracy guarantees the correct match of tracklets shortly after the OD time. Experiments with climbing Starlink satellites demonstrate that the tracklets within three days of the last TLE (two-line element) are all correctly matched to the right satellites. With the matched tracklets, the thrust accelerations of climbing Starlink satellites can be precisely estimated through an orbit control approach, and the position prediction accuracy over 48 hours is at the level of a few kilometers, providing accurate orbit knowledge for reliable collision warning involving Starlink satellites.

Influence Of Different Lighting Conditions On Accuracy Of Visual Shade Matching By Dental Professionals

Aims : The purpose of this study was to assess the influence of different lighting conditions on accuracy of visual shade matching by dental professionals. This study also determined any gender bias in shade matching abilities of dental practitioners. Methods and Material : Sixty dental practitioners participated in this study. All participants were tested for color blindness using Ishihara’s tests. Two classical vita shade tabs were randomly selected, and their identification codes were concealed. The participants were asked to match these selected tabs by using a complete vita shade guide under three different light conditions-natural light, clinical light, and using the commercially available hand-held shade matching device. The chosen shade tabs were recorded, and the correct matches were counted. Statistical analysis used : The data were statistically analyzed using Mann Whitney ‘U’ test and Kruskal Wallis ‘H’ test. Results : Hand-held shade matching light was better than clinical light in terms of accuracy of visual shade matching. Natural daylight was statistically significantly better than clinical light (p =0.001). Accuracy among male participants was lower as compared to female participants, but the difference was not statistically significant (p>0.05). Conclusions : The use of a standardized hand-held shade matching device is comparable to natural daylight, and is recommended for accurate shade matching in absence of natural lighting conditions.

Forgery detection in medical images with distinguished recognition of original and tampered regions using density-based clustering technique

Telemedicine is one of the most significant part of the medical field. It enables transfer of medical images over the internet which offers telediagnosis facility. Due to transfer of medical images from different channels, detection of their authenticity is a crucial research concern. Copy-move forgery is one of the most popular technique for image manipulation. We propose a method for copy-move forgery detection in medical images. Our method apply boundary extraction followed by Laplacian blob detection from the image to identify regions with similar properties. Further, we utilize Good Features To Track (GFTT) and BinBoost techniques for keypoint extraction and descriptor computation, respectively. Similar descriptors are identified using Hamming distance-based nearest neighbor search. Clustering over keypoints is performed using Ant Colony Density-based Clustering (ACDC) technique. We employ Fast Sample Consensus (FSC) technique for selection of correct matches and removal of imprecise keypoints. Further, we use correlation map generation for localization of manipulated regions. Experimental outcomes display that our technique can efficaciously identify tampered medical images even when manipulated images are sustaining various geometrical and post-processing attacks. Proposed technique can also efficaciously distinguish between original and tampered region within forged medical images using Haralick texture features.

An efficient SIFT-based matching algorithm for optical remote sensing images

ABSTRACT Scale-invariant feature transform (SIFT) and its improved versions have been widely used to match the remote-sensing optical images. However, it is still a challenging task to get enough correct matching pairs by using SIFT-based methods. In this letter, an efficient matching scheme is proposed to increase the number of correct matches. The proposed matching scheme uses scale, orientation, and translation differences between the input images to remove the outliers and to retain the correct matches. At first, the initial matching candidates are obtained by a SIFT-based algorithm. Then, our proposed matching scheme is used to select the correct matching pairs. The proposed method can obtain more correct matches than the state-of-the-art methods. Experiments are performed on five pairs of optical images to verify the performance of the proposed method.

Automatic Matching of Multimodal Remote Sensing Images via Learned Unstructured Road Feature

Automatic matching of multimodal remote sensing images remains a vital yet challenging task, particularly for remote sensing and computer vision applications. Most traditional methods mainly focus on key point detection and description of the original image, thus ignoring the deep semantic feature information such as semantic road features, with the result that the traditional method can not effectively resist nonlinear grayscale distortion, and has low matching efficiency and poor accuracy. Motivated by this, this paper proposes a novel automatic matching method named LURF via learned unstructured road features for the multimodal images. There are four main contributions in LURF. To begin with, the semantic road features were extracted from multimodal images based on segmentation model CRESIv2. Next, based on semantic road features, a stable and reliable intersection point detector has been proposed to detect unstructured key points. Moreover, a local entropy descriptor has been designed to describe key points with the local skeleton feature. Finally, a global optimization strategy is adopted to achieve the correct matching. The extensive experimental results demonstrate that the proposed LURF outperforms other state-of-the-art methods in terms of both accuracy and efficiency on different multimodal image data sets.

An Optimized SIFT-OCT Algorithm for Stitching Aerial Images of a Loblolly Pine Plantation

When producing orthomosaic from aerial images of a forested area, challenges arise when the forest canopy is closed, and tie points are hard to find between images. The recent development in deep leaning has shed some light in tackling this problem with an algorithm that examines each image pixel-by-pixel. The scale-invariant feature transform (SIFT) algorithm and its many variants are widely used in feature-based image stitching, which is ideal for orthomosaic production. However, although feature-based image registration can find many feature points in forest image stitching, the similarity between images is too high, resulting in a low correct matching rate and long splicing time. To counter this problem by considering the characteristics of forest images, the inverse cosine function ratio of the unit vector dot product (arccos) is introduced into the SIFT-OCT (SIFT skipping the first scale-space octave) algorithm to overcome the shortfalls of too long a matching time caused by too many feature points for matching. Then, the fast sample consensus (FSC) algorithm was introduced to realize the deletion of mismatched point pairs and improve the matching accuracy. This optimized method was tested on three sets of forest images, representing the forest core, edge, and road areas of a loblolly pine plantation. The same process was repeated by using the regular SIFT and SIFT-OCT algorithms for comparison. The results showed the optimized SIFT-OCT algorithm not only greatly reduced the splicing time, but also increased the correct matching rate.

Global and local geometric constrained feature matching for high resolution remote sensing images

Feature matching is the foundation of many large-scale vision tasks, such as 3D modeling and simultaneous localization and mapping (SLAM). There are few feature matching algorithms based on geometric constraints. In this paper, we combine global and local geometric constraints. We use the grid-based motion statistics (GMS) feature matching algorithm as the global geometric constraint to obtain high probability correct matching areas and feature matches and then use local geometric constraints to predict and obtain the final matching results. This algorithm can meet the high precision and high efficiency requirements of high resolution remote sensing images. The experiments show that the proposed algorithm can obtain more robust and precise results than the existing feature matching algorithms based on feature descriptions, such as SURF and ORB. Additionally, our algorithm can also be extended to other types of high-resolution images.

RI-MFM: A Novel Infrared and Visible Image Registration with Rotation Invariance and Multilevel Feature Matching

In the past ten years, multimodal image registration technology has been continuously developed, and a large number of researchers have paid attention to the problem of infrared and visible image registration. Due to the differences in grayscale distribution, resolution and viewpoint between two images, most of the existing infrared and visible image registration methods are still insufficient in accuracy. To solve such problems, we propose a new robust and accurate infrared and visible image registration method. For the purpose of generating more robust feature descriptors, we propose to generate feature descriptors using a concentric-circle-based feature-description algorithm. The method enhances the description of the main direction of feature points by introducing centroids, and, at the same time, uses concentric circles to ensure the rotation invariance of feature descriptors. To match feature points quickly and accurately, we propose a multi-level feature-matching algorithm using improved offset consistency for matching feature points. We redesigned the matching algorithm based on the offset consistency principle. The comparison experiments with several other state-of-the-art registration methods in CVC and homemade datasets show that our proposed method has significant advantages in both feature-point localization accuracy and correct matching rate.

FPGA-based multi-view stereo system with flexible measurement setup

In recent years, stereoscopic image processing algorithms have gained importance for a variety of applications. To capture larger measurement volumes, multiple stereo systems are combined into a multi-view stereo (MVS) system. To reduce the amount of data and the data rate, calculation steps close to the sensors are outsourced to Field Programmable Gate Arrays (FPGAs) as upstream computing units. The calculation steps include lens distortion correction, rectification and stereo matching. In this paper a FPGA-based MVS system with flexible camera arrangement and partly overlapping field of view is presented. The system consists of four FPGA-based passive stereoscopic systems (Xilinx Zynq-7000 7020 SoC, EV76C570 CMOS sensor) and a downstream processing unit (Zynq Ultrascale ZU9EG SoC). This synchronizes the sensor near processing modules and receives the disparity maps with corresponding left camera image via HDMI. The subsequent computing unit calculates a coherent 3D point cloud. Our developed FPGA-based 3D measurement system captures a large measurement volume at 24 fps by combining a multiple view with eight cameras (using Semi-Global Matching for an image size of 640 px × 460 px, up to 256 px disparity range and with aggregated costs over 4 directions). The capabilities and limitation of the system are shown by an application example with optical non-cooperative surface.

(Retracted) Computer-aided three-dimensional animation design and enhancement based on spatial variation and convolution algorithm

Computer-aided technology is a system of personal confidential interaction between computer and users under the guidance of people. Due to the continuous development of computer technology, this technology has been used in many fields. Computer-aided design is the use of computer systems to assist designers in engineering or product design. It is a technology to achieve the best design effect. Three-dimensional (3D) animation is not limited by time and space. It uses various forms of expression to express complex and abstract program content, scientific principles, and abstract concepts in centralized and simplified forms. The creative feature of animation is that the author does not use the advanced 3D technology to present the real world to the audience. Here, we aim to study the 3D animation image design and image enhancement based on computer-aided technology, hoping to make the 3D animation more realistic and make the film and television picture clearer with the help of computer-aided technology. We expound the current situation of 3D graphics research. We analyze the current development of the 3D animation industry. We propose an extreme value constraint based on spatial variation and a convolution algorithm based on learning a ringing images. The experimental results show that after Retinex method processes the image, the matching feature points of the target image in the scale-invariant feature transformation algorithm are 55. The correct matching rate of feature points after homomorphic filtering is 0.85. The number of correct matching points of the image processed by Retinex method changes greatly, and the change of homomorphic filtering is small.

A robust registration method for UAV thermal infrared and visible images taken by dual-cameras

Automatic registration of unmanned aerial vehicle (UAV) thermal infrared and visible (TIR&V) images is fundamental for subsequent applications. However, few studies address this issue due to significant radiation gap, shape gap, and texture gap among TIR&V images. The area-based methods are not able to satisfy the accuracy and robustness of location at the same time, while the image pyramid-based methods are computationally expensive. To alleviate these problems, we proposed a so-called TWMM method for the registration of UAV TIR&V images taken by the camera equipped with both thermal infrared and visible sensors. TWMM is realized by combining Template matching with Weights, Multilevel local max-pooling, and Max index backtracking. TWMM consists of four steps: (1) computing similarity maps of the atomic patches using template matching with weights; (2) building pyramid similarity maps using multilevel local max-pooling; (3) deducing the corresponding points (CPs) from top to bottom using max index backtracking; and (4) eliminating outliers and estimating homography. Among the four steps, step 1 and step 2 are used to compute the similarity maps of patches with different sizes; step 3 and step 4 are used to deduce CPs and estimate homography with multiple similarity maps. TWMM was comprehensively evaluated with 600 UAV image pairs under four different scenes and also compared with current methods (i.e. SIFT, SURF, RIFT, RCB, TFeat, HardNet, RANSAC_Flow, HOPC, and CFOG). These image pairs have multiple features, i.e., different land covers, spatial resolutions, and illumination conditions, etc. Results indicate that TWMM achieves an 86.0% correct CP ratio (RCP) and a 96.0% correct matching rate (CMR) for all test images, which is a 15.1% improvement and 11.6% improvement, respectively, over the best state-of-the-art methods. TWMM also shows better robustness than other methods for weak-light images, achieving a 20.7% improvement in RCP and a 28.1% improvement in CMR. Therefore, TWMM is an effective and robust method for UAV TIR&V image registration and has good ability under different scenes.

RI-LPOH: Rotation-Invariant Local Phase Orientation Histogram for Multi-Modal Image Matching

To better cope with the significant nonlinear radiation distortions (NRD) and severe rotational distortions in multi-modal remote sensing image matching, this paper introduces a rotationally robust feature-matching method based on the maximum index map (MIM) and 2D matrix, which is called the rotation-invariant local phase orientation histogram (RI-LPOH). First, feature detection is performed based on the weighted moment equation. Then, a 2D feature matrix based on MIM and a modified gradient location orientation histogram (GLOH) is constructed and rotational invariance is achieved by cyclic shifting in both the column and row directions without estimating the principal orientation separately. Each part of the sensed image’s 2D feature matrix is additionally flipped up and down to obtain another 2D matrix to avoid intensity inversion, and all the 2D matrices are concatenated by rows to form the final 1D feature vector. Finally, the RFM-LC algorithm is introduced to screen the obtained initial matches to reduce the negative effect caused by the high proportion of outliers. On this basis, the remaining outliers are removed by the fast sample consensus (FSC) method to obtain optimal transformation parameters. We validate the RI-LPOH method on six different types of multi-modal image datasets and compare it with four state-of-the-art methods: PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH. The experimental results show that our proposed method has obvious advantages in the success rate (SR) and the number of correct matches (NCM). Compared with PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH, the mean SR of RI-LPOH is 170.3%, 279.8%, 81.6%, and 25.4% higher, respectively, and the mean NCM is 13.27, 20.14, 1.39, and 2.42 times that of the aforementioned four methods.

1035 Gender Equality in Heart Transplantation: A Single Centre Study

Abstract Background International Society for Heart and Lung Transplantation catergoirises female gender in donor/recipient, as a significant risk factor for mortality after heart transplantation. Further, donor-recipient gender mismatch is also reported to be a determinant of post-transplant morbidity/mortality. To examine the effect of gender in survival outcomes, we conducted a retrospective study. Method Population comprised of 298 patients undergoing heart transplantation from 2001 to 2021, divided into four groups on the basis of donor and recipient matching. Group A consisted of men who received male donor-hearts, group B of women who received female donor-hearts, group C of women who received male donor-hearts, and group D of men who received female donor-hearts. Standard heart transplantation protocols were applied to all patient groups for graft preservation, surgical technique, donor-recipient size matching, induction & immunosuppressive therapy. Results The study groups were found to be homogeneous to the major preoperative- risk- factors (aetiology, transplantation status, donor-recipient-age, total ischemic time). Donor gender, recipient gender and donor-recipient gender mismatching did not significantly modify the short and mid-term survivals, functional recovery, and primary graft dysfunction post-surgery. Conclusions Even though previous reports suggest that gender negatively affects survival, this proved to have no influence on the outcomes in our study. These results can be explained by a correct donor-recipient size matching. The well-documented female recipients’ tendency to more frequent and fatal rejection was not confirmed in our experience. The patient's age at transplantation, the routine use of induction therapy and an aggressive immunosuppressive regimen may be the substrate of these findings.

1036 Gender Equality in Heart Transplantation: A Single Centre Study

Abstract Background International Society for Heart and Lung Transplantation categorises female gender in donor/recipient, as a significant risk factor for mortality after heart transplantation. Further, donor-recipient gender mismatch is also reported to be a determinant of post-transplant morbidity/mortality. To examine the effect of gender in survival outcomes, we conducted a retrospective study. Method Population comprised of 298 patients undergoing heart transplantation from 2001 to 2021, divided into four groups on the basis of donor and recipient matching. Group A consisted of men who received male donor-hearts, group B of women who received female donor-hearts, group C of women who received male donor-hearts, and group D of men who received female donor-hearts. Standard heart transplantation protocols were applied to all patient groups for graft preservation, surgical technique, donor-recipient size matching, induction & immunosuppressive-therapy. Results The study groups were found to be homogeneous to the major preoperative- risk- factors (aetiology, transplantation status, donor-recipient-age, total ischemic time). Donor gender, recipient gender and donor-recipient gender mismatching did not significantly modify the short and mid-term survivals, functional recovery, and primary graft dysfunction post-surgery. Conclusions Even though previous reports suggest that gender negatively affects survival, this proved to have no influence on the outcomes in our study. These results can be explained by a correct donor-recipient size matching. The well-documented female recipients’ tendency to more frequent and fatal rejection was not confirmed in our experience. The patient's age at transplantation, the routine use of induction therapy and an aggressive immunosuppressive regimen may be the substrate of these findings.

Invariants in problems of pattern recognition and control of states of complex objects

Objective. The aim of the study is to build emulators of the main sensors of a small spacecraft (SC).Method. The quality of recovery is determined by the standard deviation (RMS) of the data from the standards. The study is based on the application of the dynamic programming method.Result. A review of works in the field of application of stable characteristics - invariants, in the applied areas of pattern recognition and state control of complex technical systems is carried out. A method aimed at identifying failures and building small spacecraft sensor emulators based on the analysis of stable indicators: correlation and covariance coefficients is proposed and tested. The proposed approach makes it possible to increase the overall reliability of the information-measuring system of a small spacecraft (SC). In the future, to emulate a failed sensor, it is planned to use multiple correlation of time sequences.Conclusion. In the general case, recognition is reduced to comparing the graph structures of objects to the correct matching of hierarchical image models based on various invariants. Control of the states of complex systems is based on the use of stable characteristics that act as invariant or subinvariant descriptors. Invariants in various applications help to recognize the current situation with a sufficient degree of accuracy, usually reducing the dimension of the problem.

ULMR: An Unsupervised Learning Framework for Mismatch Removal.

Due to radiometric and geometric distortions between images, mismatches are inevitable. Thus, a mismatch removal process is required for improving matching accuracy. Although deep learning methods have been proved to outperform handcraft methods in specific scenarios, including image identification and point cloud classification, most learning methods are supervised and are susceptible to incorrect labeling, and labeling data is a time-consuming task. This paper takes advantage of deep reinforcement leaning (DRL) and proposes a framework named unsupervised learning for mismatch removal (ULMR). Resorting to DRL, ULMR firstly scores each state–action pair guided by the output of classification network; then, it calculates the policy gradient of the expected reward; finally, through maximizing the expected reward of state–action pairings, the optimal network can be obtained. Compared to supervised learning methods (e.g., NM-Net and LFGC), unsupervised learning methods (e.g., ULCM), and handcraft methods (e.g., RANSAC, GMS), ULMR can obtain higher precision, more remaining correct matches, and fewer remaining false matches in testing experiments. Moreover, ULMR shows greater stability, better accuracy, and higher quality in application experiments, demonstrating reduced sampling times and higher compatibility with other classification networks in ablation experiments, indicating its great potential for further use.

A Novel Multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies

Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.

Robust regression using probabilistically linked data

AbstractThere is growing interest in a data integration approach to survey sampling, particularly where population registers are linked for sampling and subsequent analysis. The reason for doing this is simple: it is only by linking the same individuals in the different sources that it becomes possible to create a data set suitable for analysis. But data linkage is not error free. Many linkages are nondeterministic, based on how likely a linking decision corresponds to a correct match, that is, it brings together the same individual in all sources. High quality linking will ensure that the probability of this happening is high. Analysis of the linked data should take account of this additional source of error when this is not the case. This is especially true for secondary analysis carried out without access to the linking information, that is, the often confidential data that agencies use in their record matching. We describe an inferential framework that allows for linkage errors when sampling from linked registers. After first reviewing current research activity in this area, we focus on secondary analysis and linear regression modeling, including the important special case of estimation of subpopulation and small area means. In doing so we consider both robustness and efficiency of the resulting linked data inferences.This article is categorized under: Algorithms and Computational Methods > Maximum Likelihood Methods Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Multivariate Analysis