Matching Confidence Research Articles

Practical valid inferences for the two-sample binomial problem

Our interest is whether two binomial parameters differ, which parameter is larger, and by how much. This apparently simple problem was addressed by Fisher in the 1930’s, and has been the subject of many review papers since then. Yet there continues to be new work on this issue and no consensus solution. Previous reviews have focused primarily on testing and the properties of validity and power, or primarily on confidence intervals, their coverage, and expected length. Here we evaluate both. For example, we consider whether a p-value and its matching confidence interval are compatible, meaning that the p-value rejects at level α if and only if the 1−α confidence interval excludes all null parameter values. For focus, we only examine non-asymptotic inferences, so that most of the p-values and confidence intervals are valid (i.e., exact) by construction. Within this focus, we review different methods emphasizing many of the properties and interpretational aspects we desire from applied frequentist inference: validity, accuracy, good power, equivariance, compatibility, coherence, and parameterization and direction of effect. We show that no one method can meet all the desirable properties and give recommendations based on which properties are given more importance.

Robust UAV Thermal Infrared Remote Sensing Images Stitching Via Overlap-Prior-Based Global Similarity Prior Model

The main problem of stitching unmanned aerial vehicle (UAV) thermal infrared remote sensing (TIRS) images lies in that the cumulative error caused by the inaccurate alignment of image matching pairs easily leads to deformation and even failure. Few studies in the literature are reported in stitching TIRS images. For the first time, we propose a simple and robust TIRS image stitching method by exploring prior information during flight. First, according to the position and orientation system information and parameter of camera, the overlap ratio of adjacent images is estimated, and the image pairs with high overlap ratio and high matching confidence in different directions are selected. Then, they are added into the alignment term of global similarity prior (GSP) model. Therefore, each image has more matching pairs constraints compared to the traditional construction method of matching pairs, which greatly improves the local registration capability of GSP and then prevents it from converging to the local optimal solution. The proposed method was extensively evaluated on a dataset including to 24 groups of large-scale farmland TIRS images collected in four experimental areas under different crop growth periods and meteorological conditions. Compared with two commercial tools and two representative stitching algorithms, the proposed method significantly improves the local alignment ability and overall stitching quality on both qualitative and quantitative evaluation. Besides, when the front overlap ratio is reduced from 85% to 70%, the proposed method still shows obvious advantages over the related methods and commercial tools, which improves the acquisition efficiency of UAV TIRS images.

Otolaryngology Match 2020-21: Survey of Prospective Applicants in the Setting of COVID-19.

Objectives:To capture the perspectives of candidates applying for otolaryngology residency positions in the 2020-21 cycle, in the context of disruption caused by the coronavirus disease 2019 (COVID-19) pandemic.Subjects and methods:Candidates planning to apply to the otolaryngology 2020-21 match were invited to complete a cross-sectional online survey. Distribution was via otomatch.com and word of mouth. Descriptive statistics were performed.Results:Of 85 eligible responses (estimated 18.9% of all applicants), many have had at least one board examination (71.8%) disrupted. A majority (85.9%) believe evaluation of candidates will change due to the pandemic, and 54.1% report they were now less confident in matching. Female applicants (37.6% of respondents) were found to have significantly higher odds of decreased confidence in matching (OR 2.781 [95% CI 1.045-7.4044]; P = .041). Many report a move to virtual interviews would increase the number of applications submitted (45.9%) and the number of interviews attended (77.6%). Some applicants (36.5%) did not believe residency programs would gather sufficient information about their candidacy to make an informed decision, and most (62.4%) did not believe that they would gather sufficient information to inform their own rank list.Conclusions:We find that candidates believe their candidacy will be assessed differently in light of the COVID-19 pandemic, are largely less confident in successfully matching, and are planning to apply and interview more broadly. These data are relevant to otolaryngology residency leadership to inform clear dialogue and a smooth transition into an unprecedented application cycle.

Geometry consistency aware confidence evaluation for feature matching

Most existing approaches prune wrong matches via estimating an image transformation or solving a graph-based global matching optimization problem, which usually suffers from varying local transformations and outliers. Inspired by the insight that neighboring true matches usually hold consistent local topological structures across images, in this paper we propose a new approach to evaluate the confidence of each putative match based on how well its two keypoints can predict each other by exploring the geometric constraint with its neighboring matches. With the evaluation, a two-stage approach combining recursively false match pruning and correct match incrementing is presented to obtain the reliable matches. Experiments on various image pairs demonstrate that our approach can conduct robust feature matching in challenging conditions and outperform state-of-the-art approaches.

Reconstruction of multi‐balloon copying curved surface based on linear distance field function optimisation

Aiming at the problems of low precision of three-dimensional (3D) curved surface reconstruction and poor surface smoothness of the reconstruction result, a method based on linear distance field function optimisation is proposed for the reconstruction of multi-balloon copying curved surface. First, curvature weighting is conducted for the curved surface point cloud model constructed with the Poisson's equation (PE), and the boundary value limiting condition of average curvature is used to conduct the smoothness processing of the curved surface; second, due to the dynamic deformation characteristic of multi-balloon copying curved surface, the linear distance field function is used to globally register the non-deformable area and the deformable area, and the distance field gradient is used to estimate the non-deformable area. Moreover, self-adaptive weight optimisation is conducted with the matching confidence level for deformable regions, thus to improve the registration accuracy of 3D point clouds. The simulation results show that the method proposed in this study improves the registration accuracy and surface reconstruction smoothness of standard PE.

On matching confidence intervals and tests for some discrete distributions: methodological and computational aspects

Exact two-tailed tests and two-sided confidence intervals (CIs) for a binomial proportion or Poisson parameter by Sterne (Biometrika 41:117–129, 1954) or Blaker (Can J Stat 28(4):783–798, 2000) are successful in reducing conservatism of the Clopper–Pearson method. However, the methods suffer from an inconsistency between the tests and the corresponding CIs: In some cases, a parameter value is rejected by the test, though it lies in the CI. The problem results from non-unimodality of the test p value functions. We propose a slight modification of the tests that avoids the inconsistency, while preserving nestedness and exactness. Fast and accurate algorithms for both the test modification and calculation of confidence bounds are presented together with their theoretical background.

Matching Confidence Constrained Bundle Adjustment for Multi-View High-Resolution Satellite Images

Bundle adjustment of multi-view satellite images is a powerful tool to align the orientations of all the images in a unified framework. However, the traditional bundle adjustment process faces a problem in detecting mismatches and evaluating low/medium/high-accuracy matches, which limits the final bundle adjustment accuracy, especially when the mismatches are several times more than the correct matches. To achieve more accurate bundle adjustment results, this paper formulates the prior knowledge of matching accuracy as matching confidences and proposes a matching confidence based bundle adjustment method. The core algorithm firstly selects several highest-confidence matches to initially correct orientations of all images, then detects and eliminates the mismatches under the initial orientation guesses and finally formulates both the matching confidences and the forward-backward projection errors as weights in an iterative bundle adjustment process for more accurate orientation results. We compared our proposed method with the famous RANSAC strategy as well as a state-of-the-art bundle adjustment method on the high-resolution multi-view satellite images. The experimental comparisons are evaluated by image checking points and ground control points, which shows that our proposed method is able to obtain more robust and more accurate mismatch detection results than the RANSAC strategy, even though the mismatches are four times more than the correct matches and it can also achieve more accurate orientation results than the state-of-the-art bundle adjustment method.

Mapping Lipid Fragmentation for Tailored Mass Spectral Libraries.

Libraries of simulated lipid fragmentation spectra enable the identification of hundreds of unique lipids from complex lipid extracts, even when the corresponding lipid reference standards do not exist. Often, these in silico libraries are generated through expert annotation of spectra to extract and model fragmentation rules common to a given lipid class. Although useful for a given sample source or instrumental platform, the time-consuming nature of this approach renders it impractical for the growing array of dissociation techniques and instrument platforms. Here, we introduce Library Forge, a unique algorithm capable of deriving lipid fragment mass-to-charge (m/z) and intensity patterns directly from high-resolution experimental spectra with minimal user input. Library Forge exploits the modular construction of lipids to generate m/z transformed spectra in silico which reveal the underlying fragmentation pathways common to a given lipid class. By learning these fragmentation patterns directly from observed spectra, the algorithm increases lipid spectral matching confidence while reducing spectral library development time from days to minutes. We embed the algorithm within the preexisting lipid analysis architecture of LipiDex to integrate automated and robust library generation within a comprehensive LC-MS/MS lipidomics workflow. Graphical Abstract.

Mutual event observations of solar system objects by SRC on Mars Express

Context. Both Martian moons, Phobos and Deimos, have been observed during several imaging campaigns by the Super Resolution Channel (SRC) on the Mars Express probe. Several tens of images are obtained during mutual event observations – when the Martian moons are both observed or together with another solar system body. These observations provide new opportunities to determine the bodies’ positions in their orbits. Aims. A method was sought to automate the observation of the positions of the imaged bodies. Within one image sequence a similarly accurate localization of the objects in all images should be possible. Methods. Shape models of Phobos and Deimos are applied to simulate the appearance of the bodies in the images. Matching the illuminated simulation against the observation provides a reliable determination of the bodies’ location within the image. To enhance the matching confidence several corrections need to be applied to the simulation to closely reconstruct the observation. Results. A list of 884 relative positions between the different objects is provided through the Centre de Données astronomiques de Strasbourg (CDS).

An efficient stereo matching based on fragment matching

We propose a stereo matching method based on image fragments. Unlike traditional pixel-based stereos matching methods, we use edge information in the reference image to divide it into small fragments, and we then use the segments to find the best matching fragments in another reference image from the horizontal and vertical directions. We obtain two disparity maps, and using the match confidence value for each disparity map, we can produce a more accurate disparity map. Next, we calculate the exact disparity value for each pixel within the fragment. Finally, the disparity map is filled and smoothed to obtain the final disparity result. Experiments demonstrated that the proposed method has low computation complexity, high matching accuracy, and the disparity of object edge is clear, and it achieved good performance with the Middlebury and KITTI benchmark.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics.

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Two-Pass K Nearest Neighbor Search for Feature Tracking

In recent years, feature tracking has become one of the most important research topics in computer vision. Many efforts have been made to design excellent feature matching methods. For large-scale structure from motion, however, existing feature tracking methods still need to improve in aspects of speed and matching confidence. To defense the drawbacks, in this paper, we design a simple and efficient feature tracking method based on the standard $k$ nearest neighbor search. First, the parallel scale-invariant feature transform (SIFT) is selected as the feature detector to locate keypoints. Second, the principal component analysis-based SIFT-descriptor extractor is used to compute robust descriptions for the selected keypoints, in which normalized operation is used for boosting the matching score. Third, the two-pass $k$ nearest neighbor search (TP-KNN) is proposed to produce correspondences for image pairs, then leading a significant improvement in the number of matches. Moreover, a geometry-constraint approach is proposed to remove outliers from the initial matches for boosting the matching precision. Finally, we conduct experiment on several challenging benchmark datasets to assess the TP-KNN method against the state-of-the-art methods. Experimental results indicate that the TP-KNN has the best performance in both speed and accuracy.

Improving Protein Detection Confidence Using SWATH-Mass Spectrometry with Large Peptide Reference Libraries.

Protein quantification using data-independent acquisition methods such as SWATH-MS most commonly relies on spectral matching to a reference MS/MS assay library. To enable deep proteome coverage and efficient use of existing data, in silico approaches have been described to use archived or publicly available large reference spectral libraries for spectral matching. Since implicit in the use of larger libraries is the increasing likelihood of false-discoveries, new workflows are needed to ensure high confidence in protein matching under these conditions. We present a workflow which introduces a range of filters and thresholds aimed at increasing confidence that the resulting proteins are reliably detected and their quantitation is consistent and reproducible. We demonstrated the workflow using extended libraries with SWATH data from human plasma samples and yeast-spiked human K562 cell lysate digest.

Exemplar-based image inpainting using structure consistent patch matching

Image inpainting restores lost or deteriorated parts of images according to the information of known regions. Criminisi has proposed an effective exemplar-based inpainting method, which has the advantages of both texture synthesis and diffusion-based inpainting. Yet, it has its own flaws of fast priority dropping and visual inconsistency. In this paper, we propose a space varying updating strategy for the confidence term and a matching confidence term to improve the filling priority estimation. We propose structure consistent patch matching to take the distribution of source and target patch differences into account. Fast Fourier transform is adapted for full image searching to achieve better and faster matching results. Experimental results are given to demonstrate the improvements made by our proposed method.

Detecting ground control points via convolutional neural network for stereo matching

In this paper, we present a novel approach to detect ground control points (GCPs) for stereo matching problem. First of all, we train a convolutional neural network (CNN) on a large stereo set, and compute the matching confidence of each pixel by using the trained CNN model. Secondly, we present a ground control points selection scheme according to the maximum matching confidence of each pixel. Finally, the selected GCPs are used to refine the matching costs, then we apply the new matching costs to perform optimization with semi-global matching algorithm for improving the final disparity maps. We evaluate our approach on the KITTI 2012 stereo benchmark dataset. Our experiments show that the proposed approach significantly improves the accuracy of disparity maps.

SU‐F‐J‐54: Towards Real‐Time Volumetric Imaging Using the Treatment Beam and KV Beam

Purpose:Existing real‐time imaging uses dual (orthogonal) kV beam fluoroscopies and may result in significant amount of extra radiation to patients, especially for prolonged treatment cases. In addition, kV projections only provide 2D information, which is insufficient for in vivo dose reconstruction. We propose real‐time volumetric imaging using prior knowledge of pre‐treatment 4D images and real‐time 2D transit data of treatment beam and kV beam.Methods:The pre‐treatment multi‐snapshot volumetric images are used to simulate 2D projections of both the treatment beam and kV beam, respectively, for each treatment field defined by the control point. During radiation delivery, the transit signals acquired by the electronic portal image device (EPID) are processed for every projection and compared with pre‐calculation by cross‐correlation for phase matching and thus 3D snapshot identification or real‐time volumetric imaging. The data processing involves taking logarithmic ratios of EPID signals with respect to the air scan to reduce modeling uncertainties in head scatter fluence and EPID response. Simulated 2D projections are also used to pre‐calculate confidence levels in phase matching. Treatment beam projections that have a low confidence level either in pre‐calculation or real‐time acquisition will trigger kV beams so that complementary information can be exploited. In case both the treatment beam and kV beam return low confidence in phase matching, a predicted phase based on linear regression will be generated.Results:Simulation studies indicated treatment beams provide sufficient confidence in phase matching for most cases. At times of low confidence from treatment beams, kV imaging provides sufficient confidence in phase matching due to its complementary configuration.Conclusion:The proposed real‐time volumetric imaging utilizes the treatment beam and triggers kV beams for complementary information when the treatment beam along does not provide sufficient confidence for phase matching. This strategy minimizes the use of extra radiation to patients.This project is partially supported by a Varian MRA grant.

Circulant Temporal Encoding for Video Retrieval and Temporal Alignment

We address the problem of specific video event retrieval. Given a query video of a specific event, e.g., a concert of Madonna, the goal is to retrieve other videos of the same event that temporally overlap with the query. Our approach encodes the frame descriptors of a video to jointly represent their appearance and temporal order. It exploits the properties of circulant matrices to efficiently compare the videos in the frequency domain. This offers a significant gain in complexity and accurately localizes the matching parts of videos. The descriptors can be compressed in the frequency domain with a product quantizer adapted to complex numbers. In this case, video retrieval is performed without decompressing the descriptors. We also consider the temporal alignment of a set of videos. We exploit the matching confidence and an estimate of the temporal offset computed for all pairs of videos by our retrieval approach. Our robust algorithm aligns the videos on a global timeline by maximizing the set of temporally consistent matches. The global temporal alignment enables synchronous playback of the videos of a given scene.

Unsupervised Texture Flow Estimation Using Appearance-Space Clustering and Correspondence.

This paper presents a texture flow estimation method that uses an appearance-space clustering and a correspondence search in the space of deformed exemplars. To estimate the underlying texture flow, such as scale, orientation, and texture label, most existing approaches require a certain amount of user interactions. Strict assumptions on a geometric model further limit the flow estimation to such a near-regular texture as a gradient-like pattern. We address these problems by extracting distinct texture exemplars in an unsupervised way and using an efficient search strategy on a deformation parameter space. This enables estimating a coherent flow in a fully automatic manner, even when an input image contains multiple textures of different categories. A set of texture exemplars that describes the input texture image is first extracted via a medoid-based clustering in appearance space. The texture exemplars are then matched with the input image to infer deformation parameters. In particular, we define a distance function for measuring a similarity between the texture exemplar and a deformed target patch centered at each pixel from the input image, and then propose to use a randomized search strategy to estimate these parameters efficiently. The deformation flow field is further refined by adaptively smoothing the flow field under guidance of a matching confidence score. We show that a local visual similarity, directly measured from appearance space, explains local behaviors of the flow very well, and the flow field can be estimated very efficiently when the matching criterion meets the randomized search strategy. Experimental results on synthetic and natural images show that the proposed method outperforms existing methods.

Dolphin: a tool for automatic targeted metabolite profiling using 1D and 2D (1)H-NMR data.

One of the main challenges in nuclear magnetic resonance (NMR) metabolomics is to obtain valuable metabolic information from large datasets of raw NMR spectra in a high throughput, automatic, and reproducible way. To date, established software packages used to match and quantify metabolites in NMR spectra remain mostly manually operated, leading to low resolution results and subject to inconsistencies not attributable to the NMR technique itself. Here, we introduce a new software package, called Dolphin, able to automatically quantify a set of target metabolites in multiple sample measurements using an approach based on 1D and 2D NMR techniques to overcome the inherent limitations of 1D (1)H-NMR spectra in metabolomics. Dolphin takes advantage of the 2D J-resolved NMR spectroscopy signal dispersion to avoid inconsistencies in signal position detection, enhancing the reliability and confidence in metabolite matching. Furthermore, in order to improve accuracy in quantification, Dolphin uses 2D NMR spectra to obtain additional information on all neighboring signals surrounding the target metabolite. We have compared the targeted profiling results of Dolphin, recorded from standard biological mixtures, with those of two well established approaches in NMR metabolomics. Overall, Dolphin produced more accurate results with the added advantage of being a fully automated and high throughput processing package.

New glycoproteomics software, GlycoPep Evaluator, generates decoy glycopeptides de novo and enables accurate false discovery rate analysis for small data sets.

Glycoproteins are biologically significant large molecules that participate in numerous cellular activities. In order to obtain site-specific protein glycosylation information, intact glycopeptides, with the glycan attached to the peptide sequence, are characterized by tandem mass spectrometry (MS/MS) methods such as collision-induced dissociation (CID) and electron transfer dissociation (ETD). While several emerging automated tools are developed, no consensus is present in the field about the best way to determine the reliability of the tools and/or provide the false discovery rate (FDR). A common approach to calculate FDRs for glycopeptide analysis, adopted from the target-decoy strategy in proteomics, employs a decoy database that is created based on the target protein sequence database. Nonetheless, this approach is not optimal in measuring the confidence of N-linked glycopeptide matches, because the glycopeptide data set is considerably smaller compared to that of peptides, and the requirement of a consensus sequence for N-glycosylation further limits the number of possible decoy glycopeptides tested in a database search. To address the need to accurately determine FDRs for automated glycopeptide assignments, we developed GlycoPep Evaluator (GPE), a tool that helps to measure FDRs in identifying glycopeptides without using a decoy database. GPE generates decoy glycopeptides de novo for every target glycopeptide, in a 1:20 target-to-decoy ratio. The decoys, along with target glycopeptides, are scored against the ETD data, from which FDRs can be calculated accurately based on the number of decoy matches and the ratio of the number of targets to decoys, for small data sets. GPE is freely accessible for download and can work with any search engine that interprets ETD data of N-linked glycopeptides. The software is provided at https://desairegroup.ku.edu/research.