Matching Accuracies Research Articles

A Method for Measuring the Error Rules in Visual Inertial Odometry Based on Scene Matching Corrections

To address problems in the integrated navigation error law of unmanned aerial vehicles (UAVs), this paper proposes a method for measuring the error rule in visual inertial odometry based on scene matching corrections. The method involves several steps to build the solution. Firstly, separate models were constructed for the visual navigation model, the Micro-Electromechanical System (MEMS) navigation model, and the scene matching correction model. Secondly, an integrated navigation error measurement model based on scene matching corrections and MEMS navigation was established (the MEMS+SM model). Finally, an integrated navigation error measurement model based on scene matching corrections, visual navigation, and MEMS navigation was constructed (the VN+MEMS+SM model). In the experimental part, this paper first calculates the average error of the VN+MEMS+SM model and the MEMS+SM model under different scene matching accuracies, scene matching times, and MEMS accuracies. The results indicate that, when the scene matching accuracy is less than 10 m and the scene matching time is less than 10 s, the errors of the VN+MEMS+SM model and the MEMS+SM model are approximately equal. Furthermore, the relationship between the scene matching time and the scene matching accuracy in the EMS+SM model was calculated. The results show that, when the scene matching time is 10 s, the critical values of the image matching accuracies required to achieve average errors of 10 m, 30 m, and 50 m are approximately 160 m, 240 m, and 310 m. Additionally, when the MEMS accuracy is 150, the scene matching accuracy is 50 m, and the scene matching time exceeds 135 s, the average error of the VN+MEMS+SM model will be smaller than that of the MEMS+SM model.

A Sheep Identification Method Based on Three-Dimensional Sheep Face Reconstruction and Feature Point Matching.

As the sheep industry rapidly moves towards modernization, digitization, and intelligence, there is a need to build breeding farms integrated with big data. By collecting individual information on sheep, precision breeding can be conducted to improve breeding efficiency, reduce costs, and promote healthy breeding practices. In this context, the accurate identification of individual sheep is essential for establishing digitized sheep farms and precision animal husbandry. Currently, scholars utilize deep learning technology to construct recognition models, learning the biological features of sheep faces to achieve accurate identification. However, existing research methods are limited to pattern recognition at the image level, leading to a lack of diversity in recognition methods. Therefore, this study focuses on the small-tailed Han sheep and develops a sheep face recognition method based on three-dimensional reconstruction technology and feature point matching, aiming to enrich the theoretical research of sheep face recognition technology. The specific recognition approach is as follows: full-angle sheep face images of experimental sheep are collected, and corresponding three-dimensional sheep face models are generated using three-dimensional reconstruction technology, further obtaining three-dimensional sheep face images from three different perspectives. Additionally, this study developed a sheep face orientation recognition algorithm called the sheep face orientation recognition algorithm (SFORA). The SFORA incorporates the ECA mechanism to further enhance recognition performance. Ultimately, the SFORA has a model size of only 5.3 MB, with accuracy and F1 score reaching 99.6% and 99.5%, respectively. During the recognition task, the SFORA is first used for sheep face orientation recognition, followed by matching the recognition image with the corresponding three-dimensional sheep face image based on the established SuperGlue feature-matching algorithm, ultimately outputting the recognition result. Experimental results indicate that when the confidence threshold is set to 0.4, SuperGlue achieves the best matching performance, with matching accuracies for the front, left, and right faces reaching 96.0%, 94.2%, and 96.3%, respectively. This study enriches the theoretical research on sheep face recognition technology and provides technical support.

Demystifying the likelihood of reidentification in neuroimaging data: A technical and regulatory analysis

Abstract Sharing research data has been widely promoted in the field of neuroimaging and has enhanced the rigor and reproducibility of neuroimaging studies. Yet the emergence of novel software tools and algorithms, such as face recognition, has raised concerns due to their potential to reidentify defaced neuroimaging data that are thought to have been deidentified. Despite the surge of privacy concerns, however, the risk of reidentification via these tools and algorithms has not yet been examined outside the limited settings for demonstration purposes. There is also a pressing need to carefully analyze regulatory implications of this new reidentification attack because concerns about the anonymity of data are the main reason that researchers think they are legally constrained from sharing their data. This study aims to tackle these gaps through rigorous technical and regulatory analyses. Using a simulation analysis, we first tested the generalizability of the matching accuracies in defaced neuroimaging data reported in a recent face recognition study (Schwarz et al., 2021). The results showed that the real-world likelihood of reidentification in defaced neuroimaging data via face recognition would be substantially lower than that reported in the previous studies. Next, by taking a US jurisdiction as a case study, we analyzed whether the novel reidentification threat posed by face recognition would place defaced neuroimaging data out of compliance under the current regulatory regime. Our analysis suggests that defaced neuroimaging data using existing tools would still meet the regulatory requirements for data deidentification. A brief comparison with the EU’s General Data Protection Regulation (GDPR) was also provided. Then, we examined the implication of NIH’s new Data Management and Sharing Policy on the current practice of neuroimaging data sharing based on the results of our simulation and regulatory analyses. Finally, we discussed future directions of open data sharing in neuroimaging.

Improving the Out-of-Domain Matching Reliability and Positioning Accuracy of Underwater Gravity Matching Navigation Based on a Novel Cyclic Boundary Semisquare-Domain Researching Method

A gravity matching algorithm is a crucial component of a vehicle’s underwater navigation system. This article studies the improvement in the out-of-domain matching reliability and positioning accuracy of underwater navigation. To achieve these goals, a novel cyclic boundary semisquare-domain researching (CBSR) method is proposed. Two times the inertial error is first employed to span an initial small-square domain and find an initial optimal matching position, which results in controlling the matching efficiency. If this optimal position is located on the domain boundary, the cyclic boundary semisquare domain rematching mechanism is triggered. The cyclic boundary semisquare domain is iteratively generated to perform repositioning and obtain a better matching position until it falls into the interior of the matching domain. The final optimal matching position is used to calibrate the sensor’s parameters and aid the navigation of the underwater vehicle. Experimental results prove that the proposed CBSR method has outstanding positioning capacity for underwater gravity matching navigation, and higher matching reliability and lower mismatching probability of the vehicle’s out-of-domain positioning. For excellent and good suitable tracks, the number of out-of-domain mismatches of the CBSR method, compared with TERCOM, are reduced by 97.73% and 86.05%, respectively, while the out-of-domain average matching accuracies are all less than one grid resolution and are improved by 85.23% and 81.07%, indicating the effectiveness and feasibility of the proposed CBSR model for improving the matching reliability and positioning accuracy of out-of-domain underwater navigation.

Improving Matching Efficiency and Out-of-domain Reliability of Underwater Gravity Matching Navigation Based on a Novel Soft-margin Local Semicircular-domain Re-searching Model

This paper mainly studies the improvement of the efficiency and out-of-domain reliability of gravity matching navigation for underwater vehicles. To overcome the traversal low-efficiency problem of the traditional terrain contour matching (TERCOM) algorithm and improve the positioning reliability of its out-of-domain mismatches, a novel soft-margin local semicircular-domain re-searching model (SLSR) is proposed by integrating the soft-margin circular grid matching (SCGM) mechanism and the local semicircular grid re-matching (LSGR) mechanism. SCGM uses three times the inertial navigation cumulative error and adds the unit grid resolution as the soft margin boundary to generate the soft-margin circular domain, which contributes to reducing in-domain matching grid points and enhancing the matching efficiency of algorithms. Then the optimal matching position in this soft-margin circular domain is obtained by using the optimization principle of matching indices. LSGR is triggered when the optimal matching position of SCGM is located near the soft-margin circular-domain boundary. It employs this optimal matching point as the center and the unit inertial navigation error as the radius to recreate the semicircular local re-searching matched grid domain (termed as semicircular domain). Moreover, the optimal matching point in this semicircular domain is obtained by the matching index optimization principle, and then it is compared and updated to obtain the final best matching position of SLSR. The simulation results show that SCGM and LSGR of the proposed SLSR method can effectively improve the matching efficiency and out-of-domain matching reliability of underwater navigation, respectively. Under the same testing conditions for the tracking starting points from three gravity regions, the number of out-of-domain mismatches of SLSR, compared with TERCOM, are lower up to 92.68%, 90.24% and 98.62%, while the average matching accuracies are relatively improved by 88.37%, 85.48% and 83.66%, which verifies the validity and feasibility of the proposed SLSR model on improving the efficiency and out-of-domain reliability of underwater gravity matching navigation.

Dense Feature Matching Based on Homographic Decomposition

Finding robust and accurate feature matches is a fundamental problem in computer vision. However, incorrect correspondences and suboptimal matching accuracies lead to significant challenges for many real-world applications. In conventional feature matching, corresponding features in an image pair are greedily searched using their descriptor distance. The resulting matching set is then typically used as input for geometric model fitting methods to find an appropriate fundamental matrix and filter out incorrect matches. Unfortunately, this basic approach cannot solve all practical problems, such as fundamental matrix degeneration, matching ambiguities caused by repeated patterns and rejection of initially mismatched features without further reconsideration. In this paper we introduce a novel matching pipeline, which addresses all of the aforementioned challenges at once: First, we perform <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iterative rematching</i> to give mismatched feature points a further chance for being considered in later processing steps. Thereby, we are searching for inliers that exhibit the same homographic transformation per iteration. The resulting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homographic decomposition</i> is used for refining matches, occlusion detection (e.g. due to parallaxes) and extrapolation of additional features in critical image areas. Furthermore, Delaunay triangulation of the matching set is utilized to minimize the repeated pattern problem and to implement <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">focused matching</i> . Doing so, enables us to further increase matching quality by concentrating on local image areas, defined by the triangular mesh. We present and discuss experimental results with multiple real-world matching datasets. Our contributions, besides improving matching recall and precision for image processing applications in general, also relate to use cases in image-based computer graphics.

DCT-Based Local Descriptor for Robust Matching and Feature Tracking in Wide Area Motion Imagery

We introduce a novel discrete cosine transform-based feature (DCTF) descriptor designed for both robustly matching features in aerial video and tracking features across wide-baseline oblique views in aerial wide area motion imagery (WAMI). Our DCTF descriptor preserves local structure more compactly in the frequency domain by utilizing the mathematical properties of the discrete cosine transform (DCT) and outperforms widely used the spatial-domain feature extraction methods, such as speeded up robust features (SURF) and scale-invariant feature transform (SIFT). The DCTF descriptor can be used in combination with other feature detectors, such as SURF and features from accelerated segment test (FAST), for which we provide experimental results. The performance of DCTF for image matching and feature tracking is evaluated on two city-scale aerial WAMI data sets (ABQ-215 and LA-351) and a synthetic aerial drone video data set digital imaging and remote sensing image generation (Rochester Institute of Technology (RIT)-DIRSIG). DCTF is a compact 120-D descriptor that is less than half the dimensionality of state-of-the-art deep learning-based approaches, such as SuperPoint, LF-Net, and DeepCompare, which requires no learning and is domain-independent. Despite its small size, the DCTF descriptor surprisingly produces the highest image matching accuracies ( F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> = 0.76 and ABQ-215), the longest maximum and average feature track lengths, and the lowest tracking error (0.3 pixel, LA-351) compared with both handcrafted and deep learning features.

Unknown Disease Outbreaks Detection: A Pilot Study on Feature-Based Knowledge Representation and Reasoning Model.

Background: The outbreak of COVID-19 in 2019 has rapidly swept the world, causing irreparable loss to human beings. The pandemic has shown that there is still a delay in the early response to disease outbreaks and needs a method for unknown disease outbreak detection. The study's objective is to establish a new medical knowledge representation and reasoning model, and use the model to explore the feasibility of unknown disease outbreak detection.Methods: The study defined abnormal values with diagnostic significances from clinical data as the Features, and defined the Features as the antecedents of inference rules to match with knowledge bases, achieved in detecting known or emerging infectious disease outbreaks. Meanwhile, the study built a syndromic surveillance base to capture the target cases' Features to improve the reliability and fault-tolerant ability of the system.Results: The study combined the method with Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), and early COVID-19 outbreaks as empirical studies. The results showed that with suitable surveillance guidelines, the method proposed in this study was capable to detect outbreaks of SARS, MERS, and early COVID-19 pandemics. The quick matching accuracies of confirmed infection cases were 89.1, 26.3–98%, and 82%, and the syndromic surveillance base would capture the Features of the remaining cases to ensure the overall detection accuracies. Based on the early COVID-19 data in Wuhan, this study estimated that the median time of the early COVID-19 cases from illness onset to local authorities' responses could be reduced to 7.0–10.0 days.Conclusions: This study offers a new solution to transfer traditional medical knowledge into structured data and form diagnosis rules, enables the representation of doctors' logistic thinking and the knowledge transmission among different users. The results of empirical studies demonstrate that by constantly inputting medical knowledge into the system, the proposed method will be capable to detect unknown diseases from existing ones and perform an early response to the initial outbreaks.

Adopted image matching techniques for aiding indoor navigation

Abstract In indoor navigation and visual odometry, cameras may be used as an aiding algorithm for inertial navigation. Fast and accurate image matching is an important task used in various applications in computer vision and visual odometry. In this research, the performances of all recently available detection, description, and matching techniques were compared. The detection techniques included HARRIS, GFTT, SIFT, SURF, STAR, FAST, ORB, MSER, Dense, and SimpleBlob. Feature description techniques included SIFT, SURF, ORB, and BRIEF. Finally, image matching techniques included BruteForce, BruteForce-L1, BruteForce-Hamming, BruteForce-HammingLUT, and FlannBased. The comparison was made between different kinds of geometric distortions and deformations, such as scaling, rotation, fish-eye distortion, and shearing. In this research, the lighting conditions and the shadowing effects were not taken into consideration. To perform this task, different types of transformations were manually applied to original images and computed with the matching evaluation parameters, such as the number of detected keypoints in images, the processing time, and the matching accuracies for each algorithm to show which algorithm was the best and most robust against these distortions. This work provided us with a perspective on and contributed to the field of image-based indoor navigation systems to recommend further research. The results showed that the ORB detector, the ORB descriptor, and either the BruteForce-Hamming or the BruteForce-HammingLUT matchers were favored to be used in indoor environments.

A low-dimensional binary-based descriptor for unknown satellite relative pose estimation

Satellite pose estimation is an essential task for on-orbit service. After obtaining sequential images of the unknown satellite, its relative pose can be estimated by capturing and matching keypoints in these images. Generally, existing point-based methods will produce numerous keypoints, represented by high-dimensional descriptors. Consequently, they are inappropriate to estimate the relative pose between the observer and the unknown target, due to the limited capacity of storage and computation of the on-board processor. On this basis, a low-dimensional binary-based descriptor is proposed, in conjunction with the use of a keypoint selection algorithm, to address the above-mentioned limitation. The developed algorithm is then used to select the keypoints from a series of points generated by the Harris detector. Each keypoint is represented by a novel 72-dimensional binary descriptor, the vicinity of which is divided into 12 sectors, and then each sector is mathematically encoded by six binary bits. The experimental results obtained via the presented approach demonstrate a good coincidence with that of the SURF and ORB methods, in terms of the keypoint matching and pose estimation accuracies, while it consumes less storage and has a faster matching speed.

Stability and similarity of the pediatric connectome as developmental measures

Patterns of functional connectivity are unique at the individual level, enabling test-retest matching algorithms to identify a subject from among a group using only their functional connectome. Recent findings show that accuracies of these algorithms in children increase with age. Relatedly, the persistence of functional connectivity (FC) patterns across tasks and rest also increases with age. This study investigated the hypothesis that within-subject stability and between-subject similarity of the whole-brain pediatric connectome are developmentally relevant outcomes. Using data from 210 help-seeking children and adolescents, ages 6–21 years (Healthy Brain Network Biobank), we computed whole-brain FC matrices for each participant during two different movies (MovieDM and MovieTP) and two runs of task-free rest (all from a single scan session) and fed these matrices to a test-retest matching algorithm. We replicated the finding that matching accuracies for children and youth (ages 6–21 years) are low (18–44%), and that cross-state and cross-movie accuracies were the lowest. Results also showed that parcellation resolution and the number of volumes used in each matrix affect fingerprinting accuracies. Next, we calculated three measures of whole-connectome stability for each subject: cross-rest (Rest1-Rest2), cross-state (MovieDM-Rest1), and cross-movie (MovieDM-MovieTP), and three measures of within-state between-subject connectome similarity for Rest1, MovieDM, and MovieTP. We show that stability and similarity were correlated, but that these measures were not related to age. A principal component analysis of these measures yielded two components that we used to test for brain-behavior correlations with IQ, general psychopathology, and social skills measures (n = 119). The first component was significantly correlated with the social skills measure (r=-0.26, p = 0.005). Post hoc correlations showed that the social skills measure correlated with both cross-rest stability (r=-0.29, p = 0.001) and with connectome similarity during MovieDM (r=-0.28, p = 0.002). These findings suggest that the stability and similarity of the whole-brain connectome relate to the development of social skills. We infer that the maturation of the functional connectome simultaneously achieves patterns of FC that are distinct at the individual subject level, that are shared across individuals, and that are persistent across states and across runs—features which presumably combine to optimize neural processing during development. Future longitudinal work could reveal the developmental trajectories of stability and similarity of the connectome.

Quantitative assessment of olfactory dysfunction accurately detects asymptomatic COVID-19 carriers.

BackgroundCOVID-19 threatens the global community because a large fraction of infected people are asymptomatic, yet can effectively transmit SARS-CoV-2. Finding and isolating these silent carriers is a crucial step in confining the spread of the disease. A sudden loss of the sense of smell has been self-reported by COVID-19 patients across different countries, consistent with expression of the molecular factors mediating SARS-CoV-2 uptake into human olfactory epithelial supporting cells. However, precise quantification of olfactory loss in asymptomatic COVID-19 carriers is missing to date.MethodsTo quantify olfactory functions in asymptomatic COVID-19 patients, we designed an olfactory-action meter that determines detectability indices at different odor concentrations and an olfactory matching accuracy score using monomolecular odors. The optimization of test parameters allowed us to reliably and accurately assess olfactory deficits in a patient within 20 minutes.FindingsMeasurement of detection indices at low concentrations revealed a 50% reduction in asymptomatic COVID-19 carriers. Further, patients with better detection scores showed significantly reduced olfactory matching accuracies compared to normal healthy subjects. Our quantification of olfactory loss, considering all parameters, identified 82% of the asymptomatic SARS-CoV-2 carriers with olfactory deficits. However, on subjective evaluation, only 15% of the patients noticed a compromised ability to smell.InterpretationCompromised olfactory fitness can serve as a strong basis for identifying asymptomatic COVID-19 patients. Detailed design specifications and protocols provided here should enable the development of a sensitive, fast, and economical screening strategy that can be administered to large populations to prevent the rapid spread of COVID-19.FundingThis work was supported by the DBT – Wellcome Trust India Alliance intermediate grant (IA/I/14/1/501,306 to N.A.) and UGC NET Fellowship (A.B.). All the funding sources played no roles in the study.

A window size selection network for stereo dense image matching

ABSTRACTStereo dense image matching normally refers to per-pixel correspondence search between stereo pairs. However, their matching results actually depend on matching window sizes. Large windows usually obtain robust matching results in weak-textured regions, but serious mismatches in depth/disparity (parallax in the epipolar space) inconsistency regions. Small windows compute accurate matching results in depth/disparity inconsistency regions, but it may contain high matching uncertainties in weak-textured regions. To improve matching accuracies, this letter focuses on adaptively selecting appropriate matching windows for each pixel. In general, we propose a window size selection network (WSSN) with the basic assumption that appropriate window sizes are related to image textures and depth/disparity variations. WSSN firstly extracts both image texture features and disparity features by convolutional neural network and then utilizes the fully connected layers to conduct optimal window size selection. Experiments on an aerial image dataset show that our proposed method is capable of selecting appropriate matching windows for each pixel. It achieved the highest matching accuracy when compared with the matching results of a series of fixed matching windows and a state-of-the-art textured based window selection method.

Quantitative Assessment of Olfactory Dysfunction Accurately Detects Asymptomatic COVID-19 Carriers

BACKGROUND: COVID-19 threatens the global community, because a large fraction of infected people are asymptomatic, yet can effectively transmit SARS-CoV-2. Finding and isolating these silent carriers is a crucial step in confining the spread of the disease. A sudden loss of the sense of smell (anosmia) has been self-reported by COVID-19 patients from different countries, consistent with the expression of the molecular factors mediating SARS-CoV-2 uptake into human olfactory epithelial supporting cells. However, a precise quantification of olfactory loss in asymptomatic COVID-19 carriers is missing to date. METHODS: To quantitatively assess olfactory functions in asymptomatic COVID-19 patients, we designed and custom-built an olfactory-action meter that precisely measures detectability indices at different odor concentrations and an olfactory matching accuracy score with an array of monomolecular odors. Test results with normal healthy subjects served to optimize parameters such that the test can be executed in less than 20 minutes without compromising accuracy. FINDINGS: Asymptomatic COVID-19 patients showed severe olfactory deficits with all parameters tested. Measuring the olfactory detection indices at low concentrations revealed olfactory deficits in 81% of patients. Patients with better detection scores showed significantly reduced olfactory matching accuracies compared to normal healthy subjects. Our quantification of olfactory loss, considering detection indices and matching performance, identifies 82% of the asymptomatic SARS-CoV-2 carriers, despite the fact that subjectively, only a few of the patients noticed a compromised ability to smell. INTERPRETATION: Our findings from systematic analysis reveal that olfactory fitness can serve as a strong basis for identifying asymptomatic COVID-19 patients. Together, these results indicate olfactory sensory as well as cognitive deficits caused by SARS-CoV-2 infection. Detailed design specifications and protocols provided here should enable the development of a sensitive, fast and economical screening strategy that can be administered to large populations to prevent the rapid spread of COVID-19. FUNDING STATEMENT: This work was supported by the Wellcome Trust – DBT India Alliance intermediate grant (IA/I/14/1/501306 to N.A.) and UGC NET Fellowship (A.B.). DECLARATION OF INTERESTS: The authors declare that there is no conflict of interest. ETHICS APPROVAL STATEMENT: All experimental procedures and protocols used in this study were approved by the ethics committees of IISER-Pune and BJGMC & SGH, Pune.

Finer Resolution Estimation and Mapping of Mangrove Biomass Using UAV LiDAR and WorldView-2 Data

To estimate mangrove biomass at finer resolution, such as at an individual tree or clump level, there is a crucial need for elaborate management of mangrove forest in a local area. However, there are few studies estimating mangrove biomass at finer resolution partly due to the limitation of remote sensing data. Using WorldView-2 imagery, unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data, and field survey datasets, we proposed a novel method for the estimation of mangrove aboveground biomass (AGB) at individual tree level, i.e., individual tree-based inference method. The performance of the individual tree-based inference method was compared with the grid-based random forest model method, which directly links the field samples with the UAV LiDAR metrics. We discussed the feasibility of the individual tree-based inference method and the influence of diameter at breast height (DBH) on individual segmentation accuracy. The results indicated that (1) The overall classification accuracy of six mangrove species at individual tree level was 86.08%. (2) The position and number matching accuracies of individual tree segmentation were 87.43% and 51.11%, respectively. The number matching accuracy of individual tree segmentation was relatively satisfying within 8 cm ≤ DBH ≤ 30 cm. (3) The individual tree-based inference method produced lower accuracy than the grid-based RF model method with R2 of 0.49 vs. 0.67 and RMSE of 48.42 Mg ha−1 vs. 38.95 Mg ha−1. However, the individual tree-based inference method can show more detail of spatial distribution of mangrove AGB. The resultant AGB maps of this method are more beneficial to the fine and differentiated management of mangrove forests.

Automated Matching of Multi-Scale Building Data Based on Relaxation Labelling and Pattern Combinations

With the increasingly urgent demand for map conflation and timely data updating, data matching has become a crucial issue in big data and the GIS community. However, non-rigid deviation, shape homogenization, and uncertain scale differences occur in crowdsourced and official building data, causing challenges in conflating heterogeneous building datasets from different sources and scales. This paper thus proposes an automated building data matching method based on relaxation labelling and pattern combinations. The proposed method first detects all possible matching objects and pattern combinations to create a matching table, and calculates four geo-similarities for each candidate-matching pair to initialize a probabilistic matching matrix. After that, the contextual information of neighboring candidate-matching pairs is explored to heuristically amend the geo-similarity-based matching matrix for achieving a contextual matching consistency. Three case studies are conducted to illustrate that the proposed method obtains high matching accuracies and correctly identifies various 1:1, 1:M, and M:N matching. This indicates the pattern-level relaxation labelling matching method can efficiently overcome the problems of shape homogeneity and non-rigid deviation, and meanwhile has weak sensitivity to uncertain scale differences, providing a functional solution for conflating crowdsourced and official building data.

Coseismic displacements of the 14 November 2016 &amp;lt;i&amp;gt;M&amp;lt;/i&amp;gt;&amp;lt;sub&amp;gt;w&amp;lt;/sub&amp;gt; 7.8 Kaikoura, New Zealand, earthquake using the Planet optical cubesat constellation

Abstract. Satellite measurements of coseismic displacements are typically based on synthetic aperture radar (SAR) interferometry or amplitude tracking, or based on optical data such as from Landsat, Sentinel-2, SPOT, ASTER, very high-resolution satellites, or air photos. Here, we evaluate a new class of optical satellite images for this purpose – data from cubesats. More specific, we investigate the PlanetScope cubesat constellation for horizontal surface displacements by the 14 November 2016 Mw 7.8 Kaikoura, New Zealand, earthquake. Single PlanetScope scenes are 2–4 m-resolution visible and near-infrared frame images of approximately 20–30 km × 9–15 km in size, acquired in continuous sequence along an orbit of approximately 375–475 km height. From single scenes or mosaics from before and after the earthquake, we observe surface displacements of up to almost 10 m and estimate matching accuracies from PlanetScope data between ±0.25 and ±0.7 pixels (∼ ±0.75 to ±2.0 m), depending on time interval and image product type. Thereby, the most optimistic accuracy estimate of ±0.25 pixels might actually be typical for the final, sun-synchronous, and near-polar-orbit PlanetScope constellation when unrectified data are used for matching. This accuracy, the daily revisit anticipated for the PlanetScope constellation for the entire land surface of Earth, and a number of other features, together offer new possibilities for investigating coseismic and other Earth surface displacements and managing related hazards and disasters, and complement existing SAR and optical methods. For comparison and for a better regional overview we also match the coseismic displacements by the 2016 Kaikoura earthquake using Landsat 8 and Sentinel-2 data.

Limited Top-Down Influence from Recognition to Same-Different Matching of Chinese Characters.

We investigated the extent to which recognition of Chinese characters influenced same-different matching performance that did not require recognition. In each experimental trial, two partially occluded characters were shown sequentially, and participants decided whether or not they were the same. The two characters were either both upright or both inverted and mirror-reflected. The participants’ Chinese reading fluency spanned the full range, from not knowing any characters to native speakers. The participants who could recognize some characters (defined as readers) were subsequently tested with character recognition in a naming task. Interestingly, although the readers’ recognition accuracies well correlated with their years of Chinese language schooling, they were uncorrelated with the matching accuracies in the same-different task with upright characters. The only indication of top-down influence was the readers’ higher accuracy in matching upright than inverted and reflected characters. However, the magnitude of this effect was small, to the extent that the average same-different accuracies were comparable for readers and non-readers alike. This small effect was further confirmed with native speakers in China, who should give rise to the largest possible effect. We conclude that top-down influence from character recognition was present but very limited, at least with the task and stimuli used.

Future dedicated Venus-SGG flight mission: Accuracy assessment and performance analysis

This study concentrates principally on the systematic requirements analysis for the future dedicated Venus-SGG (spacecraft gravity gradiometry) flight mission in China in respect of the matching measurement accuracies of the spacecraft-based scientific instruments and the orbital parameters of the spacecraft. Firstly, we created and proved the single and combined analytical error models of the cumulative Venusian geoid height influenced by the gravity gradient error of the spacecraft-borne atom-interferometer gravity gradiometer (AIGG) and the orbital position error and orbital velocity error tracked by the deep space network (DSN) on the Earth station. Secondly, the ultra-high-precision spacecraft-borne AIGG is propitious to making a significant contribution to globally mapping the Venusian gravitational field and modeling the geoid with unprecedented accuracy and spatial resolution through weighing the advantages and disadvantages among the electrostatically suspended gravity gradiometer, the superconducting gravity gradiometer and the AIGG. Finally, the future dedicated Venus-SGG spacecraft had better adopt the optimal matching accuracy indices consisting of 3×10−13/s2 in gravity gradient, 10m in orbital position and 8×10−4m/s in orbital velocity and the preferred orbital parameters comprising an orbital altitude of 300±50km, an observation time of 60months and a sampling interval of 1s.

A Computational Evaluation of Sentence Processing Deficits in Aphasia.

Individuals with agrammatic Broca's aphasia experience difficulty when processing reversible non-canonical sentences. Different accounts have been proposed to explain this phenomenon. The Trace Deletion account (Grodzinsky, 1995, 2000, 2006) attributes this deficit to an impairment in syntactic representations, whereas others (e.g., Caplan, Waters, Dede, Michaud, & Reddy, 2007; Haarmann, Just, & Carpenter, 1997) propose that the underlying structural representations are unimpaired, but sentence comprehension is affected by processing deficits, such as slow lexical activation, reduction in memory resources, slowed processing and/or intermittent deficiency, among others. We test the claims of two processing accounts, slowed processing and intermittent deficiency, and two versions of the Trace Deletion Hypothesis (TDH), in a computational framework for sentence processing (Lewis & Vasishth, 2005) implemented in ACT-R (Anderson, Byrne, Douglass, Lebiere, & Qin, 2004). The assumption of slowed processing is operationalized as slow procedural memory, so that each processing action is performed slower than normal, and intermittent deficiency as extra noise in the procedural memory, so that the parsing steps are more noisy than normal. We operationalize the TDH as an absence of trace information in the parse tree. To test the predictions of the models implementing these theories, we use the data from a German sentence-picture matching study reported in Hanne, Sekerina, Vasishth, Burchert, and De Bleser (2011). The data consist of offline (sentence-picture matching accuracies and response times) and online (eye fixation proportions) measures. From among the models considered, the model assuming that both slowed processing and intermittent deficiency are present emerges as the best model of sentence processing difficulty in aphasia. The modeling of individual differences suggests that, if we assume that patients have both slowed processing and intermittent deficiency, they have them in differing degrees.