Correct Matches Research Articles

Superpixel-Based Local Features for Image Matching

Image matching is the research basis of many computer vision problems, such as intelligent driving, object recognition and structure from motion. However, the traditional feature-based image-matching results are usually very sparse and unevenly distributed for wide baseline or weakly textured images. Implementing an efficient and robust image-matching technology is a challenging task. To solve these problems, we propose an efficient extractor and binary descriptor based on superpixels and a modified binary robust independent elementary features (BRIEF) descriptor called FSRB. FSRB can improve the computational efficiency, number of matches, feature distribution and robustness of feature-based image matching. In theory, FSRB is rotation-, scale-, affine-, distorted-, and intensity-invariant. A comprehensive performance evaluation of FSRB is performed. The experimental results show that our method can effectively obtain many matches for different types of images. Compared with state-of-the-art algorithms, our method performed very well in terms of the number of correct matches (which increased by 2-5 times), time consumption, matching accuracy, matching success rate and feature repetition rate. In addition, our method is applied to sparse 3D reconstruction of multiview images, and satisfactory results are obtained.

Comparative characteristics of conventional visual methods and instrumental digital methods for tooth color determination

Shade matching is a challenging step and the major objective in esthetic and restorative dentistry. On many occasions, the production of the accurate target color will not be achieved due to errors in the registration of the target color, or errors in composing the appropriate color of the restoration in the dental laboratory. Thus, the accurate and correct shade matching of tooth and restoration is one of the most important quality criteria for the patient and the key to successful results for the dentist.
 Conclusion: Spectrophotometers, colorimeters, intraoral scanners, and imaging systems are useful and relevant tools for tooth color measurement and analysis, and for the quality control of color reproduction. The combination of visual and instrumental methods of tooth color determination will complement each other, leading toward predictable esthetic results.

MIFF

Human Mobility Extraction with cellular Signaling Data (SD) is essential for human mobility understanding, epidemic control, and wireless network planning. SD log the detailed interactions between cellphones and cellular towers, but suffer from a spatio-temporal uncertainty problem due to cellular network tower-level load rebalancing (switching users between towers) and cellphone usage activities. To date, most models focus on utilizing better data like RSSI or GPS, do not directly address uncertainty. To address the SD uncertainty issue, we utilize two insights based on (i) individuals' regular mobility patterns and (ii) common co-movement mobility patterns between cellphone users as suggested by fundamental human mobility nature. Accordingly, we design a Multi-Information Fusion Framework (MIFF) to assist in extracting road-level human mobility based on cell-tower level traces. To evaluate the effectiveness of MIFF, we conduct experiments on one-month SD obtained from a cellular service operator, and SD manually collected by handheld mobile devices in two cities in China. Four transportation modes, namely railways, cars, buses, and bikes are evaluated. Experimental results show that with MIFF, our road-level trajectory extraction accuracy can be improved by 5.0% on Point correct matching index and 68.5% on Geographic Error on average.

Robust line feature matching based on pair-wise geometric constraints and matching redundancy

This paper presents a novel method for matching line segments in images based on pair-wise geometric constraints and matching redundancy. In this study, pairs of line segments satisfying angle and distance constraints are used as matching primitives. To ensure that each extracted line segment is paired with another line segment, the search region of each line segment is gradually grown until it is paired. Initial pair-to-pair correspondences between two images are established using four pair-wise constraints; next, line-to-line correspondences are obtained. To effectively solve the matching conflict in the results, a method of recording the result of line pair matching based on a double-layer matrix is proposed. Based on the double-layer matrix, an effective checking method for the line matching results based on the collinearity constraint and matching redundancy is presented. It fully utilizes redundancy information and considers the collinearity of fragmented line segments. Further, it can effectively separate correct and incorrect matches from the one-to-many, many-to-one, and many-to-many matching results. The proposed method was tested on 12 image pairs from a benchmark of matched-lines, and compared with other state-of-the-art methods. The results demonstrate the superiority of the proposed method due to its higher accuracy and greater recall in challenging cases.

Automatic Matching of Multispectral Images Based on Nonlinear Diffusion of Image Structures

Imaging with different spectrums often leads to significant nonlinear differences in the intensity, which brings a great challenge to the automatic matching of multispectral images. Considering that there are often limitations to only using intensity information, this article studies multispectral image matching based on the structure consistency. First, an extended log-Gabor filter is constructed to build phase congruency maps, which encodes the structure information and provides rich and robust features. Then, a nonlinear diffusion-based algorithm is developed to detect the salient feature points on the phase congruency map, which are expected to be illumination and contrast invariant. Finally, the structure descriptors are built according to the orientation of the maximum log-Gabor filter responses, and the image matching is achieved by computing the correspondence. Extensive experiments are carried out on various multispectral image datasets. The results show that the proposed method holds a stable performance over the nonlinear intensity variance across spectrums, and outperforms the comparison methods in terms of the number of correct matches and the matching precision.

A local feature extraction method for UAV-based image registration based on virtual line descriptors

Image local feature extraction is extensively utilized in the field of photogrammetry where the spatial distribution of features is important in high-quality image matching, particularly in high-resolution unmanned aerial vehicle (UAV) image registration. Presently, the spatial distribution problems are considered in some local feature extraction methods, though these methods are designed for point descriptors. Line descriptors are more robust to repetitive patterns compared to point descriptors and have attracted extensive attention in recent years. Hence, a feature extraction method is designed in this paper for line descriptors based on the K-connected virtual line descriptors matching method. Using the four measures, the quality of local features is quantified, and a regular gridding strategy based on the quality of local features is applied in the feature selection procedure. The proposed feature extraction method was successfully applied to match various simulated and real UAV-based images. Based on the experimental results using real images, it is indicated that two evaluation criteria, namely the spatial distribution quality of features and the number of correct matches, are improved to at least 12% and 15%, respectively, for verifying the capability of the proposed method to enhance matching performance.

Accuracy and repeatability of different intraoral scanners on shade determination

AbstractObjectiveTo evaluate the accuracy and repeatability of different intraoral scanners on shade determination.Materials and methodsTen different shades of Vita Mark II blocks were used. A disc‐shape specimen (10 mm in diameter and 1 mm thick) per ceramic block was fabricated. Ten color measurements per specimen were performed by each instrument (Vita Easyshade V [control], 3shape Trios, Cerec Omnicam, Cerec Primescan) and recorded in Vita Classic color system. The number of correct shade match per instrument for each shade was recorded. Instrumental accuracy was compared using Cochran Q test and repeatability was analyzed using Cronbach's alpha.ResultsThere was a significant difference in the instrumental accuracy for shade determination (p < 0.001). There was no statistical difference between the Easyshade V (78%) and the 3Shape Trios (66%) (p > 0.05), with the latter being similar to the other scanners Primescan(63%) and Omnicam (57%) (p > 0.05). No significant difference was found (p > 0.05) when different shades were evaluated by the same instrument. Similar repeatability was found for the different devices, ranging from 44.3% for Easyshade to 51.9% for Omnicam.ConclusionThe evaluated instruments showed less than expected repeatability and accuracy on measuring different dental shades. Therefore, caution should be exercised when using instrumental shade determination, which should be accompanied by experienced human visual assessment.Clinical significanceThe outcome of this study might help clinicians evaluate the performance of intraoral scanners as a shade matching tool.

THE ADULT-CHILD DYAD AS A PROBLEM-SOLVING SYSTEM: AN EXPERIMENT WITH ONE BILINGUAL INDONESIAN CHILD

<p>This study tries to reveal how the transition from interpsychological to intrapsychological functioning happens in an adult-child dyad’s activity involving a 3.6 year-old-child and the child’s mother. The study explores how the adult and child divided up the strategic responsibilities for carrying out a problem-solving task of completing a puzzle. The experiment, which took place in the child’s living room and lasted for 48 minutes, was videotaped and observed while all utterances by the adult and child were transcribed verbatim. Coding was done in order to note information showing the gaze behavior, interventions from the adult, and episodes for each correct match. Thematic analysis was chosen as the method to analyze the transcripts in order to find meaningful patterns. The study found that the adult’s intervention is important in the early stage of the joint-problem activity while, when the child takes more responsibility and develops some strategies as the child becomes more self-regulated, the adult’s role in scaffolding decreases. There is a transition from the interpsychological to intrapsychological functioning in the adult-child dyad while gazes towards the played cards outweigh the role of gazes towards other objects.</p>

Heterogeneous remote-sensing image matching method based on deep learning

In this paper, a deep learning matching method is proposed to address the difficulty in matching heterogeneous remote sensing images, which is caused by their differences in imaging modes, time phases, and resolutions. A heterogeneous image is inputted into the convolutional neural network (CNN) to extract deep features and, then, a graph neural network (GNN) is used for matching. Finally, correct matching points are retained while ensuring the effective elimination of mismatches. The algorithm adopted in this study was tested using multiple sets of heterogeneous remote-sensing images and compared with D2-Net+NN+RANSAC and Superpoint+SuperGlue algorithms. The results show that the algorithm used in this study possesses strong adaptability and robustness and is an optimal algorithm for the robust matching of remote-sensing images with different sources and large differences.

Failure prediction in electrical connector assembly: a case in automotive assembly process

Purpose Electrical defects cover an important part of assembly defects and strongly affect the vehicle system performance. Almost 40% of assembly defects are classified as human errors and electrical connection failures represent a significant part of them. Humans still remain a cost-effective solution for the flexible manufacturing systems with increasing product complexity. So, understanding human behaviors is still a challenging task. The purpose of this study is to define, prioritize and validate the critical factors for the complexity of electrical connector plugin process. Design/methodology/approach The critical variables were defined by the expert team members. The required number of measurements and variables were revised resulting preliminary analysis of binary logistic regression. After the revision of measurement plan, the list of critical input variables and the mathematical model were defined. The model has been validated by the fitted values of the residuals (FITS analysis). Findings To the best of the authors’ knowledge, this is one of the limited studies, which defines the critical factors for electrical connection process complexity. Female connector harness length, connector width/height/length differences, operator sense of correct connector matching and ergonomy were defined as the factors with the highest impact on the failure occurrence. The obtained regression equation strongly correlates the failure probability. Practical implications The obtained mathematical model can be used in new model development processes both for the product and assembly process design (ergonomy, accessibility and lay-out). Originality/value The obtained risk factors demonstrated a strong correlation with assembly process complexity and failure rates. The output of this study would be used as an important guide for process (assembly line ergonomy, accessibility and lay-out) and product design in new model development and assembly ramp-up phases.

Remote Sensing Image Registration Based on Local Affine Constraint With Circle Descriptor

Many methods have been developed to improve the performance of image registration. In this letter, we introduce a novel method based on a local affine constraint for remote sensing image registration, which can be widely used in image processing and pattern recognition. Our algorithm has three components. First, we exploit the scale invariant feature transform (SIFT) method to extract feature points and calculate the gradient magnitude to establish feature descriptors with a circular instead of square neighborhood. Second, an initial matching is implemented by the nearest neighbor distance ratio (NNDR) and the fast sample consensus (FSC) algorithm. Finally, fine registration is established using more correct matches obtained by the local affine transformation circular region search algorithm. Experimental results show that the proposed method achieves subpixel accuracy. In addition, both the correct matching rate and registration demonstrate the effectiveness and efficiency of our method.

USE OF BITEMARKS FOR HUMAN IDENTIFICATION BY USING INTER-CANINE DISTANCE AND MESIO-DISTAL WIDTH OF MAXILLARY INCISORS.

Aim: to determine the use of bite marks for human identification by using inter- canine distance and mesio-distal width of maxillary incisors. Introduction: Odontogenic evidence is considered to be the third most precise method of identification of human in forensic sciences. Bite-marks on the body are intentionally made, those found on food particles are usually impractically left by the offender at the scene of crime, these marks are of great importance in identification of the offender. The present study was aimed to determine the use of bitemarks for human identification by using inter-canine distance and mesio-distal width of the maxillary incisors. Materials and Methods: 40 consenting volunteers were included in the study. With the help of autoclaved Vernier calliper, the inter-canine distance and mesio-distal width of maxillary incisors were measured and bite on chocolate (CADBURY 5Star) was taken. Using Vernier calliper, Inter-canine distance and Mesio-distal width (2 maxillary central and 2 lateral incisors), were measured from the bite mark. Both measurements obtained were coded and matching was done by two doubly blinded examiners. Results: The probability of correct identification on the basis of IC distance and MD width obtained from bite-marks is 68.35%. The average proportion of correct matching is 0.78 for IC distance measurement and 0.46 for MD width. Conclusion: Metric method can be used for human identification by using bite-mark. IC distance is more reliable and efficient in human identification than MD width.

A Fast Selection Method of Landmarks for Terrain Matching Navigation

Under fixed imaging conditions, the landmark selection method based feature traversal analysis has high computational complexity. The hierarchical statistical significance detection method uses global statistical information for feature analysis to overcome the computational complexity problem caused by feature traversal analysis. The frequency domain de-correlation method can remove repeat mode in the image by adaptive Gaussian filtering on the amplitude-frequency characteristics. In this paper, combined the hierarchical statistical saliency detection method with the frequency domain de-correlation method, a fast landmark selection algorithm based on saliency analysis is proposed. Based on the proposed algorithm, the automatic landmark selection architecture for terrain matching navigation was constructed. The selection of landmark points was carried out in the Qin-ling Mountains and the Guangdong and Guangxi hills. The results show that compared with the feature or pixel-based landmark selection method, the landmark selection efficiency of the proposed method is improved by 2 to 3 orders of magnitude. The correct matching rate of candidate landmarks selected in the Qinling Mountains and the Guangdong and Guangxi hills are 73.9% and 88.3% respectively.

Nucleotide-Selective Templated Self-Assembly of Nanoreactors under Dissipative Conditions.

Nature adopts complex chemical networks to finely tune biochemical processes. Indeed, small biomolecules play a key role in regulating the flux of metabolic pathways. Chemistry, which was traditionally focused on reactions in simple mixtures, is dedicating increasing attention to the network reactivity of highly complex synthetic systems, able to display new kinetic phenomena. Herein, we show that the addition of monophosphate nucleosides to a mixture of amphiphiles and reagents leads to the selective templated formation of self-assembled structures, which can accelerate a reaction between two hydrophobic reactants. The correct matching between nucleotide and the amphiphile head group is fundamental for the selective formation of the assemblies and for the consequent up-regulation of the chemical reaction. Transient stability of the nanoreactors is obtained under dissipative conditions, driven by enzymatic dephosphorylation of the templating nucleotides. These results show that small molecules can play a key role in modulating network reactivity, by selectively templating self-assembled structures that are able to up-regulate chemical reaction pathways.

Nucleotide‐Selective Templated Self‐Assembly of Nanoreactors under Dissipative Conditions

AbstractNature adopts complex chemical networks to finely tune biochemical processes. Indeed, small biomolecules play a key role in regulating the flux of metabolic pathways. Chemistry, which was traditionally focused on reactions in simple mixtures, is dedicating increasing attention to the network reactivity of highly complex synthetic systems, able to display new kinetic phenomena. Herein, we show that the addition of monophosphate nucleosides to a mixture of amphiphiles and reagents leads to the selective templated formation of self‐assembled structures, which can accelerate a reaction between two hydrophobic reactants. The correct matching between nucleotide and the amphiphile head group is fundamental for the selective formation of the assemblies and for the consequent up‐regulation of the chemical reaction. Transient stability of the nanoreactors is obtained under dissipative conditions, driven by enzymatic dephosphorylation of the templating nucleotides. These results show that small molecules can play a key role in modulating network reactivity, by selectively templating self‐assembled structures that are able to up‐regulate chemical reaction pathways.

Modelling the expected probability of correct assignment under uncertainty

When making important decisions such as choosing health insurance or a school, people are often uncertain what levels of attributes will suit their true preference. After choice, they might realize that their uncertainty resulted in a mismatch: choosing a sub-optimal alternative, while another available alternative better matches their needs. We study here the overall impact, from a central planner’s perspective, of decisions under such uncertainty. We use the representation of Voronoi tessellations to locate all individuals and alternatives in an attribute space. We provide an expression for the probability of correct match, and calculate, analytically and numerically, the average percentage of matches. We test dependence on the level of uncertainty and location. We find that the overall mismatch is considerable even for low uncertainty—a possible concern for policy makers. We further explore a commonly used practice—allocating service representatives to assist individuals’ decisions. We show that within a given budget and uncertainty level, the effective allocation is for individuals who are close to the boundary between several Voronoi cells, but are not right on the boundary.

Image Stitching using AKAZE Features

Accelerated KAZE (AKAZE) is a multi-scale 2D feature detection and description algorithm in nonlinear scale spaces proposed recently. This paper presents an image stitching algorithm which uses a feature detection and description algorithm; AKAZE and an image blending algorithm; weighted average blending. The whole process is divided into the following steps: First of all, detect feature points in the image and then get feature descriptors of detected points using AKAZE. Next, obtain corresponding matching pairs by using K-NN (K nearest neighbors) algorithm and remove the false matched points by MSAC (M-estimator SAmple Consensus) algorithm. MSAC is a variant of the RANSAC (Random Sample Consensus) algorithm and more accurate than RANSAC. Thereafter, calculate the homography matrix from correct matches. At last, blend the images by using weighted average blending algorithm. Comparison of proposed AKAZE-based algorithm with SIFT-, SURF- and ORB-based algorithms is also presented. According to the experiments, the proposed AKAZE-based image stitching algorithm minimizes stitching seam and generates a perfect stitched image, and also this algorithm is faster than previous algorithms.

Recognizing individual concert halls is difficult when listening to the acoustics with different musical passages.

This article presents a listening experiment in which the listeners' task was to recognize the acoustics of a seat in a specific concert hall. Stimuli included two short passages extracted from a Beethoven symphony and samples of a solo violin auralized to four real concert halls. In each trial, listeners were presented with a reference and four alternatives with one correct match. In the "same" condition, the reference and the alternatives contained the same source sound. In the "different" condition, the source sounds were different musical passages but always of the same sound type, that is, symphonic music or solo violin. Results show that on average listeners could recognize the halls when the task was performed with the same source sound but had difficulty when listening to different sounds. The patterns of erroneous responses exhibited confusion between particular hall pairs and corresponded well to the values and just-noticeable-differences of the traditional objective room acoustic parameters. While the type of music is previously well known to influence the perception of concert hall acoustics, the present results indicate that even minor changes in the source sound content may have a strong impact on the ability to recognize the acoustics of individual halls.

Study on the Influence of Image Noise on Monocular Feature-Based Visual SLAM Based on FFDNet.

Noise appears in images captured by real cameras. This paper studies the influence of noise on monocular feature-based visual Simultaneous Localization and Mapping (SLAM). First, an open-source synthetic dataset with different noise levels is introduced in this paper. Then the images in the dataset are denoised using the Fast and Flexible Denoising convolutional neural Network (FFDNet); the matching performances of Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and Oriented FAST and Rotated BRIEF (ORB) which are commonly used in feature-based SLAM are analyzed in comparison and the results show that ORB has a higher correct matching rate than that of SIFT and SURF, the denoised images have a higher correct matching rate than noisy images. Next, the Absolute Trajectory Error (ATE) of noisy and denoised sequences are evaluated on ORB-SLAM2 and the results show that the denoised sequences perform better than the noisy sequences at any noise level. Finally, the completely clean sequence in the dataset and the sequences in the KITTI dataset are denoised and compared with the original sequence through comprehensive experiments. For the clean sequence, the Root-Mean-Square Error (RMSE) of ATE after denoising has decreased by 16.75%; for KITTI sequences, 7 out of 10 sequences have lower RMSE than the original sequences. The results show that the denoised image can achieve higher accuracy in the monocular feature-based visual SLAM under certain conditions.

A-236 Concurrent Validity of the Paper-Match in a Diverse Population

Abstract Introduction Symbol and digit substitution tasks are key instruments in the evaluation of attention, speed of processing, executive functioning, and cognitive dysfunction. The Match is an electronic (app-based) substitution task developed as part of the University of California San Francisco Brain Health Assessment that has been used to demonstrate cognitive impairment. We describe the paper-based oral symbol/number substitution task (Paper-Match), which was developed as part of the 5-Cog battery to assess for cognitive impairment including dementia and demonstrate it’s use in an urban, multi-ethnic, socioeconomically disadvantaged primary care population in Bronx NY. Method 109 participants, mean age 72.61 SD 6.42 and education 11.70 SD 3.82 years, 81% female, 48.6% Hispanic/Latino and 47.7% African American seen as part of 5-Cog. Participants (36.7% tested in Spanish) completed the Paper-Match (scored based on the number of correct matches in 90 seconds) and a comprehensive neuropsychological battery. Descriptive and correlational analysis were conducted. Results Mean Paper-Match score was 38.24 SD 11.83 items correct (range 4–51). Paper-Match performance was highly correlated with performance on the Symbol Digit Modalities Test (r = .819, p < .001) and moderately correlated with performance on the Montreal Cognitive Assessment (MOCA; r = .604, p < .001). Years of education and primary language impacted performances. Conclusion The results of this study provide the initial description and validation of the Paper-Match an oral symbol digit substitution task and its use in a diverse population of older primary care patients. Further research on the psychometric properties and construct validity of the Paper-Match are needed.