Correspondence Problem Research Articles

Rights and obligations of a citizen of the Russian Federation: the problem of correspondence (constitutional and legal aspect)

Rights and obligations of a citizen of the Russian Federation: the problem of correspondence (constitutional and legal aspect)

Discussion of Correspondence and Regularity Problem in Deriving and Applying Distribution Fraction Calculations

Discussion of Correspondence and Regularity Problem in Deriving and Applying Distribution Fraction Calculations

Spiking Cooperative Network Implemented on FPGA for Real-Time Event-Based Stereo System

A hardware-efficient implementation of a spiking cooperative network (SCN) for a real-time event-based stereo correspondence system is presented. While fully utilizing the advantage of event data, the proposed SCN design significantly reduces the amount of hardware resources by utilizing distinct properties of the SCN, such as the repeatability of synaptic connections and operations, through physical constraints. A stereo system consisting of a field-programmable gate array (FPGA) and a pair of dynamic vision sensors (DVSs) is implemented to demonstrate the SCN design. Stereo livestreamed event data are generated from the DVSs, and the SCN is implemented on an FPGA chip to process the event data. The SCN system has four cores, each comprising an array of 32 Coincidence-Disparity units that calculate the 32-level disparity in a semi-parallel manner. The system performance was evaluated experimentally to estimate the depth of objects moving at different speeds. A rotating drum with a diameter of 8 cm was used in the test. The median relative error of the estimated depth at a rotation speed of 16.7 Hz ranged from 7.3% to 10.6%.

Improving Word Alignment by Adding Gromov-Wasserstein into Attention Neural Network

Statistical machine translation systems usually break the translation task into two or more subtasks and an important one is finding word alignments over a parallel sentence bilingual corpus. We address the problem of introducing word alignment for language pairs by developing a novel neural network model that can applied to other generative alignment models. We use Multi-layer attention model and multi-layer model with multi-head-attention mechanism on each layer provides superior translation quality. It can be trained on bilingual data without relying on word alignment. In this paper, we cast the correspondence problem directly as an optimal distance problem. We use the Gromov-Wasserstein distance to calculated how similarities between word pairs are related across languages. The resulting alignments dramatically outperform the GIZA++ and FastAlign approach, these alignments are comparable on public data sets.

PELATIHAN PEMBUATAN SURAT LAMARAN KERJA DAN KARTU NAMA SISWA MENGGUNAKAN MAIL MERGE DI MADRASAH ALIYAH SWASTA YPI BATANG KUIS

This community service activity is one of the Tri Dharma Perguruan Tinggi by providing learning to students at Madrasah Aliyah Swasta YPI Batang Kuis in training on making job applications and student business cards using Mail Merge. Mail Merge is a facility in Ms. Word that is used to deal with correspondence problems such as job application letters, invitation letters, sales promotion letters, business cards, and various other types of document creation, which can be sent to multiple recipients/destinations using the information stored in the list, database, or spreadsheet. The advantage of using mail merge is that it makes it easier for the letter/document maker because it only creates one type of letter/document that can be sent to several different recipients. Through this training, Madrasah Aliyah Swasta YPI Batang Kuis students are expected to be able to take advantage of the Mail Merge feature to make job application letters and student business cards more easily and more efficiently as a support in the teaching and learning process.

New Stereo Vision Algorithm Composition Using Weighted Adaptive Histogram Equalization and Gamma Correction

This work presents the composition of a new algorithm for a stereo vision system to acquire accurate depth measurement from stereo correspondence. Stereo correspondence produced by matching is commonly affected by image noise such as illumination variation, blurry boundaries, and radiometric differences. The proposed algorithm introduces a pre-processing step based on the combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Gamma Correction Weighted Distribution (AGCWD) with a guided filter (GF). The cost value of the pre-processing step is determined in the matching cost step using the census transform (CT), which is followed by aggregation using the fixed-window and GF technique. A winner-takes-all (WTA) approach is employed to select the minimum disparity map value and final refinement using left-right consistency checking (LR) along with a weighted median filter (WMF) to remove outliers. The algorithm improved the accuracy 31.65% for all pixel errors and 23.35% for pixel errors in nonoccluded regions compared to several established algorithms on a Middlebury dataset.

ResDepth: A deep residual prior for 3D reconstruction from high-resolution satellite images

Modern optical satellite sensors enable high-resolution stereo reconstruction from space. But the challenging imaging conditions when observing the Earth from space push stereo matching to its limits. In practice, the resulting digital surface models (DSMs) are fairly noisy and often do not attain the accuracy needed for high-resolution applications such as 3D city modeling. Arguably, stereo correspondence based on low-level image similarity is insufficient and should be complemented with a priori knowledge about the expected surface geometry beyond basic local smoothness. To that end, we introduce ResDepth, a convolutional neural network that learns such an expressive geometric prior from example data. ResDepth refines an initial, raw stereo DSM while conditioning the refinement on the images. I.e., it acts as a smart, learned post-processing filter and can seamlessly complement any stereo matching pipeline. In a series of experiments, we find that the proposed method consistently improves stereo DSMs both quantitatively and qualitatively. We show that the prior encoded in the network weights captures meaningful geometric characteristics of urban design, which also generalize across different districts and even from one city to another. Moreover, we demonstrate that, by training on a variety of stereo pairs, ResDepth can acquire a sufficient degree of invariance against variations in imaging conditions and acquisition geometry.

Simultaneous estimation of projector and camera poses for multiple oneshot scan using pixel-wise correspondences estimated by U-Nets and GCN

ABSTRACT Dense and accurate 3D shape acquisition of objects by active-stereo technique has been an important research topic and intensively researched. One of the promising fields for active-stereo techniques is medical applications, such as 3D endoscope systems. In such systems, since a sensor is dynamically moved during the operation, single-frame shape reconstruction, a.k.a. oneshot scan, is necessary. For oneshot scan, there are several open problems, such as low resolution because of spatial coding, and unstable correspondence estimation between the detected patterns and the projected pattern because of irregular reflection. In this paper, we propose a solution for those problems. To increase the resolution, an accurate and stable interpolation method based on deep neural networks (DNNs) is proposed. Since most patterns used for oneshot scan are periodic, pixel-wise phase estimation can be achieved by detecting repetition in the pattern. A graph convolutional network (GCN), which is a deep neural network for graphs, is used for the correspondence problem. In the experiment, pixel-wise shape reconstruction results, as well as robust correspondence estimation using DNNs and a GCN, are shown. In addition, the effectiveness of the techniques is confirmed by comparing the proposed method with existing methods.

Unsupervised learning of depth estimation from imperfect rectified stereo laparoscopic images

BackgroundLearning-based methods have achieved remarkable performances on depth estimation. However, the premise of most self-learning and unsupervised learning methods is built on rigorous, geometrically-aligned stereo rectification. The performances of these methods degrade when the rectification is not accurate. Therefore, we explore an approach for unsupervised depth estimation from stereo images that can handle imperfect camera parameters. MethodsWe propose an unsupervised deep convolutional network that takes rectified stereo image pairs as input and outputs corresponding dense disparity maps. First, a new vertical correction module is designed for predicting a correction map to compensate for the imperfect geometry alignment. Second, the left and right images, which are reconstructed based on the input image pair and corresponding disparities as well as the vertical correction maps, are regarded as the outputs of the generative term of the generative adversarial network (GAN). Then, the discriminator term of the GAN is used to distinguish the reconstructed images from the original inputs to force the generator to output increasingly realistic images. In addition, a residual mask is introduced to exclude pixels that conflict with the appearance of the original image in the loss calculation. ResultsThe proposed model is validated on the publicly available Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED) dataset and the average MAE is 3.054 mm. ConclusionOur model can effectively handle imperfect rectified stereo images for depth estimation.

A study on machine learning techniques for the schema matching network problem

Schema matching is the problem of finding semantic correspondences between elements from different schemas. This is a challenging problem since disparate elements in the schemas often represent the same concept. Traditional instances of this problem involved a pair of schemas. However, recently, there has been an increasing interest in matching several related schemas at once, a problem known as schema matching networks. The goal is to identify elements from several schemas that correspond to a single concept. We propose a family of methods for schema matching networks based on machine learning, which proved to be a competitive alternative for the traditional matching problem in several domains. To overcome the issue of requiring a large amount of training data, we also propose a bootstrapping procedure to generate training data automatically. In addition, we leverage constraints that arise in network scenarios to improve the quality of this data. We also study a strategy for receiving user feedback to assert some of the matchings generated and, relying on this feedback, improve the final result’s quality. Our experiments show that our methods can outperform baselines, reaching F1-score up to 0.83.

Clinical pharmaceutical screening in critical situations in a radioiodine therapy management service.

Radioiodine therapy can be used in differentiated thyroid carcinoma and requires extensive evaluation to ensure effectiveness and safety. Therefore, it is necessary to evaluate all health problems and medications used in the pre-radioiodine therapy period and comprehensive medication managementservices can serve as a screening tool in this context. The present study aims to describe critical clinical situations identified during the initial assessments of a comprehensive medication management service offered to differentiated thyroid carcinoma patients pre-radioiodine therapy, and the pharmaceutical interventions performed to solve them. A descriptive study with regard to the initial ten months of a comprehensive medication management service was carried out in a large oncology hospital (Rio de Janeiro, Brazil). Descriptive analysis was used to describe the critical clinical situations identified, as well as the correspondent drug therapy problems and the type, acceptability, and outcomes of the pharmaceutical interventions performed to solve them. Thirty patients with an average of 45.8 years and 5.1 medications were evaluated. Five critical clinical situations were identified; corresponding to drug therapy problems two(needs additional drug therapy - n = 4) and drug therapy problems four (dosage too low - n = 1). All pharmaceutical interventions were accepted. The comprehensive medication management service provision pre-radioiodine therapy is feasible and represents an important screening strategy.

Comparative analysis of laser and simulated speckle pattern for single shot 3D reconstruction

Stereo photogrammetry is a fundamental technique for 3D measurements in different applications. As passive stereo methods struggle in texture-less regions, different light-coded patterns are projected to solve the correspondence problem. When the reconstruction method requires a series of sequentially coded light patterns, the system is prone to movement-related errors. A single pattern is utilized where the potential subjects are dynamic or change the position rapidly. A random speckle pattern is a good candidate for such systems. Primarily, there are two approaches to generate the speckle pattern in stereoscopic systems. The speckles can be generated by the coherent illumination of a laser diode, and the laser speckle-like pattern can also be simulated and projected by a professional projector. The purpose of both is to solve the stereo correspondence problem; however, the performance of both can differ, subjective to employed 3D sensing algorithm. In this Letter, we compare the performance of both for single-shot 3D reconstruction. We have discussed the pros and cons of both methods and analyze their ability to resolve long range 3D reconstruction.

A functional skeleton transfer

The animation community has spent significant effort trying to ease rigging procedures. This is necessitated because the increasing availability of 3D data makes manual rigging infeasible. However, object animations involve understanding elaborate geometry and dynamics, and such knowledge is hard to infuse even with modern data-driven techniques. Automatic rigging methods do not provide adequate control and cannot generalize in the presence of unseen artifacts. As an alternative, one can design a system for one shape and then transfer it to other objects. In previous work, this has been implemented by solving the dense point-to-point correspondence problem. Such an approach requires a significant amount of supervision, often placing hundreds of landmarks by hand. This paper proposes a functional approach for skeleton transfer that uses limited information and does not require a complete match between the geometries. To do so, we suggest a novel representation for the skeleton properties, namely the functional regressor, which is compact and invariant to different discretizations and poses. We consider our functional regressor a new operator to adopt in intrinsic geometry pipelines for encoding the pose information, paving the way for several new applications. We numerically stress our method on a large set of different shapes and object classes, providing qualitative and numerical evaluations of precision and computational efficiency. Finally, we show a preliminar transfer of the complete rigging scheme, introducing a promising direction for future explorations.

Social credit mechanisms and modern standards of legal protection of personal data: correspondence problems

The subject of this article is the problem of correspondence of the practices of digital profiling and social score, which imply collection and analysis of biographical (reputational) information, to the worldwide-accepted standards of protection of personal data and privacy. Analysis is conducted on the legislation of the People's Republic of China – the country that in recent years has implemented the “Social Credit System” in the sphere of public administration. This project consists of management practices, which are viewed through the prism of the legal model of personal data protection formed by the Law in Protection of Personal Information adopted in 2021. The peculiarity of this research is its comparative legal nature: the provisions of China’s legislation are juxtaposed to the provisions of the General Data Protection Regulation adopted in the European Union and Russia’s Federal Law “On Personal Data”. Assessment is given to the European and Russian models of regulation of operations with personal data in the context of possible implementation of digital profiling practices, social score (ranking, grading), and automated law enforcement decision-making. Having determined the gaps in the current Russian and EU legislation on personal data, and indicating the risk caused by the presence of blanket rules, the conclusion is made according to which the modern legislation on personal data can be an obstacle for arbitrary use of such data; however, it cannot stop the implementation of innovative technologies, mechanisms and practices that suggest using registry and biographical information of individuals for the purpose of social control into the public administration.

Planar multi-patch domain parameterization for isogeometric analysis based on evolution of fat skeleton

In this paper, we propose a new algorithm for computing planar multi-patch domain parameterizations which are suitable for consequent numerical computations based on isogeometric analysis. Firstly, a so-called fat skeleton of the given domain, represented by a multi-patch NURBS parameterization, is constructed, which determines the topology of a multi-patch NURBS parameterization suitable for the given domain. Then, we formulate an evolution process which successively transforms the initial shape represented by the fat skeleton to the target shape represented by the given domain. Moreover, a multi-patch optimization is applied in every time iteration to make the multi-patch NURBS parameterization as uniform and orthogonal as possible. The method is constructed such that it is fully automatic and it solves the biggest problems of the domain parameterization problem, i.e., it automatically determines a suitable topology of a multi-patch NURBS parameterization for the given domain and it also automatically solves the boundary correspondence problem. The functionality of the proposed method is demonstrated on several examples.

Semantic Correspondence with Geometric Structure Analysis

This article studies the correspondence problem for semantically similar images, which is challenging due to the joint visual and geometric deformations. We introduce the Flip-aware Distance Ratio method (FDR) to solve this problem from the perspective of geometric structure analysis. First, a distance ratio constraint is introduced to enforce the geometric consistencies between images with large visual variations, whereas local geometric jitters are tolerated via a smoothness term. For challenging cases with symmetric structures, our proposed method exploits Curl to suppress the mismatches. Subsequently, image correspondence is formulated as a permutation problem, for which we propose a Gradient Guided Simulated Annealing (GGSA) algorithm to perform a robust discrete optimization. Experiments on simulated and real-world datasets, where both visual and geometric deformations are present, indicate that our method significantly improves the baselines for both visually and semantically similar images.

High-speed and accurate 3D shape measurement using DIC-assisted phase matching and triple-scanning

Structured-light (SL) three-dimensional (3D) shape measurements technique has been extensively studied and applied in various fields, but the real-time capability of SL system is limited by the number of projected SL patterns. This paper presents a digital image correlation (DIC) assisted phase matching and triple-scanning method for high-speed and accurate 3D shape measurements based on the combined projection of three-step phase-shifting patterns and one speckle pattern. Benefiting from the triple-view geometric constraints and DIC technique, stereo correspondence and absolute fringe order of each valid pixel in the left wrapped phase map can be explored without requiring spatial phase unwrapping process or any priori information, the initial disparity map and absolute phase map can be generated. Subsequently, an efficient disparity refinement scheme based on connectivity area segmentation and neighbor disparity check is applied to the initial disparity map and absolute phase map to compensate the pixels with wrong disparities. Several efficient strategies are developed in the pipeline of data processing to speed up the algorithm. To improve the measurement accuracy, stereo SL model is proposed to make adequate use of triple-view information to calculate 3D coordinates using the disparity map and absolute phase map, and the measurement error has been reduced by 33% compared with the conventional methods. The experimental results demonstrate that the proposed method can perform high-speed and accurate 3D shape measurements for arbitrary surfaces and dynamic scenes.

Parallax‐based second‐order mixed attention for stereo image super‐resolution

Stereo image pairs can effectively enhance the performance of super-resolution (SR) since both intra-view and cross-view information can be used. However, exploiting cross-view information accurately is extremely challenging. Most recent methods use the attention mechanism to get stereo correspondence. But these methods ignore the high-frequency information since they only utilise first-order statistics, which leads to reducing the discriminative ability of the network. To address this issue, in this work, a parallax-based second-order mixed attention stereo SR network (PSMASSRnet) is proposed to integrate the cross-view information from a stereo image pair for SR. Specifically, a novel parallax-based second-order mixed attention module (PSMAM) is developed to combine second-order channel features and spatial features to obtain more discriminative representations. Furthermore, a dense cross-atrous spatial pyramid pooling (ASPP) module is also presented, which can effectively explore the local and the multi-scale features with different dilation rates to extract more discriminative features with fewer parameters and less execution. The extensive experiments on the KITTI2012, KITTI2015 and Middlebury datasets have demonstrated the superiority of the proposed PSMASSRnet over the state-of-the-art methods in the aspects of both the quantitative metrics and the visual quality.

Local stereo matching algorithm using modified dynamic cost computation

Stereo matching is an essential subject in stereo vision architecture. Traditional framework composition consists of several constraints in stereo correspondences such as illumination variations in images and inadequate or non-uniform light due to uncontrollable environments. This work improves the local method stereo matching algorithm based on the dynamic cost computation method for depth measurement. This approach utilised modified dynamic cost computation in the matching cost. A modified census transform with dynamic histogram is used to provide the cost in the cost computation. The algorithm applied the fixed-window strategy with bilateral filtering to retain image depth information and edge in the cost aggregation stage. A winner takes all (WTA) optimisation and left-right check with adaptive bilateral median filtering are employed for disparity refinement. Based on the Middlebury benchmark dataset, the algorithm developed in this work has better accuracy and outperformed several other state-of-the-art algorithms.

A Novel Dbmd Implementation for Big Data Mining and Clustering Via Cloud Computing

Matrix structure was one of the most important devices for finding data from big data. Here you’ll find data produced by current applications using cloud computing. However, moving big data using such a system in a performance computer or through virtual machines is still inefficient or impossible. Furthermore, big data is often gathered data from a variety of data sources and stored on a variety of machines using scheduling algorithms. As a result, such data usually bear solid shifted commotion. Growing circulated matrix deterioration is necessary and beneficial for big data analysis. Such a plan should have a good chance of succeeding. Represent the diverse clamor and deal with the correspondence problem in a disseminated manner. In order to do this, we used a Bayesian matrix decay model (DBMD) for big data mining and grouping. Only three approaches to disseminated computation are considered: 1) accelerate slope drop, 2) alternating path method of multipliers (ADMM), and 3) observable derivation. We look at how these approaches could be mixed together in the future. To deal with the commotion’s heterogeneity, we suggest an ideal module weighted norm that reduces the assessment’s differentiation. Finally, a comparison was made between these approaches in order to understand the differences in their outcomes.