Correspondence Search Research Articles

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.

Graph-matching-based correspondence search for nonrigid point cloud registration

Nonrigid registration finds transformations to fit a source point cloud/mesh to a target point cloud/mesh. Most nonrigid registration algorithms consist of two steps; finding correspondence and optimization. Among these, finding correspondence plays an important role in registration performance. However, when two point clouds have large displacement, it is hard to know correct correspondences and an algorithm often fails to find correct transformations. In this paper, we propose a novel graph-matching-based correspondence search for nonrigid registration and a corresponding optimization method for finding transformation to complete nonrigid registration. Considering global connectivity as well as local similarity for the correspondence search, the proposed method finds good correspondences according to semantics and consequently finds correct transformations even when the motion is large. Our algorithm is experimentally validated on human body and animal datasets, which verifies that it is capable of finding correct transformations to fit a source to a target.

Variational Level Set Evolution for Non-Rigid 3D Reconstruction From a Single Depth Camera.

We present a framework for real-time 3D reconstruction of non-rigidly moving surfaces captured with a single RGB-D camera. Based on the variational level set method, it warps a given truncated signed distance field (TSDF) to a target TSDF via gradient flow without explicit correspondence search. We optimize an energy that contains a data term which steers towards voxel-wise alignment. To ensure geometrically consistent reconstructions, we develop and compare different strategies, namely an approximately Killing vector field regularizer, gradient flow in Sobolev space and newly devised accelerated optimization. The underlying TSDF evolution makes our approach capable of capturing rapid motions, topological changes and interacting agents, but entails loss of data association. To recover correspondences, we propose to utilize the lowest-frequency Laplacian eigenfunctions of the TSDFs, which encode inherent deformation patterns. For moderate motions we are able to obtain implicit associations via a term that imposes voxel-wise eigenfunction alignment. This is not sufficient for larger motions, so we explicitly estimate voxel correspondences via signature matching of lower-dimensional eigenfunction embeddings. We carry out qualitative and quantitative evaluation of our geometric reconstruction fidelity and voxel correspondence accuracy, demonstrating advantages over related techniques in handling topological changes and fast motions.

Revisiting the Dichotomy between Abusive Supervision and Work Performance of Subordinates in Private Organizations in Kenya: An Empirical Search for Correspondence between Theory and Practice

This systematic and empirical review attempted to expand the frontiers of knowledge on abusive supervision against employee performance using private sector as a case sample. Using no mediating model between X and Y, the study focused on the assumed and observable linearity between the two variables under investigation whilst using available quantitative and qualitative data as point of reference. Hypothetically, it is assumed that, abusive supervision has a negative correlation with employee performance across organization. This kind of reasoning is guided by observable longitudinal data, feelings and opinions drawn from various categories of individuals over time. Important to note, is the fact that, these observations are in conflict with many research works and reports whose conclusions seem to suggest that abusive supervision has a direct positive causal impact on employee performance. This empirical review is borne out of this contradiction as an attempt to contribute to the ongoing debate and systematically direct reasoning to the desired end. A statistically selected sample of 80 respondents from 101 total population was drawn from 10 private organizations across Kenya for quantitative data and a team of key informants (10) one from each organization was selected for interviews. A mixed method approach was adopted which gave birth to descriptive statistics and thematic approach for analysis. A casual path linking the supervisor mindfulness, supervisor hostility, supervisor attitudes and organizational leadership structures was established. Supervisor hostility and attitudes negatively affected employee performance in the private sector organizations whilst supervisor mindfulness and organizational leadership structures positively affected employee performance in the same context. Important to note is that, although the associations between perceptions of abusive supervision and employee performance appear to be universally negative, the magnitude of the relationship between perceptions of abusive supervision and employee performance varies according to the study design, context, culture and timing. Great need is sought to continuously harmonize the synergy between theory and practice for the future of the practitioners rests entirely on this evaluation.

Graph convolutional neural network for multi-scale feature learning

Automatic deformable 3D modeling is computationally expensive, especially when considering complex position, orientation and scale variations. We present a volume segmentation framework to utilize local and global regularizations in a data-driven approach. We introduce automated correspondence search to avoid manually labeling landmarks and improve scalability. We propose a novel marginal space learning technique, utilizing multi-resolution pooling to obtain local and contextual features without training numerous detectors or excessively dense patches. Unlike conventional convolutional neural network operators, graph-based operators allow spatially related features to be learned on the irregular domain of the multi-resolution space, and a graph-based convolutional neural network is proposed to learn representations for position and orientation classification. The graph-CNN classifiers are used within a marginal space learning framework to provide efficient and accurate shape pose parameter hypothesis prediction. During segmentation, a global constraint is initially non-iteratively applied, with local and geometric constraints applied iteratively for refinement. Comparison is provided against both classical deformable models and state-of-the-art techniques in the complex problem domain of segmenting aortic root structure from computerized tomography scans. The proposed method shows improvement in both pose parameter estimation and segmentation performance.

Dense Image-Matching via Optical Flow Field Estimation and Fast-Guided Filter Refinement

The development of an efficient and robust method for dense image-matching has been a technical challenge due to high variations in illumination and ground features of aerial images of large areas. In this paper, we propose a method for the dense matching of aerial images using an optical flow field and a fast-guided filter. The proposed method utilizes a coarse-to-fine matching strategy for a pixel-wise correspondence search across stereo image pairs. The pyramid Lucas–Kanade (L–K) method is first used to generate a sparse optical flow field within the stereo image pairs, and an adjusted control lattice is then used to derive the multi-level B-spline interpolating function for estimating the dense optical flow field. The dense correspondence is subsequently refined through a combination of a novel cross-region-based voting process and fast guided filtering. The performance of the proposed method was evaluated on three bases, namely, the matching accuracy, the matching success rate, and the matching efficiency. The evaluative experiments were performed using sets of unmanned aerial vehicle (UAV) images and aerial digital mapping camera (DMC) images. The results showed that the proposed method afforded the root mean square error (RMSE) of the reprojection errors better than ±0.5 pixels in image, and a height accuracy within ±2.5 GSD (ground sampling distance) from the ground. The method was further compared with the state-of-the-art commercial software SURE and confirmed to deliver more complete matches for images with poor-texture areas, the matching success rate of the proposed method is higher than 97% while SURE is 96%, and there is 47% higher matching efficiency. This demonstrates the superior applicability of the proposed method to aerial image-based dense matching with poor texture regions.

SEMANTIC ROAD SCENE KNOWLEDGE FOR ROBUST SELF-CALIBRATION OF ENVIRONMENT-OBSERVING VEHICLE CAMERAS

Abstract. Environment-observing vehicle camera self-calibration using a structure from motion (SfM) algorithm allows calibration over vehicle lifetime without the need of special calibration objects being present in the calibration images. Scene-specific problems with feature-based correspondence search and reconstruction during the SfM pipeline might be caused by critical objects like moving objects, poor-texture objects or reflecting objects and might have negative influence on camera calibration. In this contribution, a method to use semantic road scene knowledge by means of semantic masks for a semantic-guided SfM algorithm is proposed to make the calibration more robust. Semantic masks are used to exclude image parts showing critical objects from feature extraction, whereby semantic knowledge is obtained by semantic segmentation of the road scene images. The proposed method is tested with an image sequence recorded in a suburban road scene. It has been shown that semantic guidance leads to smaller deviations of the estimated interior orientation and distortion parameters from reference values obtained by test field calibration compared to a standard SfM algorithm.

MONOCULAR-DEPTH ASSISTED SEMI-GLOBAL MATCHING

Abstract. Reconstruction of dense photogrammetric point clouds is often based on depth estimation of rectified image pairs by means of pixel-wise matching. The main drawback lies in the high computational complexity compared to that of the relatively straightforward task of laser triangulation. Dense image matching needs oriented and rectified images and looks for point correspondences between them. The search for these correspondences is based on two assumptions: pixels and their local neighborhood show a similar radiometry and image scenes are mostly homogeneous, meaning that neighboring points in one image are most likely also neighbors in the second. These rules are violated, however, at depth changes in the scene. Optimization strategies tend to find the best depth estimation based on the resulting disparities in the two images. One new field in neural networks is the estimation of a depth image from a single input image through learning geometric relations in images. These networks are able to find homogeneous areas as well as depth changes, but result in a much lower geometric accuracy of the estimated depth compared to dense matching strategies. In this paper, a method is proposed extending the Semi-Global-Matching algorithm by utilizing a-priori knowledge from a monocular depth estimating neural network to improve the point correspondence search by predicting the disparity range from the single-image depth estimation (SIDE). The method also saves resources through path optimization and parallelization. The algorithm is benchmarked on Middlebury data and results are presented both quantitatively and qualitatively.

BICOS—An Algorithm for Fast Real-Time Correspondence Search for Statistical Pattern Projection-Based Active Stereo Sensors

Pattern projection-based 3D measurement systems are widely used for contactless, non-destructive optical 3D shape measurements. In addition, many robot-operated automation tasks require real-time reconstruction of accurate 3D data. In previous works, we have demonstrated 3D scanning based on statistical pattern projection-aided stereo matching between two cameras. One major advantage of this technology is that the actually projected patterns do not have to be known a priori in the reconstruction software. This allows much simpler projector designs and enables high-speed projection. However, to find corresponding pixels between cameras, it is necessary to search the best match amongst all pixels within the geometrically possible image area (that is, within a range on the corresponding epipolar line). The well-established method for this search is to compare each candidate pixel by temporal normalized cross correlation of the brightness value sequences of both pixels. This is computationally expensive and interdicts fast real-time applications on inexpensive computer hardware. We show two variants of our algorithm “Binary Correspondence Search” (BICOS), which solve this task in significantly reduced calculation time. In practice, our algorithm is much faster than traditional, purely cross-correlation-based search while maintaining a similar level of accuracy.

PALVO: visual odometry based on panoramic annular lens.

Visual odometry has received a great deal of attention during the past decade. However, being fragile to rapid motion and dynamic scenarios prevents it from practical use. Here, we present PALVO by applying panoramic annular lens to visual odometry, greatly increasing the robustness to both cases. We modify the camera model for PAL and specially design the initialization process based on the essential matrix. Our method estimates the camera's poses through two-stage tracking, meanwhile builds the local map using a probabilistic mapping method based on the Bayesian framework and feature correspondence search along the epipolar curve. Several experiments are performed to verify our algorithm, demonstrating that our algorithm provides an extremely competitive performance in robustness to rapid motion and dynamic scenarios, meanwhile achieves the same level of accuracy as the state-of-the-art visual odometry.

Depth estimation using modified cost function for occlusion handling

This paper presents a novel approach to the occlusion handling problem in depth estimation using three views. A solution based on modification of similarity cost function is proposed. During the depth estimation via optimization algorithms, like Graph Cuts, the similarity metric is constantly updated so that only non-occluded fragments in the side views are considered. At each iteration of the algorithm, non-occluded fragments are detected based on side view virtual depth maps synthesized from currently the best estimated depth map of the center view. Then, the similarity metric is updated for correspondence search only in non-occluded regions of the side views. The experimental results, conducted on well-known 3D video test sequences, show that the depth maps estimated with the proposed approach provide about 1.25 dB virtual view quality improvement in comparison with the virtual view synthesized based on depth maps generated with the use of the state-of-the-art MPEG Depth Estimation Reference Software.

Scale invariant line-based co-registration of multimodal aerial data using L1 minimization of spatial and angular deviations

In this work, we investigate the coregistration of multimodal data, such as photogrammetric/LiDAR point clouds, digital surface models, orthoimages, or 3D CAD city models, using corresponding line segments. The lines are analytically derived as intersections of adjacent planar surfaces, which can be determined more robustly and are deemed more accurate compared to single point based features. We propose a two-stage approach, which first focuses on finding optimal line correspondences between the datasets using a scale-invariant graph matching method, and then utilizes the found matching as a basis for calculating the optimal coregistration transform. By decoupling the correspondence search from the transform calculation, our approach can use more line pairs for determining the optimal transform than would be practicable with a combined, sampling-style approach. As opposed to competing methods, our transform computation is based on explicitly minimizing the average L1 distance on the matched line set. The assumed model accounts for an isotropic scaling factor, three translations and three rotation angles. We conducted experiments on two publicly available ISPRS datasets: Vaihingen and Dortmund, and compared the performance of several variations of our approach with three competing methods. The results indicate that the L1 methods decreased the median matched line distance by up to one third in case of pre-aligned Z axes. Moreover, when coregistering two photogrammetric datasets acquired from distinct viewing perspectives, our method was able to triple the number of matched lines (under a strict proximity-based criterion) compared to its competitor. Our results show that it is worthwhile to base the transform calculation on significantly more line pairs than is customary for sample consensus-based approaches. Our established validation dataset for line-based coregistration has been published and made available online (https://doi.org/10.17632/dmp7tkn8kc.2).

Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information.

Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolationfree, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradientbased registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.

Adaptive Pixel-wise and Block-wise Stereo Matching in Lighting Condition Changes

The depth information performs a very important role in a production of three-dimensional (3D) video content. One way to acquire this information is a stereo matching method. The stereo matching method searches for correspondences from a stereo image that has two different viewpoints. Subsequently, it estimates the depth information by calculating a disparity value between two corresponding points. Generally, a relatively accurate result is obtained from the correspondence search when the stereo image is captured under uniform illumination and exposure conditions. However, it is difficult to estimate accurate correspondences if each viewpoint image is captured under varying illumination and exposure conditions. In this paper, we analyze conventional pixel-wise and block-wise stereo matching methods that are robust to lighting condition changes. Subsequently, we propose an adaptive pixel-wise and block-wise stereo matching method based on the analysis result.

On-Device Scalable Image-Based Localization via Prioritized Cascade Search and Fast One-Many RANSAC.

We present the design of an entire on-device system for large-scale urban localization using images. The proposed design integrates compact image retrieval and 2D-3D correspondence search to estimate the location in extensive city regions. Our design is GPS agnostic and does not require network connection. In order to overcome the resource constraints of mobile devices, we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3D model-based localization. Furthermore, we propose a new hashing-based cascade search for fast computation of 2D-3D correspondences. In addition, we propose a new one-many RANSAC for accurate pose estimation. The new one-many RANSAC addresses the challenge of repetitive building structures (e.g. windows and balconies) in urban localization. Extensive experiments demonstrate that our 2D-3D correspondence search achieves the state-of-the-art localization accuracy on multiple benchmark datasets. Furthermore, our experiments on a large Google street view image dataset show the potential of large-scale localization entirely on a typical mobile device.

Integrating Deep Semantic Segmentation Into 3-D Point Cloud Registration

Point cloud registration is the task of aligning 3D scans of the same environment captured from different poses. When semantic information is available for the points, it can be used as a prior in the search for correspondences to improve registration. Semantic-assisted Normal Distributions Transform (SE-NDT) is a new registration algorithm that reduces the complexity of the problem by using the semantic information to partition the point cloud into a set of normal distributions, which are then registered separately. In this paper we extend the NDT registration pipeline by using PointNet, a deep neural network for segmentation and classification of point clouds, to learn and predict per-point semantic labels. We also present the Iterative Closest Point (ICP) equivalent of the algorithm, a special case of Multichannel Generalized ICP. We evaluate the performance of SE-NDT against the state of the art in point cloud registration on the publicly available classification data set Semantic3d.net. We also test the trained classifier and algorithms on dynamic scenes, using a sequence from the public dataset KITTI. The experiments demonstrate the improvement of the registration in terms of robustness, precision and speed, across a range of initial registration errors, thanks to the inclusion of semantic information.

Consistent multiple graph matching with multi-layer random walks synchronization

We address the correspondence search problem among multiple graphs with complex properties while considering the matching consistency. We describe each pair of graphs by combining multiple attributes, then jointly match them in a unified framework. The main contribution of this paper is twofold. First, we formulate the global correspondence search problem of multi-attributed graphs by utilizing a set of multi-layer structures. The proposed formulation describes each pair of graphs as a multi-layer structure, and jointly considers whole matching pairs. Second, we propose a robust multiple graph matching method based on the multi-layer random walks framework. The proposed framework synchronizes movements of random walkers, and leads them to consistent matching candidates. In our extensive experiments, the proposed method exhibits robust and accurate performance over the state-of-the-art multiple graph matching algorithms.

TRANSLATOR’S CROSS-CULTURAL COMPETENCE IN CONDITIONS OF INTERNATIONAL RELATIONS INTEGRATION PROCESSES

The purpose of the article. The research is related to the generalization of scientific information on the emergence and development of the concept of «intercultural communication», as well as its impact on the life of modernized society, reveals the essence and versatility of the concepts of «translation» and «translator» in the structure of the intercultural communicative act and analyzes the relationship and the influence of a translator on the finite result in the process of intercultural communication. Scientific novelty determines the role of a translator in overcoming communicative deviations on the basis of linguistic and cultural differences. As the translator’s task includes not only the search of equivalent correspondences for the transmission of the communicative installation of the sender, but also the provision to the recipient of necessary information for understanding the explanation, that is why the issue of translator’s competence in this context is of paramount importance. Conclusions. It is proved that the competence of a translator is very important and necessary means of intercultural communication and plays an extraordinary role in maintaining direct communication between communicators belonging to completely different linguistic and cultural areas.

СПОСОБЫ ПЕРЕВОДА ЛЕКСИЧЕСКИХ ЕДИНИЦ В АНГЛО-ТАТАРСКОМ СЛОВАРЕ ДЛЯ УЧАЩИХСЯ

The article considers the ways of lexical units translation in the English-Tatar dictionary for students. Educational Tatar lexicography with a foreign-language component is becoming more and more in demand today due to the development of polylingual Russian-Tatar-English environment within the territory of the Republic of Tatarstan. The paper reveals the main ways of translating words, phrases and sentences, which can be used by lexicographers for further work in this field. The author pays attention to the search for correspondences in the two different-system languages, the accuracy and adequacy of translation.

RETRACTED ARTICLE: Research on Chinese and English language information retrieval algorithm based on bilingual theme model

In order to meet the needs of users to query English information through their mother tongue, this paper studies the feasibility of cross language information retrieval by bilingual theme model from the point of view of precise correspondence search between Chinese and English topic words, and combines bilingual theme space constructed by bilingual related topics. A matching algorithm based on the weight value of each topic to respond to the topic correlation is presented in descending order according to the weight statistics. The results of data analysis show that the retrieval algorithm proposed in this paper is feasible, accurate, and highly interactive with users. It can basically achieve the retrieval of English information by BITC, and satisfy users’ accessibility to English information through native language.