Rectification Model Research Articles

Automatic Detection and 3D Reconstruction of Buildings from Historical Maps

ABSTRACT This paper presents an automatic 3D building reconstruction methodology for historical urban maps. It uses facade and openings detection on the maps, followed by rectification, regularization, and 3D model generation techniques. Evaluation metrics confirm the effectiveness of the approach, with high accuracy in detecting facades and their openings while maintaining geometric integrity. The flexibility and interoperability of the chosen 3D building representation method allow for adjustments in dimensions without compromising layout, making it suitable for completing the urban environments where facades may not be directly visible. This methodology represents a significant step toward automating the reconstruction of 3D historical urban landscapes, contributing to heritage preservation and architectural understanding.

Practical single-line diagram recognition based on digital image processing and deep vision models

The Single-Line Diagram (SLD) is a standardized graphical representation of power systems. Power engineering software typically requires users to model power systems by drawing SLDs. Although existing computer vision-based SLD recognition approaches facilitate system modeling to some extent, their performance is usually limited by the low resolution of images, the imbalance in size and number of electrical symbols, and the interference between adjacent graphical elements on practical SLDs. This paper proposes a novel SLD recognition framework to address these challenges. First, a text and symbol separation module based on digital image processing is developed to enable independent analysis of text labels and electrical symbols to avoid mutual interference. To accurately recognize electrical symbols of different sizes, the proposed framework integrates the Swin Transformer into the Faster Region-based Convolutional Neural Network (Faster R-CNN), which ultimately achieves a minimum precision of 98.9% and a minimum recall of 98.12%. In addition, the framework modifies the Text Super-Resolution Network (TSRN) to improve text recognition performance on low-resolution images. Combined with the Attentional Scene Text Recognizer with Flexible Rectification (ASTER) model, the proposed text recognition model results in an average accuracy increase of 1% on the TextZoom dataset and 97.1% accuracy in recognizing practical SLD text labels. A synthetic SLD generation method is also proposed to augment the actual SLD datasets to overcome the class imbalance of electrical symbols. The proposed framework significantly improves the performance of existing engineering drawing recognition frameworks. Relevant source code is available online at https://github.com/Lattle-y/work.git.

Real‐time mosaic of multiple fisheye surveillance videos based on geo‐registration and rectification

AbstractA distributed fisheye video surveillance system (DFVSS) can monitor a wide area without blind spots, but it is often affected by the viewpoint discontinuity and space inconsistency of multiple videos in the area. This paper proposes a novel real‐time fisheye video mosaic algorithm for wide‐area surveillance. First, by extending the line photogrammetry theory under central projection to spherical projection, a fisheye video geo‐registration model is established and estimated using orthogonal parallel lines on the ground, so that all videos of DFVSS are in the unified reference system to eliminate the space inconsistency between them. Second, by combining the photogrammetry orthorectification technique with thin‐plate spline transformation, a fisheye video rectification model is established to eliminate serious distortion in geo‐registered fisheye videos and align them accurately. Third, the viewport‐dependent video selection strategy and video look‐up table computation technique are adopted to create a high‐resolution panorama from input fisheye videos in real time. A parking lot of about 0.4 km2 monitored by eight fisheye cameras was selected as the test area. The experimental result shows the line re‐projection error in fisheye videos is about 0.5 pixels, and the overall efficiency, including panorama creation and mapping to the ground as texture, is not <30 fps. It indicates that the proposed algorithm can achieve a good balance between the limitation of video transmission bandwidth and the smooth observation requirement of computer equipment for the panorama, which is of great value for the construction and application of DFVSS.

Prediction-Oriented Disparity Rectification Model for Geometry-Based Light Field Compression

The geometry-based light field compression approach aims to use the inherent disparity cue provided by the light field to reduce the redundancy of data and improve immersive media broadcasting. Some methods, including our previous one, transmit explicit disparity maps along with sparsely selected views and then exploit disparity-based view prediction to remove correlations in the angular domain. However, these methods neglect two important issues: the 4D correspondence between the disparity array and view array and the practical non-uniform disparity distribution in captured light fields. As a result, the prediction potentiality of disparity maps is heavily restricted, and the limited disparity-based prediction accuracy degrades the final compression performance. In this paper, we focus on these two issues of estimated disparity maps and propose a prediction-oriented disparity rectification (PoDR) model. First, based on our previous disparity estimation, we propose to utilize the 4D structural prior of light fields to refine the estimated disparity map. The 4D correspondence between a disparity array and a view array is enhanced, leading to higher prediction accuracy and lower bit costs. Second, for the refined disparity array, we propose a variable stride-based determination algorithm to obtain the practical non-uniform disparity distribution in captured light fields. Specifically, the angular distance between each pair of disparity maps is efficiently derived by the approximate solution based on the gradient variation of the prediction errors. By combining these two modules, the proposed PoDR model improves the overall compression performance compared with our previous work. Furthermore, we verify that the proposed method can obtain better light field fundamental capability (e.g., refocusing) than state-of-the-art methods.

Shear layer response to overmodulated acoustic perturbations

Shear layers act as demodulators when subjected to amplitude modulated, acoustic perturbations. Recent work explained that the demodulation is a result of the hypothesized relationship between the excitation waveform and the vorticity signal of the large-scale structures, which was represented by a half-wave rectification model. In this paper, this model is explored by overmodulating the excitation signal amplitude. The rectifier model predicts several effects of overmodulation on the flow response, including a doubling of the demodulated response frequency. To validate these predictions, a free, round jet that is excited acoustically upstream of the nozzle is studied using particle image velocimetry. Analytical results from the model are confirmed using a second experimental setup where a jet that emerges from a nozzle and attaches to an adjacent, inclined wall is excited acoustically. Beyond the insight it provides into shear layer vortex dynamics, overmodulation serves as a useful excitation technique for practical applications, where the shear layer response frequency can be increased at the expense of the response amplitude.

Keyframe-Editable Real-Time Motion Synthesis

Since existing motion synthesis methods often lack precise controls to the synthesis process, we propose a keyframe-editable motion synthesis framework which allows users to edit the keyframes of an expected motion sequence and use the edited keyframes to control and drive the synthesis process. Specifically, a motion segment can be represented as a start-end frame pair and a group of motion patterns which record the joint angle changing processes between the start and end frames. With the pre-trained paired dictionaries relating the start-end frame space and motion pattern space, we can use the edited keyframe pair to generate its corresponding motion pattern, so the validity of keyframes is very important to the quality of synthesized motions. Thus, we use the probability to measure the motion naturalness and propose a naturalness rectification method to guarantee the validity of the edited keyframes. We also provide a joint-move interface and propose a position refinement method for the detailed adjustment of the joint positions of keyframes. Besides, we add extra naturalness constraint to the motion synthesis process to further improve the naturalness of the generated in- between frames. Extensive rectification experiments in different situations verify the effect of the proposed naturalness rectification model. The comparisons of the synthesis results with other state-of-the-art synthesis methods demonstrate the advantage of our motion synthesis model. The high efficiencies of all the algorithms make the proposed framework competent to the real-time motion synthesis.

A High-Efficiency Self-Synchronous RF–DC Rectifier Based on Time-Reversal Duality for Wireless Power Transfer Applications

An RF–DC rectifier is an important part in a wireless power transfer system. Diode-based rectifiers are widely used in low-power harvesting scenarios, and for high power, a transistor based on the time-reversal duality was proposed. This paper presents a high-efficiency self-synchronous RF–DC rectifier based on a waveform-guided design method and an improved rectification model of a commercial GaN device. The main contributions of this paper are that (1) an improved transistor model with correct reverse bias is built for accurate rectifier simulation, and (2) a new design method of self-synchronous RF–DC rectifier is proposed: as soon as the operating mode of the rectifier, input power, and DC load are set, matching and coupling network can be calculated directly based on waveform-guided method, thus design and adjustment process of a conventional power amplifier (PA) due to the duality between a PA and a rectifier would no longer be required. A 5.8 GHz self-synchronous RF–DC rectifier is designed for validation, and the optimum RF–DC conversion efficiency is 68% with 12 W input power as well as 19.9 V output DC potential with 50 Ω load resistance. The proposed rectifier is suitable for high input power rectification applications of wireless power transfer.

Lookup-Table-Based Automated Rectifier Synthesis

Rectifier design nowadays relies heavily on iterative optimization, which is prone to fall into local minima or end up with no solution. Such a procedure is time-consuming and requires lots of experience from designers. Guided rectifier synthesis with the least engagement of optimization is thus desired. In this article, a lookup-table-based automated rectifier synthesis method is presented. The lookup tables are essentially contour maps of the rectifier performance indicators on a 2-D plane of the absorbed RF power P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">abs</sub> and the load resistance R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> . To establish lookup tables, an improved rectification model is developed, allowing for accurate data generation at any specific coordinate (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> , P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">abs</sub> ). High-quality lookup tables are thus established with the equidistant data over the (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> , P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">abs</sub> ) plane. Based on the lookup tables, the optimal operating point (R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</sub> , P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">abs</sub> ) can be straightforwardly identified according to the requirement, facilitating the subsequent diode and topology selection and matching circuit design. The whole design flow is sequential and automated, only engaging fine-tuning in the final stage. Simulation and measurement results validate the proposed methodology.

Specific Absorption Rate-Aware Beamforming in MISO Downlink SWIPT Systems

This paper investigates the optimal transmit beamforming design of simultaneous wireless information and power transfer (SWIPT) in the multiuser multiple-input-single-output (MISO) downlink with specific absorption rate (SAR) constraints. We consider the power splitting technique for SWIPT, where each receiver divides the received signal into two parts: one for information decoding and the other for energy harvesting with a practical non-linear rectification model. The problem of interest is to maximize as much as possible the received signal-to-interference-plus-noise ratio (SINR) and the energy harvested for all receivers, while satisfying the transmit power and the SAR constraints by optimizing the transmit beamforming at the transmitter and the power splitting ratios at different receivers. The optimal beamforming and power splitting solutions are obtained with the aid of semidefinite programming and bisection search. Low-complexity fixed beamforming and hybrid beamforming techniques are also studied. Furthermore, we study the effect of imperfect channel information and radiation matrices, and design robust beamforming to guarantee the worst-case performance. Simulation results demonstrate that our proposed algorithms can effectively deal with the radio exposure constraints and significantly outperform the conventional transmission scheme with power backoff.

Rectifying the Misinformation in Social Network

This paper proposes a rumor rectification model considering the context when rumor in the social network has propagated for a period of time such that it is too late to block the rumor. We give an explanation that existing models on rumor blocking are not appropriate in this case. TIM+ is an efficient approximation algorithm in influence maximization problem, however it handles the case when there is only one influence cascade in the network. We modify it to suit the two cascades case in our problem and find obvious effect comparing with the random solution.

Highly efficient near-field thermal rectification between InSb and graphene-coated SiO2

We report a highly efficient thermal rectification model based on the near-field thermal radiation between InSb and graphene-coated SiO2, separated by nanoscale vacuum gaps. The results show that the introduction of graphene sheet enhances significantly near-field radiative heat flux and thermal rectification efficiency owing to the strong coupling of surface plasmon–polaritons between InSb and graphene. Specifically, under the same temperature bias, a rectification efficiency exceeding 80% is obtained at vacuum gaps varying from 10 nm to 100 nm for the graphene-coated SiO2 case, while such an efficiency requires a narrower gap between 10 nm to 20 nm for the bare SiO2 case. In addition, the introduction of graphene can lower greatly the emitter's temperature TH, e.g., an efficiency of 60% requires TH = 550 K in the bare SiO2 case, while it requires only the temperature around 400 K in the coated SiO2 case. The above results might be helpful in designing a highly efficient thermal diode with a wider vacuum gap and a lower operating temperature.

Imbalanced Deep Learning by Minority Class Incremental Rectification.

Model learning from class imbalanced training data is a long-standing and significant challenge for machine learning. In particular, existing deep learning methods consider mostly either class balanced data or moderately imbalanced data in model training, and ignore the challenge of learning from significantly imbalanced training data. To address this problem, we formulate a class imbalanced deep learning model based on batch-wise incremental minority (sparsely sampled) class rectification by hard sample mining in majority (frequently sampled) classes during model training. This model is designed to minimise the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process. To that end, we introduce a Class Rectification Loss (CRL) function that can be deployed readily in deep network architectures. Extensive experimental evaluations are conducted on three imbalanced person attribute benchmark datasets (CelebA, X-Domain, DeepFashion) and one balanced object category benchmark dataset (CIFAR-100). These experimental results demonstrate the performance advantages and model scalability of the proposed batch-wise incremental minority class rectification model over the existing state-of-the-art models for addressing the problem of imbalanced data learning.

Asymmetric injection and distribution of space charges in propylene carbonate under impulse voltage

Space charge can distort the electric field in high voltage stressed liquid dielectrics and lead to breakdown. Observing the evolution of space charge in real time and determining the influencing factors are of considerable significance. The spatio-temporal evolution of space charge in propylene carbonate, which is very complex under impulse voltage, was measured in this study through the time-continuous Kerr electro-optic field mapping measurement. We found that the injection charge from a brass electrode displayed an asymmetric effect; that is, the negative charge injection near the cathode lags behind the positive charge injection near the anode. Physical mechanisms, including charge generation and drift, are analyzed, and a voltage-dependent saturated drift rectification model was established to explain the interesting phenomena. Mutual validation of models and our measurement data indicated that a barrier layer, which is similar to metal-semiconductor contact, was formed in the contact interface between the electrode and propylene carbonate and played an important role in the space charge injection.

Directional Label Rectification in Adaptive Graph

With the explosive growth of multivariate time-series data, failure (event) analysis has gained widespread applications. A primary goal for failure analysis is to identify the fault signature, i.e., the unique feature pattern to distinguish failure events. However, the complex nature of multivariate time-series data brings challenge in the detection of fault signature. Given a time series from a failure event, the fault signature and the onset of failure are not necessarily adjacent, and the interval between the signature and failure is usually unknown. The uncertainty of such interval causes the uncertainty in labeling timestamps, thus makes it inapplicable to directly employ any standard supervised algorithms in signature detection. To address this problem, we present a novel directional label rectification model which identifies the fault-relevant timestamps and features in a simultaneous approach. Different from previous graph-based label propagation models using fixed graph, we propose to learn an adaptive graph which is optimal for the label rectification process. We conduct extensive experiments on both synthetic and real world datasets and illustrate the advantage of our model in both effectiveness and efficiency.

MANAGEMENT OF OIL RECTIFICATION PROCESS BY USING OF MODELS OF PROCESS AND VIRTUAL ANALYZERS

The article considers the management of an atmospheric column by using of dynamic rectification model. The criteria of management are defined. The method the optimization is chosen. Management of temperatures provides the set quality of oil products at light fractions selection corresponding selection from potential.

A target tracking and location robot system based on omnistereo vision

PurposeBecause of their large field of view, omnistereo vision systems have been widely used as primary vision sensors in autonomous mobile robot tasks. The purpose of this article is to achieve real-time and accurate tracking by the omnidirectional vision robot system.Design/methodology/approachThe authors provide in this study the key techniques required to obtain an accurate omnistereo target tracking and location robot system, including stereo rectification and target tracking in complex environment. A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.FindingsThe experiments are conducted with all the necessary stages involved in obtaining a high-performance omnistereo vision system. The proposed correction algorithm can process the image in real time. The experimental results of the improved tracking algorithm are better than the original algorithm. The statistical analysis of the experimental results demonstrates the effectiveness of the system.Originality/valueA simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

Calibration and rectification of vertically aligned binocular omnistereo vision systems

Omnidirectional stereo vision systems have been widely used as primary vision sensors in intelligent robot 3D measurement tasks, which require stereo calibration and rectification. Current stereo calibration and rectification methods suffer from complex calculations or a lack of accuracy. This paper establishes a simple and effective equivalency between an omnidirectional stereo vision system and a perspective vision system by studying stereo calibration and rectification methods. First, we improved the stereo calibration method. By applying the essential matrix, the complicated calibration process of the original method is simplified. By using a manual extraction method to extract corner points, noise error is eliminated and high precision is ensured. Second, we propose a new rectification method. By using the proposed simple rectification model and calibration data, the baseline length and an accurate column-aligned image pair are easily obtained, which reduces the computation time. The proposed stereo calibration and rectification method can simply and effectively obtain two key parameters of the triangulation formula for 3D measurement tasks: baseline length and parallax. Using real data captured by equipment, we performed experiments covering all the necessary stages to obtain a high-performance omnidirectional stereo vision system. Statistical analyses of the experimental results demonstrate the effectiveness of the proposed method.

Agricultural Data Rectification Algorithm Based on Internet of Things

Agricultural information data in Internet of Things is the basis of abnormal environment warning system, however, these data in the measurement process will inevitably contain random error and gross error. For the problem of exceptional data existing in wireless sensor networks, a data rectification model based on the detection of gross error is proposed, which can automatically eliminate and compensate the gross error in measured data, in order to achieve the purpose of accurate data rectification. First, utilize the statistical tests method to detect and identify gross error in measured data , and the method can detect more than three gross errors, which show superiority in the number of gross error than other gross error detection methods. Then, median filter is exploited to estimate the value of gross error identified. At last, three order exponential smoothing is used to data reconciliation. Experimental results show that this method can achieve effective performance.

Cloud photogrammetry with dense stereo for fisheye cameras

Abstract. We present a novel approach for dense 3-D cloud reconstruction above an area of 10 × 10 km2 using two hemispheric sky imagers with fisheye lenses in a stereo setup. We examine an epipolar rectification model designed for fisheye cameras, which allows the use of efficient out-of-the-box dense matching algorithms designed for classical pinhole-type cameras to search for correspondence information at every pixel. The resulting dense point cloud allows to recover a detailed and more complete cloud morphology compared to previous approaches that employed sparse feature-based stereo or assumed geometric constraints on the cloud field. Our approach is very efficient and can be fully automated. From the obtained 3-D shapes, cloud dynamics, size, motion, type and spacing can be derived, and used for radiation closure under cloudy conditions, for example. Fisheye lenses follow a different projection function than classical pinhole-type cameras and provide a large field of view with a single image. However, the computation of dense 3-D information is more complicated and standard implementations for dense 3-D stereo reconstruction cannot be easily applied. Together with an appropriate camera calibration, which includes internal camera geometry, global position and orientation of the stereo camera pair, we use the correspondence information from the stereo matching for dense 3-D stereo reconstruction of clouds located around the cameras. We implement and evaluate the proposed approach using real world data and present two case studies. In the first case, we validate the quality and accuracy of the method by comparing the stereo reconstruction of a stratocumulus layer with reflectivity observations measured by a cloud radar and the cloud-base height estimated from a Lidar-ceilometer. The second case analyzes a rapid cumulus evolution in the presence of strong wind shear.

On the Accuracy of Dense Fisheye Stereo

Fisheye cameras offer a large field of view, which is important for several robotics applications as a larger field of view allows for covering a large area with a single image. In contrast to classical cameras, however, fisheye cameras cannot be approximated well using the pinhole camera model and this renders the computation of depth information from fisheye stereo image pairs more complicated. In this work, we analyze the combination of an epipolar rectification model for fisheye stereo cameras with existing dense methods. This has the advantage that existing dense stereo systems can be applied as a black-box even with cameras that have field of view of more than 180° to obtain dense disparity information. We thoroughly investigate the accuracy potential of such fisheye stereo systems using image data from our UAV. The empirical analysis is based on image pairs of a calibrated fisheye stereo camera system and two state-of-the-art algorithms for dense stereo applied to adequately rectified image pairs from fisheye stereo cameras. The canonical stochastic model for sensor points assumes homogeneous uncertainty and we generalize this model based on an empirical analysis using a test scene consisting of mutually orthogonal planes. We show that: (1) the combination of adequately rectified fisheye image pairs and dense methods provides dense 3D point clouds at 6–7 Hz on our autonomous multicopter UAV; (2) the uncertainty of points depends on their angular distance from the optical axis; (3) how to estimate the variance component as a function of that distance; and (4) how the improved stochastic model improves the accuracy of the scene points.