Explore the vast range of titles available.

Conventional displacement sensors have limitations in practical applications. This paper develops a vision sensor system for remote measurement of structural displacements. An advanced template matching algorithm, referred to as the upsampled cross correlation, is adopted and further developed into a software package for real-time displacement extraction from video images. By simply adjusting the upsampling factor, better subpixel resolution can be easily achieved to improve the measurement accuracy. The performance of the vision sensor is first evaluated through a laboratory shaking table test of a frame structure, in which the displacements at all the floors are measured by using one camera to track either high-contrast artificial targets or low-contrast natural targets on the structural surface such as bolts and nuts. Satisfactory agreements are observed between the displacements measured by the single camera and those measured by high-performance laser displacement sensors. Then field tests are carried out on a railway bridge and a pedestrian bridge, through which the accuracy of the vision sensor in both time and frequency domains is further confirmed in realistic field environments. Significant advantages of the noncontact vision sensor include its low cost, ease of operation, and flexibility to extract structural displacement at any point from a single measurement.

A non-contact vision sensor system for monitoring structural displacements with advanced Zernike subpixel edge detection technique is suggested in this paper. Edge detection can detect features of objects effectively without using templates. Subpixel techniques provide more accurate and cost-effective results when compared to integer pixel methods. Built on these two techniques, a new version sensor method was developed to detect the vibrations of structures in this study. Satisfactory agreements were found between the displacements measured by the vision sensor system and those recorded by the Multipurpose Testing System (MTS). A field test was then carried out on a street sign using the proposed vision system. Satisfactory results were obtained using the new version of the sensor system at many points simultaneously without any manually marked targets. Moreover, the system was able to provide natural frequencies and mode shapes of the target instantaneously, which could be used to accurately locate damage.

The paper presents an intelligent real-time slope surface deformation monitoring system based on binocular stereo-vision. To adapt the system to field slope monitoring, a design scheme of concentric marking point is proposed. Techniques including Zernike moment edge extraction, the least squares method, and k-means clustering are used to design a sub-pixel precision localization method for marker images. This study is mostly focused on the tracking accuracy of objects in multi-frame images obtained from a binocular camera. For this purpose, the Upsampled Cross Correlation (UCC) sub-pixel template matching technique is employed to improve the spatial-temporal contextual (STC) target-tracking algorithm. As a result, the tracking accuracy is improved to the sub-pixel level while keeping the STC tracking algorithm at high speed. The performance of the proposed vision monitoring system has been well verified through laboratory tests.

The pursuit of compact microscopy systems faces dual constraints from cascaded optical elements and sensor pixel limits. While the integration of metalens and sensor eliminates the bulky elements, the resolution remains confined by pixel-induced under-sampling. Here, we propose a computational imaging framework that synergizes a compact metalens microscope with a transformer-based neural network to achieve subpixel-resolution. To bridge the simulation-to-reality gap, we construct the first experimental dataset of metalens-acquired thyroid pathological sections images. The training strategy enables rapid (~ 0.2s for 110 μm × 110 μm FOV), high-fidelity (structural similarity up to 91%) reconstruction from single-frame inputs, achieving 3 × spatial sampling density with a high resolution (close to the ground truth resolution of 0.87 μm). We further demonstrate its scalability by implementing the trained network in a metalens array-based system, achieving wide-field (4 mm × 6 mm) and high-resolution (close to the Olympus 10 × /0.25NA objective) imaging, with a field of view approximately 14.5 times that of the Olympus objective. The proposed framework highlights the synergy between simplified optical hardware and computational reconstruction, paving the way for compact and intelligent microscopy.

The article describes the technology of express analysis of images and videos, recorded by coastal video monitoring systems, developed by the authors. Its main feature is its ability to measure or evaluate in real time the signals of sea waves, sea level fluctuations, variations of underwater currents, etc., on video recordings or streaming video from coastal cameras. The real-time mode is achieved due to processing video information read not from files, but from the graphic memory of the screen. Measurements of sea signals can be carried out continuously for a long time, up to several days, with high sampling rate, up to 16 Hz, at several points of the observed water area simultaneously. This potentially allows studying the entire spectrum of wave movements, from short waves with periods of 0.3–0.5 s to multi-day fluctuations at the sea level of a synoptic scale. The paper provides examples of the use of this technology for analyzing images and videos obtained in the network of scientific video monitoring of the Peter the Great Bay (Sea of Japan/East Sea), deployed by the authors.

We study a simple \"horizon model\" for the problem of recovering an image from noisy data; in this model the image has an edge with α - Holder regularity. Adopting the viewpoint of computational harmonic analysis, we develop an overcomplete collection of atoms called wedgelets, dyadically organized indicator functions with a variety of locations, scales and orientations. The wedgelet representation provides nearly optimal representations of objects in the horizon model, as measured by minimax description length. We show how to rapidly compute a wedgelet approximation to noisy data by finding a special edgelet-decorated recursive partition which minimizes a complexity-penalized sum of squares. This estimate, using sufficient subpixel resolution, achieves nearly the minimax mean-squared error in the horizon model. In fact, the method is adaptive in the sense that it achieves nearly the minimax risk for any value of the unknown degree of regularity of the horizon, 1 ≤ α ≤ 2. Wedgelet analysis and denoising may be used successfully outside the horizon model. We study images modelled as indicators of star-shaped sets with smooth boundaries and show that complexity-penalized wedgelet partitioning achieves nearly the minimax risk in that setting also.

Long description: The dissertation mainly studies a novel method of subpixel stereo vision for Electronic cluster eye (eCley), a state-of-the-art artificial superposition compound eye with super resolution. In the whole thesis, The author mainly deduce the mathematical model of stereo vision in eCley theoretically based on its special structure, discuss the optical correction and geometric calibration that are essential to high precision measurement, study the implementation of methods of the subpixel baselines for each pixel pair based on intensity information and gradient information in transitional areas, and eventually implement real-time subpixel distance measurement for objects through these edge features. To verify the various methods adopted, and to analyze the precision of these methods, experiments are implemented in many practical scenes. This stereo vision method extends the ability of perceiving 3D information in eCley, and makes it applicable to more comprehensive fields such as 3D object position, distance measurement, and 3D reconstruction.

An algorithm for approximation of the laser sheet of a line in images obtained by means of a array photodetector is considered. A description of a mathematical model of an image of a laser sheet is presented. The influence of the dimensions of the Gaussian beam, angle of inclination, and dimensions of a treated fragment on the standard deviation and bias of the estimators in the determination of the parameters of the line is investigated by means of simulation modeling. Recommendations for image processing are given.[PUBLICATION ABSTRACT]

In statistical image reconstruction, data are often recorded on a regular grid of squares, known as pixels, and the reconstructed image is defined on the same pixel grid. Thus, the reconstruction of a continuous planar image is piecewise constant on pixels, and boundaries in the image consist of horizontal and vertical edges lying between pixels. This approximation to the true boundary can result in a loss of information that may be quite noticeable for small objects, only a few pixels in size. Increasing the resolution of the sensor may not be a practical alternative. If some prior assumptions are made about the true image, however, reconstruction to a greater accuracy than that of the recording sensor's pixel grid is possible. We adopt a Bayesian approach, incorporating prior information about the true image in a stochastic model that attaches higher probability to images with shorter total edge length. In reconstructions, pixels may be of a single color or split between two colors. The model is illustrated using both real and simulated data.

Forest fires are among the biggest threats to forest ecosystems and forest resources, and can lead to ecological disasters and social crises. Therefore, it is imperative to detect and extinguish forest fires in time to reduce their negative impacts. Satellite remote sensing, especially meteorological satellites, has been a useful tool for forest-fire detection and monitoring because of its high temporal resolution over large areas. Researchers monitor forest fires directly at pixel level, which usually presents a mixture of forest and fire, but the low spatial resolution of such mixed pixels cannot accurately locate the exact position of the fire, and the optimal time window for fire suppression can thus be missed. In order to improve the positioning accuracy of the origin of forest fire (OriFF), we proposed a mixed-pixel unmixing integrated with pixel-swapping algorithm (MPU-PSA) model to monitor the OriFFs in time. We then applied the model to the Japanese Himawari-8 Geostationary Meteorological Satellite data to obtain forest-fire products at subpixel level. In this study, the ground truth data were provided by the Department of Emergency Management of Hunan Province, China. To validate the positioning accuracy of MPU-PSA for OriFFs, we applied the model to the Himawari-8 satellite data and then compared the derived fire results with fifteen reference forest-fire events that occurred in Hunan Province, China. The results show that the extracted forest-fire locations using the proposed method, referred to as forest fire locations at subpixel (FFLS) level, were far closer to the actual OriFFs than those from the modified Himawari-8 Wild Fire Product (M-HWFP). This improvement will help to reduce false fire claims in the Himawari-8 Wild Fire Product (HWFP). We conducted a comparative study of M-HWFP and FFLS products using three accuracy-evaluation indexes, i.e., Euclidean distance, RMSE, and MAE. The mean distances between M-HWFP fire locations and OriFFs and between FFLS fire locations and OriFFs were 3362.21 m and 1294.00 m, respectively. The mean RMSEs of the M-HWFP and FFLS products are 1225.52 m and 474.93 m, respectively. The mean MAEs of the M-HWFP and FFLS products are 992.12 m and 387.13 m, respectively. We concluded that the newly proposed MPU-PSA method can extract forest-fire locations at subpixel level, providing higher positioning accuracy of forest fires for their suppression.