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The distribution of diffuse spot energy can be used to sensitively evaluate the aberrations and defects of optical systems. Therefore, the objective and quantitative measurement of diffuse spot parameters is an important means to control the detection quality of space debris detection systems. At present, the existing optical system dispersion measurement method can only judge whether the energy distribution meets the index. However, these methods ca not provide an objective quantitative basis to guide the installation process. To solve this problem, a mathematical simulation model of 3D multi-phase sub-pixel PSF distribution is proposed. According to the relation between the CCD target plane and the theoretical image plane (focal plane, defocus, and deflection), the diffuse spot distribution of the optical system is simulated with different phase combinations. Then, Pearson Correlation Coefficient (PCC) is used to evaluate the matching similarity of the diffuse spot image. The simulation results show that when the PCC is greater than 0.96, the distribution of the two diffuse spots can be identified as matching. This also confirms the accuracy of the proposed PSF model. Then, the focusing deviation of the system being tested can be analyzed according to the phase size of the diffuse spot simulation image. This method can quickly and accurately guide the focal surface installation and testing of the system. Therefore, the purpose of improving the detection accuracy of space debris is achieved. It also provides a quantitative basis for the engineering application of optical detection systems in the future. [ABSTRACT FROM AUTHOR]

We describe the design and performance of a multi‐view 3D display system displaying an adjustable number of views. This uses an 8K panel and a simple eye tracking method to extend the viewing zones over a very large range and provides a multi‐view 3D display with high resolution and smooth motion parallax that gives the viewer good freedom of movement. The results are obtained from a system built around a 32" 8k monitor with a 45 lpi lenticular screen; it gives good image depth with motion parallax to a single user, with a viewing angle more than 60. [ABSTRACT FROM AUTHOR]

In the domain of the Internet of Things (IoT), Optical Camera Communication (OCC) has garnered significant attention. This wireless technology employs solid-state lamps as transmitters and image sensors as receivers, offering a promising avenue for reducing energy costs and simplifying electronics. Moreover, image sensors are prevalent in various applications today, enabling dual functionality: recording and communication. However, a challenge arises when optical transmitters are not in close proximity to the camera, leading to sub-pixel projections on the image sensor and introducing strong channel dependence. Previous approaches, such as modifying camera optics or adjusting image sensor parameters, not only limited the camera's utility for purposes beyond communication but also made it challenging to accommodate multiple transmitters. In this paper, a novel sub-pixel optical transmitter discovery algorithm that overcomes these limitations is presented. This algorithm enables the use of OCC in scenarios with static transmitters and receivers without the need for camera modifications. This allows increasing the number of transmitters in a given scenario and alleviates the proximity and size limitations of the transmitters. Implemented in Python with multiprocessing programming schemes for efficiency, the algorithm achieved a 100% detection rate in nighttime scenarios, while there was a 89% detection rate indoors and a 72% rate outdoors during daylight. Detection rates were strongly influenced by varying transmitter types and lighting conditions. False positives remained minimal, and processing times were consistently under 1 s. With these results, the algorithm is considered suitable for export as a web service or as an intermediary component for data conversion into other network technologies. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A measurement method based on the homography matrix and partial area effect is studied to improve the precision of the visual measurement of the bearing size. Firstly, the camera is calibrated to obtain its distortion coefficient, and the image is corrected for distortion. Secondly, the relationship between the pixel plane and the bearing imaging plane is determined by solving the homography matrix with a chessboard. Finally, the sub-pixel edge contour of the bearing is obtained through the improved partial area effect, and the pixel coordinates are mapped to the world coordinates according to the homography matrix. The feature size is obtained by fitting the mapping points. Experimental results showed that the transformation of the homography matrix is more accurate than the pixel equivalent in the process of converting the pixel size to the actual size. The improved partial area effect sub-pixel edge detection results are better than the partial area effect and Zernike moment. The measurement error for the bearing outer diameter is less than 0.05 mm and is within 0.5 pixel. [ABSTRACT FROM AUTHOR]

A gear integrated error, a combination of individual and composite errors, carries richer information and has long been a key target of classic gear error measurement techniques. However, in the age of intelligent manufacturing, the classic methods for gear integrated error measurement are no longer able to meet the emerging requirements of large-scale gears and real-time online measurement. To address this gap, a novel approach to obtaining the gear integrated error based on GTC−Facet (Gaussian template convolution-Facet) is proposed. This method accurately pinpoints the sub-pixel contour of gears in images, enabling a quick derivation of the gear integrated error curve. From this curve, other individual and composite errors can be analyzed. The gear error information obtained through our method has higher measurement accuracy, achieving a positioning accuracy of 3.6 μm for the gear profile. Moreover, during the measurement process, the measured gear remains unclamped, and the entire measurement process can be completed within 0.35 s, which is much faster than classic methods. Our method meets the demands of online measurements and provides a new avenue for gear error measurement. [ABSTRACT FROM AUTHOR]

There are many pixel-level and sub-pixel-level edge localization methods, but some methods perform poorly in the presence of noise and require image pre-processing or iterative methods to improve the localization accuracy, which increases the computational cost. In this paper, we propose an improved Zernike moment subpixel edge localization algorithm based on the ramp model, and combine the idea of bilinear interpolation method to optimize the selection of parameters in the edge model. Through the validation of several examples, it is found that the algorithm outperforms the compared methods for edge localization of noisy images. After the edge detection, in order to improve the measurement accuracy of polyvinyl chloride (PVC) plates, a parallel line fitting method is proposed to fit the edge points, thus avoiding the interference of extraneous noise points and achieving accurate measurement of PVC plate size. The experiments were carried out for several measurements of the sheet length, and the method was verified to have high measurement accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Video magnification has received increased attention in structural deformation measurement. Current video magnification methods typically chose the Eulerian perspective rather than the Lagrangian, which requires tedious work for tedious parameter adjustment. However, the low computational efficiency is a prominent problem of the Euler-based method due to the complex time domain filtering processing. To address this issue, a novelty motion magnification algorithm, called Euler fast motion detection (EFMD), is proposed in this paper, which can realize real-time measurement with low computation cost. Laboratory verification and field experiments were carried out to verify the accuracy of the algorithm in deck and cable dynamic response measurement. In laboratory experiments, the error of natural frequency measured by the proposed method is within 2%. In the field experiments, the small displacements (about 1.5 mm) can be achieved by the proposed method at a distance greater than 20 m, and the error of the cable vibration is less than 4%. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image super-resolution (SR) reconstruction technology can improve the quality of low-resolution (LR) images. There are many available deep learning networks different from traditional machine learning algorithms. However, these networks are usually prone to poor performance on complex computation, vanishing gradients, and loss of useful information. In this work, we propose a sub-pixel convolutional neural network (SPCNN) for image SR reconstruction. First, to reduce the strong correlation, the RGB mode was translated into YCbCr mode, and the Y channel data was chosen as the input LR image. Meanwhile, the LR image was chosen as the network input to reduce computation instead of the interpolation reconstructed image as used in the super-resolution convolutional neural network (SRCNN). Then, two convolution layers were built to obtain more features, and four non-linear mapping layers were used to achieve different level features. Furthermore, the residual network was introduced to transfer the feature information from the lower layer to the higher layer to avoid the gradient explosion or vanishing gradient phenomenon. Finally, the sub-pixel convolution layer based on up-sampling was designed to reduce the reconstruction time. Experiments on three different data sets proved that the proposed SPCNN performs superiorly to the Bicubic, sparsity constraint super-resolution (SCSR), anchored neighborhood regression (ANR), and SRCNN methods on reconstruction precision and time consumption. [ABSTRACT FROM AUTHOR]

In the domain of the Internet of Things (IoT), Optical Camera Communication (OCC) has garnered significant attention. This wireless technology employs solid-state lamps as transmitters and image sensors as receivers, offering a promising avenue for reducing energy costs and simplifying electronics. Moreover, image sensors are prevalent in various applications today, enabling dual functionality: recording and communication. However, a challenge arises when optical transmitters are not in close proximity to the camera, leading to sub-pixel projections on the image sensor and introducing strong channel dependence. Previous approaches, such as modifying camera optics or adjusting image sensor parameters, not only limited the camera’s utility for purposes beyond communication but also made it challenging to accommodate multiple transmitters. In this paper, a novel sub-pixel optical transmitter discovery algorithm that overcomes these limitations is presented. This algorithm enables the use of OCC in scenarios with static transmitters and receivers without the need for camera modifications. This allows increasing the number of transmitters in a given scenario and alleviates the proximity and size limitations of the transmitters. Implemented in Python with multiprocessing programming schemes for efficiency, the algorithm achieved a 100% detection rate in nighttime scenarios, while there was a 89% detection rate indoors and a 72% rate outdoors during daylight. Detection rates were strongly influenced by varying transmitter types and lighting conditions. False positives remained minimal, and processing times were consistently under 1 s. With these results, the algorithm is considered suitable for export as a web service or as an intermediary component for data conversion into other network technologies.

The localization of the center of the star image formed on a sensor array directly affects attitude estimation accuracy. This paper proposes an intuitive self-evolving centroiding algorithm, termed the sieve search algorithm (SSA), which employs the structural properties of the point spread function. This method maps the gray-scale distribution of the star image spot into a matrix. This matrix is further segmented into contiguous sub-matrices, referred to as sieves. Sieves comprise a finite number of pixels. These sieves are evaluated and ranked based on their degree of symmetry and magnitude. Every pixel in the image spot carries the accumulated score of the sieves associated with it, and the centroid is its weighted average. The performance evaluation of this algorithm is carried out using star images of varied brightness, spread radius, noise level, and centroid location. In addition, test cases are designed around particular scenarios, like non-uniform point spread function, stuck-pixel noise, and optical double stars. The proposed algorithm is compared with various long-standing and state-of-the-art centroiding algorithms. The numerical simulation results validated the effectiveness of SSA, which is suitable for small satellites with limited computational resources. The proposed algorithm is found to have precision comparable with that of fitting algorithms. As for computational overhead, the algorithm requires only basic math and simple matrix operations, resulting in a visible decrease in execution time. These attributes make SSA a fair compromise between prevailing gray-scale and fitting algorithms concerning precision, robustness, and processing time. [ABSTRACT FROM AUTHOR]

A gear integrated error, a combination of individual and composite errors, carries richer information and has long been a key target of classic gear error measurement techniques. However, in the age of intelligent manufacturing, the classic methods for gear integrated error measurement are no longer able to meet the emerging requirements of large-scale gears and real-time online measurement. To address this gap, a novel approach to obtaining the gear integrated error based on GTC−Facet (Gaussian template convolution-Facet) is proposed. This method accurately pinpoints the sub-pixel contour of gears in images, enabling a quick derivation of the gear integrated error curve. From this curve, other individual and composite errors can be analyzed. The gear error information obtained through our method has higher measurement accuracy, achieving a positioning accuracy of 3.6 μm for the gear profile. Moreover, during the measurement process, the measured gear remains unclamped, and the entire measurement process can be completed within 0.35 s, which is much faster than classic methods. Our method meets the demands of online measurements and provides a new avenue for gear error measurement.

Micro bearings were widely used in precision instruments and equipment, and the accuracy of their inner and outer diameter dimensions and coaxiality were closely related to the precision and accuracy of the entire instrument. Therefore, in view of the lack of research on the detection of the inner and outer diameters and geometric tolerances of rolling bearings and the low accuracy, a machine vision-based detection method for the precision and high-efficiency of the geometric quantity of small bearings was proposed. Firstly, high-quality bearing images were obtained through a backlight vision system, and an anisotropic bilateral filtering（ABF） algorithm was used for filtering. Then, the sub-pixel information of the edge of the inner and outer circles was obtained to accurately calculate the boundary position based on the cubic curve model of local area features. Finally, the inner and outer diameters of the measured bearings and their coaxiality were calculated, and the experimental data was analyzed to judge whether the bearings were qualified or not, which verified the feasibility and superiority of the method. The experimental results show that the accuracy of this method can reach 3 μm, realize non-contact, high-precision, intelligent and fast target detection of part size. The method has high detection efficiency and good accuracy, and can detect multiple indicators at the same time. The conclusion is stable and reliable, and both accuracy and precision can meet the requirements of industrial detection. [ABSTRACT FROM AUTHOR]

Understanding and mapping multiple cropping patterns(MCP) is important to meet global food demand. Due to the random crops in smallholder farming, it is challenging to obtain phenology and location of MCP. Phenology exhibits stable rhythmic variation, and vegetation index(VI) forms time-series profiles reflecting rhythmic variations. Since most crops have different growth cycles, time-series are used as a fingerprint to distinguish multiple crops in a specific area. Moderate Resolution Imaging Spectroradiometer(MODIS) normalized-VI(NDVI) data were used to construct time-series, but different land cover types were often mixed in a 250 m MODIS pixel. Time-series were explored as input of spectral unmixing algorithms that could capture the spatial distribution of MCP and subpixels at large areas. Time-series profiles of MCP were extracted based on N-finder algorithm(N-FINDR). Considering the mixed-pixel, fully constrained least-squares(FCLS) was used to extract the spatial distribution of MCP. The accuracy of the algorithm was verified using high spatial resolution images with an overall accuracy of 85.65%. The results show that the algorithm simultaneously extracts MCP and spatial distributions at large scales. [ABSTRACT FROM AUTHOR]

The wind turbine works in a harsh mining environment, so the blade surface of the wind turbine is susceptible to granular dust pollution, wind, and sand erosion, which will cause cracks, sand holes, and other damage to the blade, also the manual damage detection is inefficient and difficult to operate. Based on the sub-pixel map matching technology, the image is segmented and matched. The sub-pixel gradient matching algorithm is used to match the image to be detected with the original image to find the minimum matching value, and when the calculation result is greater than the specified threshold, the paddle is considered to be damaged, so as to complete the surface damage detection of the paddle. The research results show that the method can effectively improve the accuracy of surface damage detection of wind turbine blades, and the computational accuracy reaches sub-pixel level. The findings can provide a theoretical basis for the surface damage detection of wind turbine blades. [ABSTRACT FROM AUTHOR]

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The high penetration depth of neutrons through many metals and other common materials makes neutron imaging an attractive method for non-destructively probing the internal structure and dynamics of objects or systems that may not be accessible by conventional means, such as X-ray or optical imaging. While neutron imaging has been demonstrated to achieve a spatial resolution below 10 μm and temporal resolution below 10 μs, the relatively low flux of neutron sources and the limitations of existing neutron detectors have, until now, dictated that these cannot be achieved simultaneously, which substantially restricts the applicability of neutron imaging to many fields of research that could otherwise benefit from its unique capabilities. In this work, we present an attenuation modeling approach to the quantification of sub-pixel dynamics in cyclic ensemble neutron image sequences of an automotive gasoline direct injector at a 5 μs time scale with a spatial noise floor in the order of 5 μm. [ABSTRACT FROM AUTHOR]

In this paper, we correct the necessary image algorithm such as sub‐pixel rendering (SPR) and de‐mura (DMR) for under‐display camera (UDC) area on AMOLED display. Because the wiring of TFT pixel circuit and structure are different between UDC and normal area, it results in color shifts and brightness nonuniformity in the UDC area. The new correction algorithms we propose are based on the existing architecture, distinguish the position coordinates of the two areas, and give a new correction method for the UDC area. The new algorithms have been integrated into the new DDIC NT37701 and have been mass‐produced for smartphone products. [ABSTRACT FROM AUTHOR]

【Background】 Latent heat flux (LE) impacts carbon dynamics and energy budget in the terrestrial ecosystem. Its accurate estimation is essential to understanding energy budget and surface-atmosphere interactions on the Earth surface. Satellite imageries have been used to calculate LE at global scale, but most of them overlooked the impact of vegetation diversity at pixel scale. 【Objective】 Considering that carbon flow and heat flux in ecosystems are closely related through photosynthesis and that plants are diverse, calculating LE using sub-pixel data should improve its accuracy, especially for complex ecosystems. 【Method】 Here, a dynamic vegetation model, LPJ-GUESS, was proposed to improve the satellite-based PT-hybrid algorithm, by using a parameter fv to represent the proportions of different PFTs in a pixel. 【Result】 Validation against ground-true data obtained from 49 flux tower sites in the world showed that the proposed model increased the square of correlation coefficient from 0.63 to 0.74, and reduced the root mean square error from 17.1 W/m2 to 11.7 W/m2. Estimation of the global LE from 1982 to 2014 revealed the proposed method was more effective for low latitude areas than for other areas, and that for areas on the same latitude, the LE showed complicated spatial distribution. 【Conclusion】 Sub-pixel method was coupled with the LE algorithm for the first time to calculate surface latent heat flux. It significantly improved the accuracy and can be used to estimate spatiotemporal distribution of LE at different scales.

The localization of the center of the star image formed on a sensor array directly affects attitude estimation accuracy. This paper proposes an intuitive self-evolving centroiding algorithm, termed the sieve search algorithm (SSA), which employs the structural properties of the point spread function. This method maps the gray-scale distribution of the star image spot into a matrix. This matrix is further segmented into contiguous sub-matrices, referred to as sieves. Sieves comprise a finite number of pixels. These sieves are evaluated and ranked based on their degree of symmetry and magnitude. Every pixel in the image spot carries the accumulated score of the sieves associated with it, and the centroid is its weighted average. The performance evaluation of this algorithm is carried out using star images of varied brightness, spread radius, noise level, and centroid location. In addition, test cases are designed around particular scenarios, like non-uniform point spread function, stuck-pixel noise, and optical double stars. The proposed algorithm is compared with various long-standing and state-of-the-art centroiding algorithms. The numerical simulation results validated the effectiveness of SSA, which is suitable for small satellites with limited computational resources. The proposed algorithm is found to have precision comparable with that of fitting algorithms. As for computational overhead, the algorithm requires only basic math and simple matrix operations, resulting in a visible decrease in execution time. These attributes make SSA a fair compromise between prevailing gray-scale and fitting algorithms concerning precision, robustness, and processing time.

The paper presents a method for mapping fluvial gravel bars based on Sentinel-2 and Landsat imagery. The proposed method therefore uses spectral signal mixture analysis (SSMA) because its results allow the development of land cover fraction maps for surface water, gravel, and vegetation. The method is validated on a spatially heterogeneous mountainous area in the upper Soča river basin in north-west Slovenia, Central Europe. Unmixing results in highly accurate fraction maps with MAE of around 0.1. Gravel fractions are mapped the most accurately, indicating that the approach can be used successfully for fluvial gravel bar mapping. Endmember sets selected automatically perform slightly worse (MAE higher by at most 0.05) than sets selected manually based on high resolution reference data. Both Sentinel-2 and Landsat imagery can be used for accurate mapping with differences between the two remote sensing systems within 0.05 MAE. For the study area, the SSMA-based soft classification method is more accurate for land cover mapping than a Spectral Angle Mapping-based hard classification. The method is promising for an effective use in other cases where highly accurate subpixel information is needed, because it is able to detect small-scale changes that could go unnoticed with hard classification mapping.

binocular photogrammetry vibration measurement method for considering the radial and tangential distortion parameters calibration by using the Levenberg-Marquardt algorithm based nonlinear optimization was proposed. As an example,the modal frequency identified of a wind turbine blade. The vibration images sequence of the blade was acquired synchronously by two cameras and the target was segmented by adaptive binary dynamic threshold method,the direction with fastest grayscale gradient of the coding point as the edge,the circle curve was fitted by the least squares and iterative method,and the precise suppixel central coordinates of the coding point in the dynamic consecutive images were obtained by the gray square centroid method.Each coding point is similar to a small "displacement transducer"that reflects the blade’s time domain vibration response.Finally,the blade’s natural modal frequency was obtained from the time domains response,and compared with the results of the finite element and Laser Doppler Vibrometry,the relative error of vibration frequencies less than 5%,which verified the reliability and effectiveness of binocular photogrammetry method.

The rich spectral data found in the hyperspectral data cube make them useful in real-world applications, such as target detection. Target pixels detection among an unknown background such as ground objects from hyperspectral data cube is of great interest for remote sensing community. The commonly used hyperspectral target detection methods often overlook the problem of prior knowledge of the target and could reduce the efficiency of these methods. It has to be noted that the spatial resolution of the hyperspectral data cube is usually limited; therefore, the sub-pixel targets only occupy part of the pixel. The replacement signal model is an essential model for sub-pixel targets. In this study, we developed a revised replacement signal model based on an automatic target generation procedure for improving hyperspectral sub-pixel target detection using the HyMap data cube. The effects of various real targets on hyperspectral data cube are evaluated to obtain consistent results. In experiments with seven targets, the proposed method achieves the average area under the ROC curve of 99%. Comparison results illustrated that the proposed method has competitive target detection performance in comparison with other state-of-the-art methods.

Due to the existence of high-temperature and high-pressure zinc vapor in the gas metal arc welding (GMAW) process of galvanized steel, it is difficult to achieve accurate and real-time detection of weld defects, which brings great challenges to robotic welding manufacturing. In order to realize the automatic classification and prediction of weld defects, a method of galvanized steel weld defects detection based on active visual sensing and machine learning was proposed. First, Gabor filter was used to remove arc light, noise, and other interference signals to obtain the weld centerline image. Then, according to the five different weld defects of the geometric and spatial distribution characteristics in weld centerline image were analyzed with the principle of sub-pixel level. Finally, using the eight feature parameters extracted from the weld feature points, a variable learning rate and momentum factor back propagation (VL-MFBP) neural network model was designed. The model introduced a variable learning rate and momentum factor to quickly find the optimal solution in a short time, its performance was better than traditional machine learning algorithms. The experimental results show that the accuracy of weld defect recognition is 98.15%, and the average processing time of a single image is only 183.74 ms. [ABSTRACT FROM AUTHOR]

Large fractions of human-altered lands are working landscapes where people and nature interact to balance social, economic, and ecological needs. Achieving these sustainability goals requires tracking human footprints and landscape disturbance at fine scales over time—an effort facilitated by remote sensing but still under development. Here, we report a satellite time-series analysis approach to detecting fine-scale human disturbances in an Ohio watershed dominated by forests and pastures but with diverse small-scale industrial activities such as hydraulic fracturing (HF) and surface mining. We leveraged Google Earth Engine to stack decades of Landsat images and explored the effectiveness of a fuzzy change detection algorithm called the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) to capture fine-scale disturbances. BEAST is an ensemble method, capable of estimating changepoints probabilistically and identifying sub-pixel disturbances. We found the algorithm can successfully capture the patterns and timings of small-scale disturbances, such as grazing, agriculture management, coal mining, HF, and right-of-ways for gas and power lines, many of which were not captured in the annual land cover maps from Cropland Data Layers—one of the most widely used classification-based land dynamics products in the US. For example, BEAST could detect the initial HF wellpad construction within 60 days of the registered drilling dates on 88.2% of the sites. The wellpad footprints were small, disturbing only 0.24% of the watershed in area, which was dwarfed by other activities (e.g., right-of-ways of utility transmission lines). Together, these known activities have disturbed 9.7% of the watershed from the year 2000 to 2017 with evergeen forests being the most affected land cover. This study provides empirical evidence on the effectiveness and reliability of BEAST for changepoint detection as well as its capability to detect disturbances from satellite images at sub-pixel levels and also documents the value of Google Earth Engine and satellite time-series imaging for monitoring human activities in complex working landscapes. [ABSTRACT FROM AUTHOR]

The high penetration depth of neutrons through many metals and other common materials makes neutron imaging an attractive method for non-destructively probing the internal structure and dynamics of objects or systems that may not be accessible by conventional means, such as X-ray or optical imaging. While neutron imaging has been demonstrated to achieve a spatial resolution below 10 μm and temporal resolution below 10 μs, the relatively low flux of neutron sources and the limitations of existing neutron detectors have, until now, dictated that these cannot be achieved simultaneously, which substantially restricts the applicability of neutron imaging to many fields of research that could otherwise benefit from its unique capabilities. In this work, we present an attenuation modeling approach to the quantification of sub-pixel dynamics in cyclic ensemble neutron image sequences of an automotive gasoline direct injector at a 5 μs time scale with a spatial noise floor in the order of 5 μm.

In order to solve the problem that the traditional stripe center extraction algorithm is sensitive to material and noise, an adaptive structured light stripe center extraction algorithm was used to extract the fringe sub-pixel coordinates. The algorithm first preprocesses the image, extracts the region of interest of the stripe image by using the image mask operation, eliminates noise interference through the adaptive convolution template, and obtains the pixel sets of the stripe area cross-sectional width and center coordinates. Secondly, according to the pixel set of the central coordinates, the initial coordinate value of the stripe center was calculated by the quadratic weighted gray centroid method, which will be used as the seed point for regional growth operation, then combined with principal component analysis to decompose the characteristic matrix, and finally the sub- pixel coordinate point of the center of the linear structured light was obtained. The results show that the center sub-pixel coordinates of the structured light stripe can be effectively and quickly obtained by this algorithm. Compared with the gray-scale barycenter method, the extraction results of the algorithm in this paper are less volatile and have a relatively small standard error. The extraction speed is nearly 4 times higher than that of the Steger method based on Hessian matrix, which meets the real-time requirements of the industrial measurement system. The proposed algorithm in this paper has high extraction accuracy, good robustness, low computational complexity, and high real-time performance, which provides nice accuracy guarantee for the subsequent 3-D vision measurement system. [ABSTRACT FROM AUTHOR]

The paper presents a method for mapping fluvial gravel bars based on Sentinel-2 and Landsat imagery. The proposed method therefore uses spectral signal mixture analysis (SSMA) because its results allow the development of land cover fraction maps for surface water, gravel, and vegetation. The method is validated on a spatially heterogeneous mountainous area in the upper Soča river basin in north-west Slovenia, Central Europe. Unmixing results in highly accurate fraction maps with MAE of around 0.1. Gravel fractions are mapped the most accurately, indicating that the approach can be used successfully for fluvial gravel bar mapping. Endmember sets selected automatically perform slightly worse (MAE higher by at most 0.05) than sets selected manually based on high resolution reference data. Both Sentinel-2 and Landsat imagery can be used for accurate mapping with differences between the two remote sensing systems within 0.05 MAE. For the study area, the SSMA-based soft classification method is more accurate for land cover mapping than a Spectral Angle Mapping-based hard classification. The method is promising for an effective use in other cases where highly accurate subpixel information is needed, because it is able to detect small-scale changes that could go unnoticed with hard classification mapping. [ABSTRACT FROM AUTHOR]

• Proposed the framework of the sub-pixel image registration. • Matching point pairs will be reduced. • Get more anti-interference matches than other methods. Due to the influence of various conditions and uncertain difficulties for remote sensing images, image registration is still a challenging task. Considering the registration accuracy of pixel level cannot satisfy the requirements of some related applications, we put forward a sub-pixel image registration method based on speeded up robust features and M-estimator sample consensus. It mainly involves four aspects. At first, extract sub-pixel level feature points based on SURF algorithm. Next, obtain the initial matching point pairs based on Sum of Squared Difference and Fast Library for Approximate Nearest Neighbors algorithms. And then, remove the mismatched pair of points based on M-estimator sample consensus algorithm. Finally, calculate geometric transformation matrix based on purified matching points to reach sub-pixel accuracy image registration. Experimental results for several remote sensing image pairs with displacement, noise added, rotation, and different sensors, times and sizes, show that the proposed method can get more anti-interference matches than other methods, and take smaller computational cost in registration process. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the effect of increasing DEM spatial resolution on the assessment of the morphometric characteristics of waterways, as well as analysis and modeling of it by using RS and GIS techniques. In this study, which was carried out in the south of Darab city DEM 90 m (as one of the most usable data in waterway modeling), increase spatial resolution of DEM attraction algorithm in neighboring pixels with two models including: touching and quadrant neighboring models to estimate the value of sub-pixels. After manufacturing output images for sub pixels in 2, 3 and 4 scales with different neighborhoods, the best scale with the most appropriate type of neighborhood was determined using ground control points (270 points); then, the values of RMSE were calculated for them. The results showed that with using the Attraction model, the accuracy of the output of images was improved and the spatial resolution of them was increased. Among scales with different neighborhoods, 3 scales and quadrant neighboring model exhibited the most accuracy by the lowest value of RMSE for the DEM 90 meter. Evaluation of waterways morphometric features showed that DEM extracted from attraction algorithm had more ability and accuracy in waterways extraction, Extraction of morphometric complications, and information in the study area.

In order to solve the problems of low efficiency and long running time caused by the traditional Zernike moment method for convolution calculation of the whole image, this paper combines the canny detection algorithm with the Zernike moment method. First, the canny edge detection algorithm, which combined with the Otsu threshold method, is used to extract the pixel edge of the image. Then an improved Hough transform method is used to fit the geometric edge in the image. Based on this, the Zernike moment method is applied to realize sub-pixel positioning of images. The algorithm improves the deficiencies of direct sub-pixel detection, improving accuracy and reducing running time. To verify the effectiveness of the proposed algorithm, the algorithm is applied to the dimension measurement experiment of T-type guide way. The results clearly show that the algorithm is superior to the traditional algorithm in accuracy. [ABSTRACT FROM AUTHOR]

Automatic warping machine is a kind of machine that can cross reeds quickly in textile industry. Generally, the speed of warping machines is lower than 120 r/min at present. In order to improve the speed of the high-speed warping machine, this paper designs a fast reed positioning algorithm, which includes image preprocessing, 1 D sub-pixel positioning of reed edges, edge line fitting and positioning of reeds. The tests and applications show that, with the decrease of hardware cost, our method can guarantee the working period within 30 ms and the measurement error within ±0.02 mm, and it ensures the stable operation of the high-speed warping machine which is over 140 r/min. [ABSTRACT FROM AUTHOR]

Evergreen forest plays a particular role in biodiversity, carbon sequestration, and soil and water conservation, and the spatial and temporal evolutions of evergreen forest complexly interact with climate change. However, it is difficult for the existing remote sensed data to meet both demands of large scale and high accuracy. To solve this problem, we developed a time-series FEVC-CV method by combining the fractional evergreen forest cover model (FEVC) with the coefficient of variation (CV) at sub-pixel scales. This method using the normalized difference vegetation index (NDVI) dataset from the Moderate Resolution Imaging Spectroradiometer (MODIS) product to meet the needs of large-scale mapping. Considering the severe mixed-pixel problem and spatial heterogeneity of the study area, the improved dimidiate pixel model was used to extract the fractional evergreen forest cover at sub-pixel scales by introducing a new variable the intra-annual NDVI minimum value (NDVI ann-min) and dividing grid units. Meanwhile, the CV of the time-series NDVI highlighted the time series fluctuation stability of evergreen forest compared with other vegetation types, such as deciduous forest and continuous crop. Therefore, the intra-annual time-series CV (CV ai) and the CV of the continuous crop key phenology (CV kp) period were used to eliminate the interferences from other vegetation types, which were extremely admixed with low-coverage evergreen forest. We then verified the accuracy of the algorithm using 2-m resolution Gaofen-1 images. The results revealed that the overall accuracy of our algorithm exceeded 90%, with a root mean square error (RMSE) of cover fraction of around 10%. In addition, the mean relative error (MRE) indicated that the extraction accuracy of evergreen forest in non-urban areas was superior to the accuracy in urban areas. The results show that the algorithm achieved fairly high accuracy in detecting evergreen forest, including evergreen trees in urban areas, at a large scale. [ABSTRACT FROM AUTHOR]

In this paper, an improved sub-pixel edge detection algorithm combining coarse and precise location is proposed. The algorithm fully considers the 8-neighborhood pixel information and keeps the Roberts operator's advantages of high location accuracy and fast speed. Meanwhile, it can effectively suppress noise and obtain better detection results. In order to solve the problem of low efficiency of the Zernike moment method in threshold selection, the Otsu's method is introduced to achieve accurate sub-pixel edge location. The experimental results show that the proposed algorithm effectively improves the detection efficiency and the detection accuracy. [ABSTRACT FROM AUTHOR]

A novel application-oriented sub-pixel measurement method utilizing an active contour model with image resampling is presented for a critical dimension (CD) measurement of pixel patterns of a flat-panel thin film transistor crystal display (TFT-LCD) and organic light-emitting diode display. In modern manufacturing of flat panel display, optical measurement of each dimension of pixel patterns has a critical role in the process control, and the resolution of a measurement system becomes a limitation of the manufacturing process. Several methods have been developed to overcome this limit at the image post processing stage, but as the level of pixel integration and manufacturing throughput are increasing rapidly, more robust and effective inspection approach is required. In this paper, a novel sub-pixel level edge detection algorithm with active contour method and fast pixel resampling is proposed for micron scale CD measurement, and its advantages on measurement repeatability and noise handling are presented, along with the results of various industrial sample measurements and comparison with conventional critical dimension measurement algorithms. [ABSTRACT FROM AUTHOR]

Ultrasound vascularity imaging provides important information for differential diagnosis of tumors. Peak-hold (PH) is a useful technique for precisely imaging small vessels by selecting a maximum brightness in each pixel through the frames obtained sequentially. To use PH successfully one needs motion compensation to reduce image blur, but out-of-plane motion cannot be avoided. To address this problem, we developed a sub-pixel motion-tracking method with out-of-plane motion detection (OPMD). It is a combination of the sum of the absolute differences (SAD) method and the Kanade-Lucas-Tomasi method and can be accurately applied to various motions. The value from OPMD (γ) is defined as a statistical value obtained from the distribution of residual values in the SAD procedure with the obtained frames. The value is ideally 0, and the frames having large γ are removed from the PH procedure. The accuracy of the proposed tracking method was found by a simulation study to be approximately 20 μm. We also found, through a phantom experiment, that the value of γ sensitively increased enough to detect out-of-plane motion. Most important, γ begins to increase before tracking errors occur. This suggests that OPMD can be used to predict tracking errors and effectively remove frames from the PH procedure. An in vivo experiment with a rabbit showed that the PH image obtained with motion tracking clearly revealed peripheral vessels that were blurred in the PH image obtained without motion tracking. We also found that the image quality becomes better when OPMD was used to remove frames including out-of-plane motion. [ABSTRACT FROM AUTHOR]

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes.

Aiming at the measuring difficulty of small modulus gear,a measuring method of gear tooth profile total deviation based on machine vision is proposed. On the basis of building measurement vision system,firstly,processing gear digital image with Gaussian filter and edge extraction,then the sub-pixel edge of gear tooth profile is obtained. Because the influence of gear center to tooth profile total deviation is relatively sensitive,by means of radius constraint data to fit round hole center in calibration disc,more accurate center position is obtained. Finally,on the basis of establishing mathematical model to realize tooth profile total deviation measurement. Through the experiment,the measurement result of measuring method and three coordinate measuring machine have higher consistency,the maximum absolute error is less than 3 μm,and the effect of gear position center error on the tooth profile total deviation is one quarter of its error value. The experimental results show that adopting measuring method can accurately reflect the gear tooth profile total deviation,and meeting the requirements of measurement precision reach micron level.