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Wide azimuth seismic data play an important role in deep reservoir prediction. According to the Paleogene clastic rock reservoir prediction, the high‐density and wide azimuth 3D seismic data acquisition of ocean bottom nodes was first carried out in a Chinese offshore oilfield in 2019. After high precision amplitude‐preserving processing, we obtained the high‐quality wide azimuth gathers. However, the research on anisotropy and reservoir prediction using wide azimuth seismic data mainly focuses on carbonate and bedrock intervals, which is not suitable for clastic rock reservoir prediction. Therefore, this paper innovatively proposes a clastic rock reservoir prediction method, which studies prestack reservoir prediction in the ray parameter domain based on wide azimuth ocean bottom node seismic data. Based on the azimuth gathers, we can obtain elastic parameters through prestack amplitude versus offset inversion, which is used to characterize the reservoir, so it is significant to obtain high precision elastic parameters in order to get highly reliable reservoir prediction results. In this paper, we develop an amplitude versus offset inversion method based on the Bayesian theory in ray parameter domain, the output of which is density, P‐wave impedance and Vp/Vs. These elastic parameters have high precision, and density data are valuable input for reservoir characterization because they are sensitive to the lithology of clastic rock reservoir at different orientations. In ray parameter domain inversion, the ray path of seismic wave propagation is considered polyline, which is more consistent with the actual situation; thus, extracted amplitudes of P gathers used in inversion are more accurate. In addition, the reflection coefficient formula in ray parameter domain has higher precision when the incident angle is large. The inversion based on the Bayesian theory can improve the stability of the inversion. Test on the actual data shows that the result of ray parameter domain inversion with a Bayesian scheme is more accurate, stable and reliable. Based on the above high precision density inversion results, an innovative wide azimuth data reservoir prediction technology based on elliptical short‐axis fitting was proposed. The actual prediction of the deep reservoir in the Bohai oilfield shows that sand thickness fitting prediction results in the short axis can best match the actual drilling sandstone thickness. The coincidence rate is 86% and the short‐axis fitting results are more in agreement with geological laws. Theoretical research and practical applications have shown that this method is feasible and effective, with high prediction accuracy, computational efficiency and strong application value. [ABSTRACT FROM AUTHOR]

In the field of visual measurement, camera calibration is the first step and an important step in determining the accuracy of the measurement. Achieving rapid and accurate camera calibration has been a significant focus of research among scholars. Therefore, a high-precision camera calibration framework based on ellipse eccentricity compensation is proposed. In the first step, the Canny algorithm is used to detect ellipse edges. In the second step, a high-precision fitting of the ellipse is accomplished by a weighted fitting method. In the third step, the initial calibration parameters are calculated by using the coordinates of the elliptical center. Subsequently, the eccentricity deviation of the ellipse is compensated based on the initial calibration parameters. The updated coordinates are then used to recalculate the calibration parameters. This iterative process is repeated using an improved swarm intelligence optimization algorithm until the error is below the threshold or the number of iterations is reached. Simulations and experiments are used to verify the proposed method. The results show that the proposed method has high accuracy and stability, and can be widely used in engineering. [ABSTRACT FROM AUTHOR]

Monitoring fetal growth in utero is crucial to anomaly diagnosis. However, current computer-vision models struggle to accurately assess the key metrics (i.e., head circumference and occipitofrontal and biparietal diameters) from ultrasound images, largely owing to a lack of training data. Mitigation usually entails image augmentation (e.g., flipping, rotating, scaling, and translating). Nevertheless, the accuracy of our task remains insufficient. Hence, we offer a U-Net fetal head measurement tool that leverages a hybrid Dice and binary cross-entropy loss to compute the similarity between actual and predicted segmented regions. Ellipse-fitted two-dimensional ultrasound images acquired from the HC18 dataset are input, and their lower feature layers are reused for efficiency. During regression, a novel region of interest pooling layer extracts elliptical feature maps, and during segmentation, feature pyramids fuse fieldlayer data with a new scale attention method to reduce noise. Performance is measured by Dice similarity, mean pixel accuracy, and mean intersectionover- union, giving 97.90%, 99.18%, and 97.81% scores, respectively, which match or outperform the best U-Net models.

The rapid development of augmented reality (AR), 3D reconstruction, simultaneous localization and mapping (SLAM), and autonomous driving requires off-the-shelf camera calibration solutions that are adaptable to cameras of different configurations in different complex scenarios. To this end, we propose a generic, robust, and accurate camera calibration framework, called Meta-Calib, by using single or multiple novel designed ArUco-encoded meta-board(s), which is dedicated to estimate accurate camera intrinsic parameters and extrinsic transformations of different multi-camera configurations. The ArUco calibration board has been redesigned to facilitate learning-based robust detection and obtain higher precision control point coordinates, which is termed the meta-board. This completely replaces the widely-used chessboard based on the corner extraction scheme to greatly alleviate the impact of image distortion on control points, especially when it is located at the boundary area of the fish-eye camera. A robust two-stage deep learning detection strategy is applied to reliably localize the ArUco-encoded inner coding region of the meta-board followed by identifying two categories of circular shapes representing "0" and "1" encoded in the ArUco pattern for decoding and orientation determination. The center points of circular shapes on the meta-board in the distorted image taken under the perspective view can be approximated through elliptical fitting with contour edges. The deviation between the fitting center points and ground-truth can be greatly suppressed when the refined sub-pixel contour edges extracted on the original image are projected to the orthographic projection view based on the camera intrinsic parameters, distortion coefficients and the prior information of the meta-board. Based on this observation, we propose a systematic iterative refinement approach to achieve the high-precision intrinsic calibration of a camera. This process involves improving the estimation of camera intrinsic parameters and fitting the center control points of circular shapes on the meta-boards in an iterative manner. The progressive nature of our approach permits reliably calibrate large distortion camera models under the presence of noisy measurements, which ensures good convergence. In addition, we also propose a graph-based multi-camera extrinsic calibration method via the corrected control points to reliably estimate both the relative poses of the meta-boards and cameras in the multi-camera system. The proposed method is not constrained by the number of cameras and meta-boards used, which makes our strategy accessible even with inflexible computer vision experts. Furthermore, we have derived the mathematical form for computing the covariance of the extrinsic transformation, which makes it possible to evaluate the uncertainty of the calibration results. Extensive experiments on a large number of both real and synthetic datasets, including perspective, fish-eye, and multiple overlapping cameras, are performed to prove the effectiveness and robustness of the developed Meta-Calib calibration framework. [ABSTRACT FROM AUTHOR]

Monitoring fetal growth in utero is crucial to anomaly diagnosis. However, current computer‐vision models struggle to accurately assess the key metrics (i.e., head circumference and occipitofrontal and biparietal diameters) from ultrasound images, largely owing to a lack of training data. Mitigation usually entails image augmentation (e.g., flipping, rotating, scaling, and translating). Nevertheless, the accuracy of our task remains insufficient. Hence, we offer a U‐Net fetal head measurement tool that leverages a hybrid Dice and binary cross‐entropy loss to compute the similarity between actual and predicted segmented regions. Ellipse‐fitted two‐dimensional ultrasound images acquired from the HC18 dataset are input, and their lower feature layers are reused for efficiency. During regression, a novel region of interest pooling layer extracts elliptical feature maps, and during segmentation, feature pyramids fuse field‐layer data with a new scale attention method to reduce noise. Performance is measured by Dice similarity, mean pixel accuracy, and mean intersection‐over‐union, giving 97.90%, 99.18%, and 97.81% scores, respectively, which match or outperform the best U‐Net models. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Donghua University (English Edition) is the property of Journal of Donghua University Editorial Board 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.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology 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.)

Cone-beam computed tomography (CBCT) is widely used in dental imaging, small animal imaging, radiotherapy, and non-destructive industrial inspection. The quality of CBCT images depends on the precise knowledge of the CBCT system's alignment. We introduce a distinct procedure, 'precision alignment loop (PAL)', to calibrate any CBCT system with a circular trajectory. We describe the calibration procedure by using a line-beads phantom, and how PAL determines the misalignments from a CBCT system. PAL also yields the uncertainties in the simulated calibration to give an estimate of the errors in the misalignments. From the analytical simulations, PAL can precisely obtain the source-to-rotation axis distance, and the geometric center G, 'the point in z -axis meets the detector', where the z -axis is coincident with the line from the x-ray source that intersects the axis of the rotation orthogonally. The uncertainties of three misalignment angles of the detector are within ±0.05°, which is close to ±0.04° for the results of Yang et al (2017 Med. Phys. 44 1692), but our method is easy and simple to implement. Our distinct procedure, on the other hand, yields the calibration of a micro-CT system and an example of reconstructed images, showing our calibration method for the CBCT system to be simple, precise, and accurate. [ABSTRACT FROM AUTHOR]

Edge detection is one of the most fundamental and critical operations in image analysis and computer vision. This paper proposes an adaptive edge detection method that combines multi-scale closest neighbor operator with grid partition technique (MSCNOGP). The multi-scale closest neighbor operator can be used to remove both noisy data and small area textures, while the grid partition technique can improve the precision of the edges. By utilizing the concepts of both twin edge pixel and grid divergence, the resulting edges can be further improved. Compared with prevailing traditional methods, the MSCNOGP method achieves both the best precision and almost the best visual effect, where both line segment fitting and ellipse fitting are applied for testing different edge detection methods. The performance on the F-measure score of the MSCNOGP method is much better than those of prevailing traditional methods. [ABSTRACT FROM AUTHOR]

A growing societal interest exists in the application of lidar technology to monitor forest resource information and forestry management activities. This study examined the possibility of estimating the diameter at breast height (DBH) of two tree species, Pinus koraiensis (PK) and Larix kaempferi (LK), by varying the number of terrestrial laser scanning (TLS) scans (1, 3, 5, 7, and 9) and DBH estimation methods (circle fitting [CF], ellipse fitting [EF], circle fitting with RANSAC [RCF], and ellipse fitting with RANSAC [REF]). This study evaluates the combination that yields the highest estimation accuracy. The results showed that for PK, the lowest RMSE of 0.97 was achieved when REF was applied to the data from nine scans after noise removal. For LK, the lowest RMSE of 1.03 was observed when applying CF to the data from seven scans after noise removal. Furthermore, ANOVA revealed no significant difference in the estimated DBH from nine scans when more than three scans were used for CF and RCF and more than five for EF and REF. These results are expected to be useful in establishing efficient and accurate DBH estimation plans using TLS for forest resource monitoring. [ABSTRACT FROM AUTHOR]

Cell segmentation from fluorescent microscopy images plays an important role in various applications, such as disease mechanism assessment and drug discovery research. Exiting segmentation methods often adopt image binarization as the first step, through which the foreground cell is separated from the background so that the subsequent processing steps can be greatly facilitated. To pursue this goal, a histogram thresholding can be performed on the input image, which first applies a Gaussian smoothing to suppress the jaggedness of the histogram curve and then exploits Rosin's method to determine a threshold for conducting image binarization. However, an inappropriate amount of smoothing could lead to the inaccurate segmentation of cells. To address this crucial problem, a multi-scale histogram thresholding (MHT) technique is proposed in the present paper, where the scale refers to the standard deviation of the Gaussian that determines the amount of smoothing. To be specific, the image histogram is smoothed at three chosen scales first, and then the smoothed histogram curves are fused to conduct image binarization via thresholding. To further improve the segmentation accuracy and overcome the difficulty of extracting overlapping cells, our proposed MHT technique is incorporated into a multi-scale cell segmentation framework, in which a region-based ellipse fitting technique is adopted to identify overlapping cells. Extensive experimental results obtained on benchmark datasets show that the new method can deliver superior performance compared to the current state-of-the-arts.

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering 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.)

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

Atom interferometry-based sensors have demonstrated remarkable precision, making them attractive for diverse applications compared to traditional sensors. However, practical experiments inevitably encounter noise, which limits precision. We investigate the performance of two commonly used methods, ellipse fitting (EF) and fringe fitting (FF), for differential phase estimation in a dual atom interferometer under various noise conditions. We comprehensively analyze the efficacy of these methods for each type of Gaussian noise, including phase noise, amplitude noise, and offset noise. Using a colored noise model, we investigate the Allan deviation results. Our investigations will offer valuable insights for future quantum sensing based on dual interferometers.

The comprehensive utilization of wood is the main goal of log cutting, but knot defects increase the difficulty of rationally optimizing cutting. Due to the lack of real shape data of knot defects in logs, it is difficult for detection methods to establish a correlation between signal and defect morphology. An image-processing method is proposed for knot inversion based on distance regularized level set segmentation (DRLSE) and spatial vertex clustering, and with the inversion of the defects existing relative board position in the log, an inversion model of the knot defect is established. First, the defect edges of the top and bottom images of the boards are extracted by DRLSE and ellipse fitting, and the major axes of the ellipses made coplanar by angle correction; second, the coordinate points of the top and bottom ellipse edges are extracted to form a spatial straight line; third, to solve the intersection dispersion of spatial straight lines and the major axis plane, K-medoids clustering is used to locate the vertex. Finally, with the vertex and the large ellipse, a 3D cone model is constructed which can be used to invert the shape of knots in the board. The experiment was conducted on ten defective larch boards, and the experimental results showed that this method can accurately invert the shapes of defects in solid wood boards with the advantages of low cost and easy operation. [ABSTRACT FROM AUTHOR]

Simple Summary: Mastitis is one of the most serious diseases in dairy husbandry, and its timely detection is critical for improving the efficiency of treatment and reducing breeding risks. However, the traditional "contact-based" manual detection method is complex and unsuitable for large-scale production practices. In recent years, the rapid development of deep learning technology has brought new possibilities. We present a novel approach for cow mastitis detection based on thermal infrared image segmentation technology. By automatically segmenting the key parts of the cow's eyes and udders in the thermal infrared image, it becomes possible to determine mastitis based on temperature. The results show that this method can meet the requirements of the timely and accurate detection of cow mastitis in large-scale dairy farms. Thermal infrared technology is utilized for detecting mastitis in cows owing to its non-invasive and efficient characteristics. However, the presence of surrounding regions and obstacles can impede accurate temperature measurement, thereby compromising the effectiveness of dairy mastitis detection. To address these problems, we proposed the CLE-UNet (Centroid Loss Ellipticization UNet) semantic segmentation algorithm. The algorithm consists of three main parts. Firstly, we introduced the efficient channel attention (ECA) mechanism in the feature extraction layer of UNet to improve the segmentation accuracy by focusing on more useful channel features. Secondly, we proposed a new centroid loss function to facilitate the network's output to be closer to the position of the real label during the training process. Finally, we used a cow's eye ellipse fitting operation based on the similarity between the shape of the cow's eye and the ellipse. The results indicated that the CLE-UNet model obtained a mean intersection over union (MIoU) of 89.32% and an average segmentation speed of 0.049 s per frame. Compared to somatic cell count (SCC), this method achieved an accuracy, sensitivity, and F1 value of 86.67%, 82.35%, and 87.5%, respectively, for detecting mastitis in dairy cows. In conclusion, the innovative use of the CLE-UNet algorithm has significantly improved the segmentation accuracy and has proven to be an effective tool for accurately detecting cow mastitis. [ABSTRACT FROM AUTHOR]

We propose a novel minimal solver for sphere fitting via its 2D central projection, i.e., a special ellipse. The input of the presented algorithm consists of contour points detected in a camera image. General ellipse fitting problems require five contour points. However, taking advantage of the isotropic spherical target, three points are enough to define the tangent cone parameters of the sphere. This yields the sought ellipse parameters. Similarly, the sphere center can be estimated from the cone if the radius is known. These proposed geometric methods are rapid, numerically stable, and easy to implement. Experimental results—on synthetic, photorealistic, and real images—showcase the superiority of the proposed solutions to the state-of-the-art methods. A real-world LiDAR-camera calibration application justifies the utility of the sphere-based approach resulting in an error below a few centimeters. [ABSTRACT FROM AUTHOR]

Lung pathological images are characterized by fuzzy boundary and overlapping and interweining cells.In order to solve the problem of cell segmentation, a lung cell image segmentation method combining Attention U-Net and bottleneck detection was proposed.Firstly, bilateral filtering and Laplacian sharpening were performed on the collected images to highlight the details of cell edges and increase the contrast between the target and the background while removing the noise.Then the Attention U-Net was trained, and the pathological images were segmented using the trained model to obtain the cell regions.Based on the segmentation results of the model, the discriminant model was established with area, circumference and roundness as screening conditions to distinguish single cell from overlapping cells.The bottleneck detection method was used to determine the separation point in the overlapping region of cells, and the ellipse fitting method was used to modify the boundary, and the final segmentation result was obtained.Experimental results show that this method can segment complex lung cell pathological images (including single cell and overlapping cells) and achieve good segmentation results.

The number of buds is an important index for the classification of cut lily flowers. Since manual counting is time-consuming and laborious, the cut flowers can be easily damaged, and the cut flowers' quality can be greatly affected. To build an efficient and non-destructive automatic counting of the lily cut flower grading system, we proposed a method for counting lily buds based on machine vision. However, in the images, the color of immature buds, stems, and leaves is similar. The buds are obscured by each other and by leaves, which may affect bud counting accuracy. In this paper, the threshold segmentation of color space transformation is applied to rough segmentation. Then the SVM is used for the second segmentation to extract the complete flower buds. Aiming at the flower buds shaded by each other and by the leaves, the ellipse fitting, and bud counting were was performed by arcs combination and direct least square method in the end. A total of 80 cut lily images (292 flower buds) were counted by the method, and the counting accuracy rate is 81.2% and 91.4% of flower buds were successfully fitted. The fitting accuracy of 91 flower buds was analyzed, and the mean relative errors of the long-axis and the short-axis of the fitting ellipses were less than 5%. When counting an image, the algorithm took a time of 2.371 s. The experimental results show that the proposed algorithm can count and fit flower buds better than other algorithms, which lays a foundation for the automatic classification of cut lily flowers to save labor costs and provides ideas and methods for ellipse fitting of ellipsoid-like objects shaded by each other. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low precision, strong subjectivity, and continuous measurement in the current measurement methods of corn phenotypic traits, a method of measuring corn phenotypic traits with high precision, low cost, easy carrying and continuous measurement was proposed. Firstly, the three-dimensional scanning device Kinect 2.0 is used to collect corn information and process and reconstruct the collected point cloud. Then, the stem and leaf point clouds were segmented by straight-through filtering, ellipse fitting and region growth segmentation. Finally, the phenotypic parameters of corn were obtained by triangulation and plane fitting for the segmented corn leaves, and the accuracy was analyzed. The results showed that the accuracy of corn plant height was 97.622 %, the average relative error of stem long axis was 9.46 %, the average relative error of stem short axis was 11.17 %, and the accuracy of leaf area was 95.577 %. Studies have shown that this method provides a new method for continuous measurement of phenotypic traits in corn. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. 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 results of ellipse fitting can be considerably distorted by outliers in the fitted point set.To tackle this problem, three improved ellipse fitting algorithms, one of which is based on least trimmed square, and the other two on dual point removal, are proposed.The least trimmed square algorithm starts from a random sample of the original complete fitted set, and then in each iteration, new fitted set is formed by points with the least residual errors, till the process converges to an ellipse fitting a subset whose members are mostly non-outliers.Dual point removal algorithms, on the other hand, starts from the whole fitted set, removes the two points respectively with the maximal positive and the minimal negative residual errors, and halts when the number of points in the remaining set does not exceeds a user-defined threshold.The two proposed algorithms and existing methods are compared on an image base of actual accessories.Experimental results show that when the number of reserved ellipse points is relatively small, the dual removal-based algorithms present the best fitting accuracies, but are slower than the least trimmed square fitting algorithm.When the best performance with parameter tuning is concerned, however, the least trimmed square algorithm achieves a shape-location matching accuracy of 0.62 pixels and an orientation matching accuracy of 0.6°, at an average execution time of 6.5ms, outperforming other algorithms.Other advantages of the proposed algorithms include the small number of algorithm parameters, the intuitiveness of the parameters, and the insensitivity of the algorithm performance to the parameters.These experimental results provide solid evidences for the effectiveness of the proposed algorithms, especially the least trimmed square algorithm.

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology 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.)

Strain measurement is one of the basic techniques in sheet metal forming for quantifying the deformation of a material under the effects of various loading conditions. The main components in the automatic strain measurement system are the image acquisition hardware and image processing software. This paper presents an automatic single point strain analysis system for sheet metal forming applications. The proposed system is very portable and consists of a USB microscope with different end caps for image acquisition. The software system was developed using Python – OpenCV library and provided with a graphical user interface (GUI).

Thermal infrared technology is utilized for detecting mastitis in cows owing to its non-invasive and efficient characteristics. However, the presence of surrounding regions and obstacles can impede accurate temperature measurement, thereby compromising the effectiveness of dairy mastitis detection. To address these problems, we proposed the CLE-UNet (Centroid Loss Ellipticization UNet) semantic segmentation algorithm. The algorithm consists of three main parts. Firstly, we introduced the efficient channel attention (ECA) mechanism in the feature extraction layer of UNet to improve the segmentation accuracy by focusing on more useful channel features. Secondly, we proposed a new centroid loss function to facilitate the network’s output to be closer to the position of the real label during the training process. Finally, we used a cow’s eye ellipse fitting operation based on the similarity between the shape of the cow’s eye and the ellipse. The results indicated that the CLE-UNet model obtained a mean intersection over union (MIoU) of 89.32% and an average segmentation speed of 0.049 s per frame. Compared to somatic cell count (SCC), this method achieved an accuracy, sensitivity, and F1 value of 86.67%, 82.35%, and 87.5%, respectively, for detecting mastitis in dairy cows. In conclusion, the innovative use of the CLE-UNet algorithm has significantly improved the segmentation accuracy and has proven to be an effective tool for accurately detecting cow mastitis.

Further processing and the added value of potatoes are limited by irregular potatoes. An ellipse-fitting-based Hausdorff distance and intersection over union (IoU) method for identifying irregular potatoes is proposed to solve the problem. First, the acquired potato image is resized, translated, segmented, and filtered to obtain the potato contour information. Secondly, a least-squares fitting method fits the extracted contour to an ellipse. Then, the similarity between the irregular potato contour and the fitted ellipse is characterized using the perimeter ratio, area ratio, Hausdorff distance, and IoU. Next, the characterization ability of the four features is analyzed, and an identification standard of irregular potatoes is established. Finally, we discuss the algorithm's shortcomings in this paper and draw the advantages of the algorithm by comparison. The experimental results showed that the characterization ability of perimeter ratio and area ratio was inferior to that of Hausdorff distance and IoU, and using Hausdorff distance and IoU as feature parameters can effectively identify irregular potatoes. Using Hausdorff distance separately as a feature parameter, the algorithm achieved excellent performance, with precision, recall, and F1 scores reaching 0.9423, 0.98, and 0.9608, respectively. Using IoU separately as a feature parameter, the algorithm achieved a higher overall recognition rate, with precision, recall, and F1 scores of 1, 0.96, and 0.9796, respectively. Compared with existing studies, the proposed algorithm identifies irregular potatoes using only one feature, avoiding the complexity of high-dimensional features and significantly reducing the computing effort. Moreover, simple threshold segmentation does not require data training and saves algorithm execution time. [ABSTRACT FROM AUTHOR]

Existing edge detection methods are based on fixed logics, which are not intelligent enough to distinguish useful edges and useless/noise edges. Recent ellipse detection methods developed some excellent algorithms that can still detect ellipses, while a large number of noise edges exist. However, these algorithms are compromised that will lose some precision and recall. This paper proposes a deep learning model that can intelligently distinguish useful edges and useless edges. Therefore, high-quality edge maps with low noise can be obtained. An arc-growing-based ellipse detection method is also proposed to take full advantage of the high-quality edge maps. Experiments are performed to reveal the mechanism of the deep learning model and to verify the performance of the proposed method. The experimental results demonstrate that the proposed method performs far better than the state-of-the-art in terms of precision, recall and the F-measure on industrial images and performs slightly better on natural images. [ABSTRACT FROM AUTHOR]

Fast and efficient fitting of accurate ellipses from data points has many applications in pattern recognition, machine vision, and robotics. However, the fitting accuracy may significantly degrade in the existence of outliers, such as the least-squares-based approaches. Despite robust methods attaining more accurate results than the least-squares manner under the contamination of outliers, they typically require the careful tuning of the hyper-parameters for good results. To mitigate the outlier disturbance, in this paper, we propose a conceptually simple yet quite useful preprocessing framework for high-precision ellipse fitting. Firstly, we leverage multi-scale operators to shrink the input image, by which a large number of outliers can be removed, followed by the smoothing of the sub-image to further improve the data quality. Then, we propose a key-point searching method to enhance the fitting precision via the analysis of the discrete pixel data in images. We prove that key-point-based ellipse fitting gives the upper bound of the approximation error generated by other sampled points with the same ellipse. Based on the key-point pairs inside and outside the ellipse, we further calculate their barycentric points and then perform fitting on these points to attain high-precision ellipses. We conduct extensive experiments on synthetic and real-world images to validate the proposed method and compare it with representative state-of-the-art approaches. Quantitative and qualitative results demonstrate that our method has more accurate and robust performance than competitors. Additionally, we employ the proposed method to compared approaches as a preprocessing step. Experiments demonstrate that our method is effective to significantly improve their fitting accuracy. Our source code is freely available at https://github.com/ChengQian09/MSKPF. • A simple yet quite useful method to improve the ellipse fitting performance. • A multi-scale smoothing module to remove the outlier disturbance. • A key-point searching technique to enhance the fitting accuracy. • Extensive experiments are implemented to demonstrate our method's advantages. [ABSTRACT FROM AUTHOR]

Over the years, many ellipse detection algorithms have been studied broadly, while the critical problem of accurately and effectively detecting ellipses in the real-world using robots remains a challenge. In this paper, we proposed a valuable real-time robot-oriented detector and simple tracking algorithm for ellipses. This method uses low-cost RGB cameras for conversion into HSV space to obtain reddish regions of interest (RROIs) contours, effective arc selection and grouping strategies, and the candidate ellipses selection procedures that eliminate invalid edges and clustering functions. Extensive experiments are conducted to adjust and verify the method’s parameters for achieving the best performance. The method combined with a simple tracking algorithm executes only approximately 30 ms on a video frame in most cases. The results show that the proposed method had high-quality performance (precision, recall, F-Measure scores) and the least execution time compared with the existing nine most advanced methods on three public actual application datasets. Our method could detect elliptical markers in real-time in practical applications, detect ellipses adaptively under natural light, well detect severely blocked and specular reflection ellipses when the elliptical object was far from or close to the robot. The average detection frequency can meet the real-time requirements (>10 Hz).

Balanced steady-state free precession (bSSFP) imaging enables high scan efficiency in MRI, but differs from conventional sequences in terms of elevated sensitivity to main field inhomogeneity and nonstandard ${\text {T}}_{{2}}/{\text {T}}_{{1}}$ -weighted tissue contrast. To address these limitations, multiple bSSFP images of the same anatomy are commonly acquired with a set of different RF phase-cycling increments. Joint processing of phase-cycled acquisitions serves to mitigate sensitivity to field inhomogeneity. Recently phase-cycled bSSFP acquisitions were also leveraged to estimate relaxation parameters based on explicit signal models. While effective, these model-based methods often involve a large number of acquisitions (N ≈ 10-16), degrading scan efficiency. Here, we propose a new constrained ellipse fitting method (CELF) for parameter estimation with improved efficiency and accuracy in phase-cycled bSSFP MRI. CELF is based on the elliptical signal model framework for complex bSSFP signals; and it introduces geometrical constraints on ellipse properties to improve estimation efficiency, and dictionary-based identification to improve estimation accuracy. CELF generates maps of ${\text {T}}_{{1}}$ , ${\text {T}}_{{2}}$ , off-resonance and on-resonant bSSFP signal by employing a separate $\text {B}_{{1}}$ map to mitigate sensitivity to flip angle variations. Our results indicate that CELF can produce accurate off-resonance and banding-free bSSFP maps with as few as N = 4 acquisitions, while estimation accuracy for relaxation parameters is notably limited by biases from microstructural sensitivity of bSSFP imaging. [ABSTRACT FROM AUTHOR]

In this paper, a novel sun sensor which has been applied on orbit since 2018 is introduced. The remote data transmitted from the satellite is analyzed to show its performance. In the lighting area, the sun sensor can give the sun vector which used for the attitude determination. From the data, the center point error is found to be the most significant factor that affects the accuracy of the sun sensor. In the “Sun pointing” work mode, the real output of the sun sensor is different from the theoretical output. The data from the star sensor is used for compensating the center point error based on ellipse fitting. The triad algorithm is affected by the center point error of the sun sensor. The simulation only considers this kind of error. And the result shows that it can decrease the Euler angle errors, so that improves the accuracy of triad algorithm.

Three-component fiber optic seismometers are widely used in the detection of seismic signals. The arctangent approach of phase-generated carrier (PGC-Arctan) demodulation algorithm is one of the commonly used demodulation techniques in FOISs. Nonlinear factors such as phase modulation depth(C) variation and light intensity disturbance (LID) have an important impact on performance of traditional PGC demodulation algorithms. This paper introduces a new demodulation algorithm called Kalman filtering ellipse (PGC-Arctan-EKF) demodulation algorithm, which can improve the robustness of the fitting. In order to meet the requirements of high performance and low power consumption of seafloor sensing, Tri-Component fiber optic seismometer based on SoC(ARM +FPGA) is proposed in this paper. The phase self-noise of the system is better than -115 dB re rad/Hz1/2,and the large signal can reach 66 dB re rad/Hz1/2 @220Hz with the THD is -61.22 dB.

Benefiting from the characteristics of full field scanning, high resolution and high precision, digital fringe projection measurement technology has been widely used in three-dimensional measurement. For the cylinder pose detection, due to the influence of occlusion and noise, the obtained three-dimensional point cloud information is usually incomplete, resulting in the unreliable object parameters after point cloud fitting. In view of the above problems, a cylinder fitting method with incomplete point cloud data based on the cutting plane is proposed. Firstly, the point cloud is processed by bilateral filtering method to reduce the influence of outliers and noise points. Then, according to the principle of forming ellipse on cylinder cutting plane, some equidistant cutting planes are selected. At the same time, in order to reduce the influence of outliers on fitting, the random sampling consistency algorithm is used for ellipse fitting with the points on each cutting plane, that is, the ellipse parameters corresponding to each cutting plane can be obtained. Finally, random sampling consistency algorithm is applied for cylinder axis fitting with the ellipse centers of all the cutting planes, calculating the axis vector of the cylinder. Thus, the parameters of the cylinder detected are obtained. The experimental results of stepped cylinder with diffuse reflection and metal cylinder show the effectiveness and high precision of the proposed method.

Extracting the phenotypic characteristics of Amygdalus mira seeds is to measure the size of a physical object that needs to operate a large number of peach seeds. However, some phenotypic data is still difficult to obtain at present. In this study, an automatic multi-feature extraction system was proposed for peach seeds using HSV color space and edge point detection. The system included three parts. The first part was the collection and image acquisition of Amygdalus mira seeds. Specifically, the Amygdalus mira seeds were collected from the scientific research institutions, and then seed images were captured using a small studio and digital camera. The second part was the image processing of peach seeds. First, the region of interest was obtained on the original image of peach seed, then converted from the RGB to the HSV color space. The threshold segmentation was then selected using the HSV space, in order to remove the seeds from the original image. The purpose of threshold extraction was to determine what threshold range of H space was used to segment the seed kernel and background and then determine the best segmentation of S space under the H threshold range. Finally, the V space threshold was selected in the threshold range of H and S space with the best segmentation, in order that all pictures were the same set of segmentation thresholds, further to realize the preliminary segmentation of peach seed. Binary morphological operations were then utilized to revise the under- and over-segmentation. The third part was the feature extraction and quantification of seeds. First, the morphological features were achieved, including area, shape index, and seed tip state. Specifically, the edge points of seed kernel images were detected to draw the fitting ellipse and separate the tip of seeds. Among them, the tip state was evaluated using the area and sharpness of the seed tip. Subsequently, the color and texture characteristics of the peach kernel were obtained using low-order moments and gray-level co-occurrence matrix. As such, the quantitative analysis was realized for the nucleus phenotype of Amygdalus mira seeds. Additionally, the extracted color features included the first-, the second-, and the third-order moments. The texture features included contrast, energy, homogeneity, and correlation. A comparative experiment was conducted to evaluate the RGB and gray threshold. It was found that the HSV threshold presented a better segmentation, indicating the highest accuracy rate (99.7%), average accuracy rate (98.9%), and Intersection over Union (IoU) (97.4%). In addition, the extraction experiments of morphological, color, and texture features were carried out to further verify the performance of the system. The results showed that there were quite different phenotypic characteristics of different seed individuals. At the same time, the H-mean and S-mean moment showed a downward trend, as the color depth of seed gradually deepened, compared the extracted color features with the visual. The same comparison experiment was also performed on texture features. The contrast increased, while the homogeneity decreased gradually, as the depth of grooves on the seed increased gradually. The energy and correlation decreased gradually when the surface texture of seeds was much clearer. In summary, the extracted characteristics of color and texture were more consistent with that of the visual, indicating the quantitative texture of Amygdalus mira seed kernel. Consequently, this system can be expected to realize the extraction and quantification of kernel tip state, color, and texture features. The finding can also provide the data foundation and technical support for the breeding research of Amygdalus mira. [ABSTRACT FROM AUTHOR]