The utilization of orbital angular momentum (OAM) as an independent degree of freedom for information encryption has gained significant attention. Nevertheless, the multiplexing capacity of OAM is ...still constrained, and there is a lack of research on accelerated decryption techniques in OAM multiplexed holography. In this study, ellipse‐like orbital angular momentum (EL‐OAM) multiplexed holography is proposed and implemented. The ellipse‐like (EL) beam incorporates three modulatable parameters: topological charge, power index, and radial shift. To achieve efficient decryption, a composite ellipse‐like lens (CELL) is introduced that generates two EL beams carrying different OAMs at distinct axial positions, resulting in two reconstruction holographic images with approximately equal intensity. The mode selectivity of EL‐OAM based on the aforementioned parameters is investigated. Experimental verification demonstrates the feasibility of EL‐OAM multiplexed holography and its potential for fast decryption in various dimensions. This breakthrough opens up new possibilities for practical information encryption and decryption systems.
Ellipse‐like orbital angular momentum and multiplexed holography utilizing an ellipse‐like beam with three adjustable parameters is proposed and implemented. To enhance decryption efficiency, a composite ellipse‐like lens, capable of generating two ellipse‐like beams with different orbital angular momenta at separate axial positions, is introduced, thereby yielding two reconstruction holographic images of roughly equal intensity.
•Block representation of edge contours is introduced to extract arcs, which are then classified into four sets.•A novel top-down ellipse fitting method is proposed to estimate ellipse parameters. ...This leads to a significant improvement in the detection accuracy.•Geometric properties, such as arc length, arc distance and cross product, are utilized to significantly reduce the computational costs of grouping arcs.•A two-level validation process is used to select highly probable potential ellipses which allowing most falsely detected ellipses to be removed, improving the precision.
Ellipse detection is a basic task in many computer-vision related problems. While widely studied in recent years, accurate and efficient detection in real-world images is still a challenge. In this paper, a novel ellipse detector, with high accuracy and efficiency, is proposed. The detector models edge by block sequences, and extracts a set of elliptical arcs, which are classified into four sets. Then top-down ellipse fitting strategy that also makes the method able to detect small and flat ellipses is designed. A two-level validation process is used to select highly probable potential ellipses, especially for fragmented ellipses. Experiments on four synthetic datasets show that the proposed method performs far better than existing methods. In images with severe cluttering and occlusion, the F-measure can still be around 0.9. On four real image datasets the proposed method achieves better F-measure scores with competitive speed than state-of-the-art techniques.
•Confocal hyperbola point to ellipse distance revisited and analytically derived.•Performance compared to algebraic, Sampson, and Harker point to ellipse distances.•Confocal hyperbola distance strong ...predictor of the true geometric distance.•Confocal hyperbola ellipse fitting superior to established ellipse fitting methods.•Confocal hyperbola suggested to fit ellipses in images and cylinders in point clouds.
This manuscript presents a new method for fitting ellipses to two-dimensional data using the confocal hyperbola approximation to the geometric distance of points to ellipses. The proposed method was evaluated and compared to established methods on simulated and real-world datasets. First, it was revealed that the confocal hyperbola distance considerably outperforms other distance approximations such as algebraic and Sampson. Next, the proposed ellipse fitting method was compared with five reliable and established methods proposed by Halir, Taubin, Kanatani, Ahn and Szpak. The performance of each method as a function of rotation, aspect ratio, noise, and arc-length were examined. It was observed that the proposed ellipse fitting method achieved almost identical results (and in some cases better) than the gold standard geometric method of Ahn and outperformed the remaining methods in all simulation experiments. Finally, the proposed method outperformed the considered ellipse fitting methods in estimating the geometric parameters of cylindrical mechanical pipes from point clouds. The results of the experiments show that the confocal hyperbola is an excellent approximation to the true geometric distance and produces reliable and accurate ellipse fitting in practical settings.
Over the years many ellipse detection algorithms spring up and are studied broadly, while the critical issue of detecting ellipses accurately and efficiently in real-world images remains a challenge. ...In this paper, we propose a valuable industry-oriented ellipse detector by arc-support line segments, which simultaneously reaches high detection accuracy and efficiency. To simplify the complicated curves in an image while retaining the general properties including convexity and polarity, the arc-support line segments are extracted, which grounds the successful detection of ellipses. The arc-support groups are formed by iteratively and robustly linking the arc-support line segments that latently belong to a common ellipse. Afterward, two complementary approaches, namely, locally selecting the arc-support group with higher saliency and globally searching all the valid paired groups, are adopted to fit the initial ellipses in a fast way. Then, the ellipse candidate set can be formulated by hierarchical clustering of 5D parameter space of initial ellipses. Finally, the salient ellipse candidates are selected and refined as detections subject to the stringent and effective verification. Extensive experiments on three public datasets are implemented and our method achieves the best F-measure scores compared to the state-of-the-art methods. The source code is available at https://github.com/AlanLuSun/High-quality-ellipse-detection.
The view factor is a fundamental parameter for evaluating the radiative heat transfer between two surfaces. View factors for various geometries have been investigated in the past studies. However, ...the view factors of a spheroid and an ellipse are known for only limited configurations. In this study, the analytical view factor expressions of an ellipse and a spheroid from a plate element are derived. The spheroid view factor solution applies to a plate element with any position and orientation, and the ellipse solution is valid when the plate element is on the perpendicular axis through the ellipse center. The derived analytical solutions are validated against the numerical results for various configurations, showing deviations of less than 0.0001 for all cases.
•Analytical view factors of a spheroid from a plate element are derived for arbitrary geometrical configurations.•Analytical view factors of an ellipse from a plate element are derived, where the plate element is on the perpendicular axis through the ellipse center.•The derived analytical solutions are validated against the numerical results for various configurations, showing the deviations of less than 0.0001 for all test cases.•For certain geometrical configurations, the analytical view factors of a spheroid and an ellipse can be described with simple formulas, which are presented in addition to the general solution.
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.
Fast and accurate ellipse detection is critical in certain computer vision tasks. In this paper, we propose an arc adjacency matrix-based ellipse detection (AAMED) method to fulfill this requirement. ...At first, after segmenting the edges into elliptic arcs, the digraph-based arc adjacency matrix (AAM) is constructed to describe their triple sequential adjacency states. Curvature and region constraints are employed to make the AAM sparse. Secondly, through bidirectionally searching the AAM, we can get all arc combinations which are probably true ellipse candidates. The cumulative-factor (CF) based cumulative matrices (CM) are worked out simultaneously. CF is irrelative to the image context and can be pre-calculated. CM is related to the arcs or arc combinations and can be calculated by the addition or subtraction of CF. Then the ellipses are efficiently fitted from these candidates through twice eigendecomposition of CM using Jacobi method. Finally, a comprehensive validation score is proposed to eliminate false ellipses effectively. The score is mainly influenced by the constraints about adaptive shape, tangent similarity, distribution compensation. Experiments show that our method outperforms the 12 state-of-the-art methods on 9 datasets as a whole, with reference to recall, precision, F-measure, and time-consumption.
Due to high mixing performance and simple geometry structure, serpentine micromixer is one typical passive micromixer that has been widely investigated. Traditional zigzag and square-wave serpentine ...micromixers can achieve sufficient mixing, but tend to induce significant pressure drop. The excessive pressure drop means more energy consumption, which leads to low cost-performance of mixing. To mitigate excessive pressure drop, a novel serpentine micromixer utilizing ellipse curve is proposed. While fluids flowing through ellipse curve microchannels, the flow directions keep continuous changing. Therefore, the Dean vortices are induced throughout the whole flow path. Numerical simulation and visualization experiments are conducted at Reynolds number (Re) ranging from 0.1 to 100. Dean vortices varies with the changing curvature in different ellipse curves, and local Dean numbers are calculated for quantitative evaluation. The results suggest that the ellipse with a larger eccentricity induces stronger Dean vortices, thus better mixing performance can be obtained. A parameter, named mixing performance cost (Mec), is proposed to evaluate the cost-performance of micromixers. Compared with the zigzag, square-wave and other improved serpentine micromixers, the ellipse curve micromixer produces lower pressure drop while have the capability to maintain excellent mixing performance. The ellipse curve micromixer is proved to be more cost-effective for rapid mixing in complex microfluidic systems.
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•A cost-effective serpentine micromixer utilizing ellipse curve is developed.•Local Dean numbers are calculated for evaluating Dean vortices quantitatively.•Mixing performance cost parameter is proposed to evaluate the cost performance.•Continuous changing curvature of ellipse curve avoids excessive pressure drop.•Moderate eccentricity leads to the most cost-effective ellipse curve micromixer.
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•The abundance of MPs in WWTP declined sharply with a removal rate of 64.4%•Larger size fraction of MPs in the effluent was reduced compared to that in the influent.•Ellipse was ...abundantly seen in the influent with a percentage of 4.4%, while not observed in the effluent.•Polyamide was the main plastic component in wastewater with 54.8%.
Municipal wastewater treatment plants (WWTP) are considered as a significant point source of microplastics (MPs) in the aquatic environment. The objective of this study was to investigate the transport and fate of MPs particles in one WWTP of China based on the conventional activated sludge process. The results exhibited that the abundance of MPs in wastewater declined sharply, from 79.9 n L−1 in the influent to 28.4 n L−1 in the effluent, with a removal rate of 64.4%. MPs removed were mostly transferred and stored into the sludge, and the abundance of MPs in dewatered sludge was 240.3 ± 31.4 n g−1 (dry sludge) with an average size of 222.6 μm. Larger size fraction of MPs in the effluent was reduced compared to that in the influent due to mechanical erosion and sedimentation into sludge. Fiber and fragment were main MPs particles in four wastewater sampling sites, with the average percentage ranged from 33.5 to 56.7% and 30.4 to 45.6%, respectively. An interesting finding is that the ellipses with the size ranged from 100 to 800 µm (average size of 348.1 µm), seldom reported before, were abundantly seen in the influent with a percentage of 4.4%, but not observed in the effluent. A higher fraction of microbead and foam in sludge (17.1% and 12.9%) indicates MPs with the smaller size (average size of 90.3 and 240.1 µm, respectively) in wastewater are prone to be adsorbed and transferred into sludge. Polyamide (nylon) was found to be the main plastic component in wastewater with 54.8% based on Raman spectra, indicating that the MPs particles are primarily originated from the wastewater discharged by washing clothes and polymer manufacturing and processing industries, followed by personal care products.
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).
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