Circle Fitting Research Articles

3-Dimensional Quantification Glenoid Bone Loss in Recurrent Shoulder Instability – Role in Establishing Parameters for Latarjet vs Bankart Procedure

Our study aims to quantify area of glenoid bone loss in the setting of recurrent shoulder instability. This study utilizes the anatomical surface of the glenoid from 3D rendered models of the scapula. Secondly, we aim to compare the results of this method with the best circle fit (Sugaya) and Glenoid Length/Width ratio (Giles) method widely used to evaluate glenoid bone loss.

Influence of flight parameters on UAS-based monitoring of tree height, diameter, and density

Increased focus on restoring forest structural variation and spatial pattern in dry conifer forests has led to greater emphasis on forest monitoring strategies that can be summarized across scales. To inform restoration objectives with data sources that can characterize individual trees, groups of trees, and the entire stand, different remote sensing strategies such as aerial and terrestrial light detection and ranging (LiDAR) have been explored. Unfortunately, high equipment and operational costs of aerial systems, along with limited spatial extent of terrestrial scanners, have restricted widespread adoption of these technologies for repeated forest monitoring. This study investigates applications of unmanned aerial system (UAS) imagery for Structure from Motion derived modeling of individual tree and stand-level metrics. Specifically, we evaluate how flight parameters impact UAS extracted height and imputed DBH accuracies against field stem-mapped values. In total, 30 UAS image datasets collected from combinations of three altitudes, two flight patterns, and five camera orientations were assessed. Tree heights were extracted using a variable window function that searched UAS-derived canopy height models, while DBH was sampled from point cloud slices at 1.32–1.42 m using a least squares circle fitting algorithm. The sample trees were then filtered against National Forest Inventory data from the study region to ensure reasonable matching of extracted heights and diameters. The matched values were used to create a height to diameter relationship for predicting missing DBH values. Extracted and imputed tree values were compared against stem-mapped values to determine tree commission and omission rates, the accuracy and precision of extracted tree height, DBH, as well as overstory and understory stand density. Finding that, 1) tree extraction accuracy and correctness was maximized (F-score = 0.77) for nadir crosshatch UAS flight designs; 2) extracted tree height R2 with stem-mapped values was high (R2 ≥ 0.98) for all UAS flight parameters, but the quality (mean error = 0.79 cm) and quantity (~10% of all trees) of extracted DBH values was maximized for lower altitude, nadir crosshatch acquisitions; 3) the distribution of predicted DBH values most closely matched field observed values for off-nadir crosshatch flight designs; 4) using either off-nadir or crosshatch flight designs at lower altitudes maximized correlation (r > 0.70) and accuracy (basal area within 2 m2 ha−1) of stand density estimates. This study demonstrates a novel UAS-based inventory strategy for estimating individual tree structural attributes (i.e., location, height, and DBH) in dry conifer forests, without the need for in situ field observations.

An Automatic Pointer Meter Reading Method based on Deep Learning in Gas Gathering Station

pointer meters are widely used in gas gathering stations, and manual meter reading is time-consuming and laborious. This paper proposes an automatic reading method of pointer meters in gas gathering stations based on deep learning and image processing algorithm. Firstly, the input image is detected by YOLO neural network algorithm to quickly locate the position of the pointer meter in the image; then, the meter image is segmented from the background image, and after filtering and binarization preprocessing, the meter border is preliminarily detected by Hough transform. After further removing the background interference, the pointer area and scale value area are segmented by connected domain detection to detect. After measuring the center of mass of the scale value area, the center of the dial is corrected again by RANSAC fitting circle, and the pointer angle is identified by index table thinning algorithm, and finally the meter indication is calculated. The results show that this method can quickly locate the pointer meters in the gas gathering station under complex background and reduce the light interference.

Accuracy Assessment of Total Stem Volume Using Close-Range Sensing: Advances in Precision Forestry

Accurate collection of dendrometric information is essential for improving decision confidence and supporting potential advances in forest management planning (FMP). Total stem volume is an important forest inventory parameter that requires high accuracy. Terrestrial laser scanning (TLS) has emerged as one of the most promising tools for automatically measuring total stem height and diameter at breast height (DBH) with very high detail. This study compares the accuracy of different methods for extracting the total stem height and DBH to estimate total stem volume from TLS data. Our results show that estimates of stem volume using the random sample consensus (RANSAC) and convex hull and HTSP methods are more accurate (bias = 0.004 for RANSAC and bias = 0.009 for convex hull and HTSP) than those using the circle fitting method (bias = 0.046). Furthermore, the RANSAC method had the best performance with the lowest bias and the highest percentage of accuracy (78.89%). The results of this study provide insight into the performance and accuracy of the tested methods for tree-level stem volume estimation, and allow for the further development of improved methods for point-cloud-based data collection with the goal of supporting potential advances in precision forestry.

Microdisplacement Measurement Based on F-P Etalon: Processing Method and Experiments.

Herein, a processing method is proposed for accurate microdisplacement measurements from a 2D Fabry–Perot (F-P) fringe pattern. The core of the processing algorithm uses the F-P interference imaging concentric ring pattern to accurately calculate the centre coordinates of the concentric ring. The influencing factors of measurement were analysed, and the basic idea of data processing was provided. In particular, the coordinate rotation by the 45-degree method (CR) was improved; consequently, the virtual pixel interval was reduced by half, and the calculation accuracy of the circle centre coordinate was improved. Experiments were conducted to analyse the influence of the subdivision and circle fitting methods. The results show that the proposed secondary coordinate rotation (SCR) by 45 degrees method can obtain higher accuracy of the centre coordinate than the CR method, and that the multichord averaging method (MCAM) is more suitable for calculation of the centre coordinate than the circular regression method (CRM). Displacement measurement experiments were performed. The results show that the standard experimental deviation of the centre of the circle is approximately 0.009 µm, and the extended uncertainty of the displacement measurement in the range of 5 mm is approximately 0.03 μm. The data processing method studied in this study can be widely used in the field of F-P interferometry.

Comparison of Numerical Calculation Methods for Stem Diameter Retrieval Using Terrestrial Laser Data

Terrestrial laser scanning (TLS) can be used as a millimeter-level measurement tool for forest inventories. However, the stem diameter retrieval accuracy in sample plot scanning is not yet convincing. The errors in each step of stem diameter retrieval algorithms must be evaluated. In this study, six numerical calculation methods for the numerical calculation step, i.e., cylinder fitting (CYF), circle fitting (CF), convex hull line fitting (CLF), the proposed caliper simulation method (CSM), closure B-spline curve fitting (SP) and closure Bézier curve fitting with global convexity (SPC), were applied to stem diameter retrieval, and the similarities and differences were evaluated. The ovality, completeness and roughness were used to evaluate the stem slice point cloud quality. A total of 165 stem slice point clouds at breast height collected from three Larix kaempferi plots were used. Compared with the field-measured stem diameters at breast height (DBHs), the root mean square errors (RMSEs) of the CYF, CF, CLF, CSM, SP and SPC methods were 0.30 cm, 0.30 cm, 0.51 cm, 0.51 cm, 0.56 cm and 0.54 cm, respectively. Compared with the SPC method results, the RMSE of the CSM results was 0.05 cm. The results illustrated that the CYF and CF methods performed the same, as did the CLF and CSM methods. Most DBHs retrieved by the CYF and CF methods were smaller than the field-measured DBHs, and most DBHs retrieved by the CLF, CSM, SP and SPC methods were larger than the field-measured DBHs. This study demonstrated that the CYF and CF methods perform the best and are the most robust, and the measurements by a diameter tape and a caliper are similar enough for forestry inventories. Evaluating and preprocessing stem slice point clouds is a potential way to improve stem diameter retrieval accuracy.

Low-cost design of a PCB imaging system based on laser triangulation

With the increasing miniaturization of electronic products, fanless design is required. The volume of electronic components, especially the height, is critical to the structural design of electronic devices. This paper proposes the design of on-line height measurement equipment based on commercial charge-coupled device (CCD), analyses the principle of height measurement of components, and explains the system composition and working principle of the measurement system. Moreover, it presents a comparative analysis of common methods for determining the center position, which include circle fitting, the gray centroid and extended method or Gaussian fitting, which generally lead to diverse results. The experimental results show that Gaussian fitting is better than other methods under realistic conditions, a three-dimensional model of the components is provided, and the error factors that affect measurement are analyzed.

Robust detection of non-overlapping ellipses from points with applications to circular target extraction in images and cylinder detection in point clouds

Detection of non-overlapping ellipses from 2-dimensional (2D) edge points is an essential step towards solving typical photogrammetry problems pertaining to feature detection, calibration, and registration of optical instruments. For instance, circular and spherical black and white calibration and registration targets are represented as ellipses in images. Furthermore, the intersection of a cut plane with cylindrical point clouds generates 2D points following elliptic patterns. To this end, this study proposes a collection of new methods for the automatic and robust detection of non-overlapping ellipses from 2D points. These methods will first be applied to detect circular and spherical targets in images and, second, to detect cylinders in 3D point clouds. The method utilizes the Euclidian ellipticity and a new systematic and generalizable threshold to decide if a set of connected points follow an elliptic pattern. When connected points include outliers, the newly proposed robust Monte Carlo-based ellipse fitting method will be deployed. This method includes three new developments: (i) selecting initial subsamples using a bucketing strategy based on the polar angle of the points; (ii) detecting inlier points by reducing the robust ellipse fitting to a robust circle fitting problem; and (iii) choosing the best inlier set amongst all subsamples using adaptive, systematic, and generalizable selection criteria. A new process is presented to extract cylinders from a point cloud by detecting non-overlapping ellipses from the points projected onto an intersecting cut plane. The proposed methods were compared to established state-of-the-art methods, using simulated and real-world datasets, through the design of four sets of original experiments. The experiments include (i) comparisons of robust ellipse fitting; (ii) sensitivity analysis of the ellipse validation criteria; (iii) comparison of non-overlapping ellipse detection; and (iv) detection of pipes from terrestrial laser scanner point clouds. It was found that the proposed robust ellipse detection was superior to four reliable robust methods, including the popular least median of squares, in both simulated and real-world datasets. The proposed process for detecting non-overlapping ellipses achieved F-measure of 99.3% on real images, compared to 42.4%, 65.6%, and 59.2%, obtained using the methods of Fornaciari, Patraucean, and Panagiotakis, respectively. The proposed cylinder extraction method identified all detectable mechanical pipes in two real-world point clouds collected in laboratory and industrial construction site conditions. The results of this investigation show promise for the application of the proposed methods for automatic extraction of circular targets from images and pipes from point clouds.

Computationally efficient approach to quantify 2D particle morphological descriptors

The macro-scale behavior of granular materials is strongly influenced by grain kinematics. The mobility of the grains in turn is affected by grain morphology which needs to be comprehensively characterized. Initially, morphological descriptors were determined by manual processes which were tedious and cumbersome. But with the help of image processing techniques and computational geometry, this problem can be handled with ease. The long-established descriptors such as sphericity and roundness can be estimated by operating over the grain boundary obtained from 2D images of the particle. The main objective of this study is to quantify these descriptors in a computationally effective way. The roundness quantification in previous studies involves the sequence of processes such as removing the noise, corner identification and fitting circles. This paper details the necessary modifications to the quantification process required to reduce the cost of time for a single particle. Further, the influence of different smoothing techniques and a new corner identification method will be detailed.

Hypersphere Fitting From Noisy Data Using an EM Algorithm

This letter studies a new expectation maximization (EM) algorithm to solve the problem of circle, sphere and more generally hypersphere fitting. This algorithm relies on the introduction of random latent vectors having a priori independent von Mises-Fisher distributions defined on the hypersphere. This statistical model leads to a complete data likelihood whose expected value, conditioned on the observed data, has a Von Mises-Fisher distribution. As a result, the inference problem can be solved with a simple EM algorithm. The performance of the resulting hypersphere fitting algorithm is evaluated for circle and sphere fitting.

Measurement method of repeated positioning accuracy of pinhole runnerbased on combination of laser and CCD

The pinhole runner is the key component of the optical-path automatic calibration system of an experimental device, and the repeated positioning accuracy is one of its important indexes. In order to effectively measure the repeated positioning accuracy of the pinhole runner in vacuum environment, a measurement method based on the combination of laser and charge coupled device (CCD) was proposed, and the position repeatability of laser spot center of runner filtering pinhole was taken as the evaluation index of its repeated positioning accuracy. A complete set of measurement system was built, the laser spot filtered through the runner filtering pinhole was collected by CCD, and the laser spot image was preprocessed by the image processing methods, such as filtering and noise reduction, threshold segmentation, binarization and edge detection. The least square method was used for circle fitting of the laser spot, and the center coordinates of laser spot were obtained. Compared with laser interferometer in non- vacuum environment, the angular deviation between them was only 11″, which showed that the measurement method had high accuracy and met the measurement requirements. Finally, the repeatability measurement of the pinhole runner in vacuum environment was carried out for about 60 times forward and backward reciprocation in about 6 hours. The results show that the method can effectively measure the repeated positioning accuracy of the pinhole runner in vacuum environment.

Iris Segmentation Based on Black Hole Algorithm for Biometric System

Current iris recognition schemes such as IntegroDifferential method, Hough Transform, Watershed Transform Circle Fitting, and Circular Hough Transformation (CHT) are used to find circular parameters between pupil and iris. Segmentation process of an eye image using the circular parameters toextracts the iris region still can be further improved. In this paper, we introduced an optimization method of circular parameters detection for iris segmentation based on Black Hole Algorithm (BHA). The proposed segmentation algorithm utilizes a computational model of the pixels’ value to detect the iris boundary. The BHA searches for center radius of both pupil and iris. The system tests the CASIA Iris Interval V3 database by on MATLAB. The segmented images show an accuracy of 98.3%. In short, the segmentation-based on BHA is efficient to identify the iris for any future access control applications.

Vision-based Size Estimation and Centroid Positioning of Partially Occluded Fruits

The objective of this study was to propose a simple and efficient image processing algorithm for estimating the size and centroid of partially occluded round fruits. In the proposed method, the size and centroid of partially occluded fruit were estimated based on the mathematical properties of the arc-radius. The experimental tests were conducted in a laboratory with orange, Sunkist, apple, and tomato fruits by setting different occlusion conditions. The occlusion percentage was varied between 0% and 90%. The accuracy and processing time of the proposed method were compared with that of the widely-used conic-section circle fitting method. The results showed that the proposed method resulted in an overall accuracy of 95.1% and processing time of 0.66 s, as opposed to 60.2% and 0.68 s, respectively, using the conic-section equation. Compared with the conic-section equation, the proposed method was able to give a more robust prediction, even for low resolution images.

Multiphase Continuous-Wave Doppler Radar With Multiarc Circle Fitting Algorithm for Small Periodic Displacement Measurement

A multiphase continuous-wave (MPCW) Doppler radar with a multiarc circle fitting (MACF) algorithm is proposed for improving the accuracy of small periodic displacement measurements. In the proposed radar, local oscillator (LO) signals with different phases are incident to multiple I/Q mixers, to simultaneously generate multiarcs for small periodic motions. The multiarcs obtained from the synchronized data are used in the circle fitting method to provide dc offset compensation, for accurate phase demodulation. The proposed MACF algorithm, based on the Levenberg–Marquardt method, can more precisely compensate for the dc offset by combining multiarcs generated from the proposed radar. A 5.8-GHz MPCW Doppler radar module with four-phased LO signals is implemented as a demonstration. The small periodic displacement of a metal plate on a single-axis linear stage is measured using the proposed radar module and algorithm. Compared with conventional CW Doppler radar using a single arc, measurement results using the proposed radar show that the root-mean-square error (RMSE) can be effectively decreased in the periodic motion from 1 to 2 mm, at distances from 30 to 60 cm. The RMSE is reduced by more than 33% for a peak-to-peak displacement of 1 mm at a distance of 60 cm.

Automated replacement scheme of DC transformer bushing based on laser scanning profile: Design and experiment

The valve-side bushing is an important part of the ultra-high voltage (UHV) converter transformer. The bushing needs to be replaced at the converter station if the bushing is damaged. The bushing replacement of the existing project needs to be carried out by crane and manual operation. However, the replacement process takes a long time and the lifting accuracy is poor, which may cause damage to the bushing. A scheme based on a 2D laser profile scanner for automatic bushing replacement applied to direct-current (DC) converter transformer is proposed in this paper, which can replace any damaged bushing in the valve hall without removing other equipment. An initial value of the cylinder fitting algorithm based on the 2D laser profile is proposed in order to solve the position of the bushing, which fully considers the characteristics of the laser profile scanner, and uses the circle and straight-line fitting algorithm to solve the initial value. It is proved that the algorithm can improve the efficiency of cylinder fitting by comparing with the results of the mean value method. Industrial robots are used to implement the replacement scheme by using this algorithm, and the feasibility of the replacement scheme is verified.

A visual detection method for PCI blockage and coke size distribution in tuyere raceway

ABSTRACT Accurate detection for pulverized coal injection (PCI) blockage and coke size distribution are effective for blast furnace (BF) operation. Nowadays, tuyere cameras have been applied in BF. However, the appearance of captured raceway images changes, which requests the detection method to be adaptive. Therefore, an intelligent detection method for PCI blockage and coke size distribution is proposed. An adaptive pre-processing algorithm is firstly developed to improve image quality. Secondly, the fitting circle is used to locate tuyere region and the improved U-net network is investigated for lance segmentation to construct the background template. Then, target regions can be obtained by background subtraction algorithm. Finally, the PCI blockage can be detected according to the area information, and the size distribution of cokes can be calculated by multi-directional Feret diameter. Massive raceway videos are used to evaluate the method. Experiment results show the method can detect PCI blockage and cokes size distribution.

Tunnel Deformation Inspection via Global Spatial Axis Extraction from 3D Raw Point Cloud.

Global inspection of large-scale tunnels is a fundamental yet challenging task to ensure the structural stability of tunnels and driving safety. Advanced LiDAR scanners, which sample tunnels into 3D point clouds, are making their debut in the Tunnel Deformation Inspection (TDI). However, the acquired raw point clouds inevitably possess noticeable occlusions, missing areas, and noise/outliers. Considering the tunnel as a geometrical sweeping feature, we propose an effective tunnel deformation inspection algorithm by extracting the global spatial axis from the poor-quality raw point cloud. Essentially, we convert tunnel axis extraction into an iterative fitting optimization problem. Specifically, given the scanned raw point cloud of a tunnel, the initial design axis is sampled to generate a series of normal planes within the corresponding Frenet frame, followed by intersecting those planes with the tunnel point cloud to yield a sequence of cross sections. By fitting cross sections with circles, the fitted circle centers are approximated with a B-Spline curve, which is considered as an updated axis. The procedure of “circle fitting and B-SPline approximation” repeats iteratively until convergency, that is, the distance of each fitted circle center to the current axis is smaller than a given threshold. By this means, the spatial axis of the tunnel can be accurately obtained. Subsequently, according to the practical mechanism of tunnel deformation, we design a segmentation approach to partition cross sections into meaningful pieces, based on which various inspection parameters can be automatically computed regarding to tunnel deformation. A variety of practical experiments have demonstrated the feasibility and effectiveness of our inspection method.

IRIS LOCATION METHOD BASED ON MATHEMATICAL MORPHOLOGY AND IMPROVED HOUGH TRANSFORM

An iris location method based on mathematical morphology and improved Hough transform is proposed in this paper. When locating the iris inner boundary, an iris inner boundary method based on mathematical morphology and circle fitting is proposed. The iris image is first preprocessed to get a binary image by the gray stretch transform or the circular region mean filter, and then the type of the connected region selected as the iris inner boundary in the binary image is determined by the ratio of the width to the length. On the foundation, the different method is used to extract the parameters of the connected region and the iris inner boundary is obtained. When locating the iris outer boundary, an iris outer boundary method based on the improved Hough transform is put forward. First, the Gaussian filter is used to deal with the iris image, the filtered image is reduced, Canny operator is employed for edge detection, an annular region centered at the center of the iris inner boundary of the reduced image is cropped to explore a circle by Hough transform, and the center and the radius of the iris outer boundary are worked out. The experimental results reveal that this proposed method has the advantages of fast speed, high accuracy, strong robustness and practicability.

A 0.7–5.7 GHz Reconfigurable MIMO Receiver Architecture for Analog Spatial Notch Filtering Using Orthogonal Beamforming

A highly reconfigurable direct-conversion software-defined multiple-input multiple-output (MIMO) receiver with four RF inputs and four I/Q baseband outputs is proposed. It allows for digital MIMO but also analog interference rejection by spatial notch filtering through four flexible and simultaneous orthogonal beams. A segmented constant-Gm vector modulator (VM) with improved interference tolerance and wide RF frequency range targeting the sub-6-GHz bands is proposed. It exploits current-domain beamforming before $I$ – $V$ conversion by transimpedance amplifiers. A 0.7–5.7-GHz 22-nm fully depleted silicon-on-insulator (FD-SOI) prototype chip achieves >29 dB spatial filtering for a single notch and an ultrawideband 20-dB notch suppression bandwidth of 2.3 GHz at broadside excitation at an local oscillator (LO) frequency of 2.5 GHz. In the notches, an IIP3 of +16 dBm and B1dB of −11.5 dBm at a 41-dB gain is achieved, improving IIP3 and B1dB by 35 and 27 dB, respectively, by spatial filtering. A single-element noise figure (NF) of 5.5–7 dB is achieved on the VM constellation corners, degrading about 2 dB on the points nearby the biggest circle fitting into a square constellation. However, sub-3-dB system NF is potentially achievable, taking into account up to 6-dB improvement by the four-element beamforming. Given both gain and phase control provided by the VM, spatial patterns with up to three independent nulls can be synthesized with the four-element antenna array. The chip of 0.52 mm2 active area consumes 77–139 mW at an LO-frequency of 0.7–5.7 GHz from a 0.8-V supply.

Frequency-Domain Modal Analysis for Power-Electronic-Based Power Systems

This letter presents two frequency-domain modal analysis methods, “peak-picking” and “circle fit” methods, used for current control interactions of grid-connected voltage-source converters (VSCs). Differing from the conventional resonance mode analysis, the modal impedances are analyzed with their Nyquist plots, instead of the magnitude-frequency response, which leads to an accurate prediction on the damping behavior of system. Experimental tests verify the theoretical modal analysis methods.