Hough Method Research Articles

On Erdős covering systems in global function fields

A covering system of the integers is a finite collection of arithmetic progressions whose union is the set of integers. A well-known problem on covering systems is the minimum modulus problem posed by Erdős in 1950, who asked whether the minimum modulus in such systems with distinct moduli can be arbitrarily large. This problem was resolved by Hough in 2015, who showed that the minimum modulus is at most 1016. In 2022, Balister, Bollobás, Morris, Sahasrabudhe and Tiba reduced Hough's bound to 616,000 by developing Hough's method. They call it the distortion method. In this paper, by applying this method, we mainly prove that there does not exist any covering system of multiplicity s in any global function field of genus g over Fq for q≥(1.14+0.16g)e6.5+0.97gs2. In particular, there is no covering system of Fq[x] with distinct moduli for q≥759.

Automatic Particle Recognition Based on Digital lmage Processing

The purpose of the research is to develop and compare various methods and algorithms for effective particle analysis based on their visual characteristics. Тhe purpose of this article is to develop and compare various methods and algorithms for effective particle analysis based on their visual characteristics. Тhe paper considers two fundamentally different approaches: the analysis of grayscale gradients and the machine learning method.Methods.Тhe research methodology includes the analysis of particle images obtained by precipitation from colloidal solutions after laser ablation and images of powder particles for selective laser melting. Тhe materials were obtained using a Quanta 200 3D electron microscope (FЕ/). For the analysis, threshold brightness binarization, contour recognition methods by the Kenny operator and the Hough algorithm are used to combine boundary points into connected contours. For comparison, the U-Net neural network solution was used, and a dataset generator was created to train the neural network. Hand-cut images of aluminum alloy powder particles and micro and nanoparticles of various metals are used as data for generation.Results.Тhe results of the study show that the Hough method provides recognition of the number of particles at the level of 80%, and the machine learning method achieves 95% accuracy in recognizing the shape of particles. Both methods can be used to analyze microand nanoparticles, including irregularly shaped particles.Conclusion.Тhe findings of the work confirm that neural networks are the optimal solution for automatic particle recognition in digital images. However, in order to create a dataset of sufficient volume, it is necessary to develop a generator of labeled images, which requires a detailed study of the subject area.

Advanced deep learning for dynamic emulsion stability measurement

Microscopic probes are used to visualise dispersions in fluid-fluid systems to characterize dispersion behaviors. An advanced multi-stage filtered Hough method and a deep learning based neural networking method (Faster R-CNN) has been compared for droplet detection performance on an image set. The comparison was made with respect to an independent manual analysis. A test analysis for droplet detection performance analysis was prepared. The Faster R-CNN method performed better relative to advanced filtered Hough method. The Faster R-CNN had within 12 % measurement error with respect to the visual or manual detection standard in terms droplet size measurement. This standardized method was utilized to analyze Exxsol D80 oil and water emulsions for evaluating droplet stability parameters. Accurate droplet stability coefficients in the inertial sub-range was evaluated alongside dependency on dispersion phase fraction. The droplet relaxation time scales for the Exxsol D80 oil and water system has been measured and reported where possible.

SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network

Change Detection in heterogeneous remote sensing images plays an increasingly essential role in many real-world applications, e.g., urban growth tracking, land use monitoring, disaster evaluation and damage assessment. The objective of change detection is to identify changes of geo-graphical entities or phenomena through two or more bitemporal images. Researchers have invested a lot in the homologous change detection and yielded fruitful results. However, change detection between heterogenous remote sensing images is still a great challenge, especially for change detection of heterogenous remote sensing images obtained from satellites and Unmanned Aerial Vehicles (UAV). The main challenges in satellite-UAV change detection tasks lie in the intensive difference of color for the same ground objects, various resolutions, the parallax effect and image distortion caused by different shooting angles and platform altitudes. To address these issues, we propose a novel method based on dual-channel fully convolution network. First, in order to alleviate the influence of differences between heterogeneous images, we employ two different channels to map heterogeneous remote sensing images from satellite and UAV, respectively, to a mutual high dimension latent space for the downstream change detection task. Second, we adopt Hough method to extract the edge of ground objects as auxiliary information to help the change detection model to pay more attention to shapes and contours, instead of colors. Then, IoU-WCE loss is designed to deal with the problem of imbalanced samples in change detection task. Finally, we conduct extensive experiments to verify the proposed method using a new Satellite-UAV heterogeneous image data set, named HTCD, which is annotated by us and has been open to public. The experimental results show that our method significantly outperforms the state-of-the-art change detection methods.

APLIKASI PENGENALAN IRIS MATA MENGGUNAKAN METODE HOUGH TRANSFORM DAN GABOR WAVELET

Biometric system is a development of the basic method of identification system by using the characteristics of humans as it’s object. These include face, fingerprints, signature, palms, iris, ears, sounds even DNA. Face recognition is one of the identification techniques in biometrics that uses part of the face as its parameter. One of the biometric parts of face is Iris. Iris is a unique part of the eyes, this is because the pattern of the somebody eyes will be quite different from the other, even genetically identical twins have different iris patterns. This research will use the Hough and Gabor method to perform iris recognition. The results show that the application has succeeded in recognizing the selected eye image if the eye image is registered in the database.

Prediction of potential pseudo-symmetry issues in the indexing of electron backscatter diffraction patterns

A new methodology to predict potential pseudo-symmetric or systematically mis-indexed orientations for an arbitrary crystal structure is presented. The method leverages the recently proposed spherical indexing algorithm to index electron backscatter diffraction patterns. Potential pseudo-symmetric orientations are interpreted as secondary peaks in the autocorrelation of the Kikuchi sphere. The generality of the method is illustrated using a number of crystal systems, ranging from nickel, where no significant pseudo-symmetric issues are expected, to SrTiO3, with mild occurrence of such issues, to the olivine series, γ-TiAl and U-6%Nb systems, where the traditional Hough method systematically mis-indexes the pseudo-symmetric variants. Furthermore, the method predicts the severity of potential pseudo-symmetric matches and ranks all variants using a normalized autocorrelation coefficient.

Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform

Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Hough accumulator matrix is constructed based on a voting scheme in parametric line space (θ, r). The variance of angles in each column is used to determine dominant building directions. We propose a hierarchical filtering and clustering approach to obtain accurate line based on detected hotspots and ordered points. An Ordered Point List matrix consisting of ordered building edge points enables the detection of line segments of arbitrary direction, resulting in high-quality building roof polygons. We tested our method on three different datasets of different characteristics: one new dataset in Makassar, Indonesia, and two benchmark datasets in Vaihingen, Germany. To the best of our knowledge, our algorithm is the first Hough method that is highly adaptable since it works for buildings with edges of different lengths and arbitrary relative orientations. The results prove that our method delivers high completeness (between 90.1% and 96.4%) and correctness percentages (all over 96%). The positional accuracy of the building corners is between 0.2–0.57 m RMSE. The quality rate (89.6%) for the Vaihingen-B benchmark outperforms all existing state of the art methods. Other solutions for the challenging Vaihingen-A dataset are not yet available, while we achieve a quality score of 93.2%. Results with arbitrary directions are demonstrated on the complex buildings around the EYE museum in Amsterdam.

A Three-Dimensional Version of the Hough Method in the Reconstruction of the External Environment and Navigation

A Three-Dimensional Version of the Hough Method in the Reconstruction of the External Environment and Navigation

Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform

Abstract This paper proposes an algorithm that assesses the angular orientation of a mobile robot with respect to its referential position or a map of the surrounding space. In the framework of the suggested method, the orientation problem is converted to evaluating a dimensional rotation of the object that is abstracted as a polygon (or a closed polygonal chain). The method is based on Hough transform, which transforms the measurement space to a parametric space (in this case, a two-dimensional space [θ, r] of straight-line parameters). The Hough transform preserves the angles between the straight lines during rotation, translation, and isotropic scaling transformations. The problem of rotation assessment then becomes a one-dimensional optimization problem. The suggested algorithm inherits the Hough method’s robustness to noise.

Oil Tanks Extraction from High Resolution Imagery Using a Directional and Weighted Hough Voting Method

Automatic objects detection, such as oil tanks, is an important application of high resolution satellite image, which can be used in disaster prevention, environment monitoring, and some military tasks. Hough Transform is one of the most commonly used method for circle target detection in the literature. However, complicated details and background in high resolution image make it difficult to find a satisfying edge map, which limits the accuracy of oil tanks extracted by edge based Circular Hough Transform methods. In this paper, we propose a novel process to detect circles directly from the gradient image rather than the edge map. By this way, we avoid the problem of edge extraction. On the other hand, by searching for the most suitable circle center in a buffer region, rather than just selecting the local maximum, we improve the accuracy of circle center location significantly. We have applied this approach to different high resolution satellite images, and find both the completeness and the accuracy of the proposed algorithm superior to the traditional edge based Hough method.

Detección de Automóviles en Escenarios Urbanos Escaneados por un Lidar

Detection of vehicles on 3D point clouds is performed by using the algorithm presented in this work. Point clouds correspond to urban environments and were acquired with the LIDAR Velodyne HDL-64E. The environment is considered semi-structured so that can be modeled using planes.Vehicle detection is carried out on to stages, segmentation and indexation. First stage is at the same time composed of three sub-stages. In the first one the principal plane (in this case the floor) is extracted, in the second sub-stage secondary planes are extracted using a tailored version of Hough's method, secondary planes are those perpendicular to the main plane. Finally in the third sub-stage and using MeanShift method, the remaining objects are segmented.Indexation on its side is divided into two sub-stages, in the first one, last segmented objects using MeanShift method are modeled using histograms according to the direction of the object's 3D points normal; in the second stage histograms are compared to those previously stored on a database of object's histograms. Optimizing of detection thresholds was carried out through ROC analysis. Two databases were used during the experiments, the first DB have 4500 objects and was used for ROC analysis training; the second one contained 3000 objects and was used for verification.

Simultaneous segmentation and classification of human actions in video streams using deeply optimized Hough transform

Most researches on human activity recognition do not take into account the temporal localization of actions. In this paper, a new method is designed to model both actions and their temporal domains. This method is based on a new Hough method which outperforms previous published ones on honeybee dataset thanks to a deeper optimization of the Hough variables. Experiments are performed to select skeleton features adapted to this method and relevant to capture human actions. With these features, our pipeline improves state-of-the-art performances on TUM dataset and outperforms baselines on several public datasets.

A Study on the Variability of Kappa ( ) in a Borehole: Implications of the Computation Process

Knowledge of the acceleration spectral shape is crucial to various applications in engineering seismology. Spectral amplitude decays rapidly at high frequencies. Anderson and Hough (1984) introduced the empirical factor κ to model this attenuation. This is the first time κ is studied in a vertical array consisting of more than two stations. We use 180 earthquakes recorded at a downhole array with five stations in soils and rock to investigate the effect of soil conditions on κ . Given that κ computation processes vary across literature when following the classic Anderson–Hough method, we investigate its variability with the different assumptions that can be made when applying the method. The estimates of κ range between 0.017 and 0.031 s at the surface and between 0.004 and 0.024 s at rock. This variability due to the assumptions made is larger than the error of each estimate and larger than the average difference in values between sediment and rock. For this data set, part of it can be attributed to the type of distance used. Given this variability, κ values across literature may not always be comparable; this may bias the results of applications using κ as an input parameter, such as ground‐motion prediction equations. We suggest ways to render the process more homogeneous. We also find that κ at rock level is not well approximated by surface records from which we deconvolved the geotechnical transfer function. Finally, we compute κ on the vertical component and find a dependence of the vertical‐to‐horizontal κ ratio on site conditions. Online Material: Table of regression parameters and figure showing the regressed lines.

Using Genetic Algorithm For Eye Location Detection

In this research a hybrid system has been made to locate the position of eyes in the human face depending on the circular form of the eye. The primary process of image has been done using Gaussian Smoothing Operator to soften the image as well as to decrease the noise in it, then Sobel Operator has also been used to show the ridges. The suggested method is to hybridize the genetic algorithm (GA) together with Hough method to show the circle (CHT) which is recalled as (GCHT). This method has been applied on some models and the result shows that it has high accuracy and high performance in determining the eye position. The research is regarded as a primary level of recognition the for security purposes.

Optimum detection and location estimation of target lines in the range-time space of a search radar

The average likelihood ratio detector is derived as the optimum detector for detecting a target line with unknown normal parameters in the range-time data space of a search radar, which is corrupted by Gaussian noise. The receiver operation characteristics of this optimum detector is derived to evaluate its performance improvement in comparison with the Hough detector, which uses the return signal of several successive scans to achieve a non-coherent integration improvement and get a better performance than the conventional detector. This comparison, which is done through analytic derivations and also through simulation results, shows that the average likelihood ratio detector has a better performance for different SNR values. This result is justified by showing the disadvantages of the Hough method, which are eliminated by the optimum detector. To have an estimate for the location of the detected target line in the optimum detection method as the Hough method, which detects and localizes the target lines simultaneously, we present the maximum a posteriori probability estimator. The estimation performance of the two methods is then compared and it is shown that the maximum a posteriori probability estimator localizes the detected target lines with a better performance in comparison with the Hough method.

An Optimized N-Dimensional Hough Filter for Detecting Spherical Image Objects

An Insight Toolkit (ITK) algorithm for detection of spherical objects using Hough methods with voting is presented in this paper. Currently, the usage of Hough methods for detecting linear and circular elements exists for 2D images in ITK. The current work extends those filters in several ways. Firstly, the new filters operate on N-dimensional images. Secondly, they work in physical coordinates which is quite essential in medical imaging modalities. Thirdly, they are optimized (multi-threaded execution, stratified sampling etc.) for usage on large datasets and show a significant speedup even in 2D and on small images. Our implementation follows the same underlying mathematics of Hough transforms (as implemented by the 2D filters) but with some minor variations. The main variation lies in the pattern of voting that involves selecting voting regions easily and efficiently accessible to region iterators rather than cones that are difficult to generalize in higher dimensions. We include 2D example code, parameter settings and show the results generated on embryonic images of the zebrafish from optical microscopy.

The estimation of buried pipe diameters using ground penetrating radar

The generalised Hough transform method is applied to the measurement of the diameters of buried cylindrical pipes by Ground Penetration Radar (GPR). 600 MHz radar scans across long pipes, buried in one metre or so of soil, show complex reflection patterns consisting of a series of inverted hyperbolic arcs. The time of flight t(y) as the probe is scanned along an axis, y, perpendicular to the pipe, shows an arc whose shape depends on four unknown variables: y 0 , the position of the centre of the pipe along the scan, z 0 , the depth of the pipe centre, R, its radius and V the velocity in the medium. Analytic expressions for the solution of these variables have been obtained. They use sets of between 1 and 4 times t, at corresponding positions y i , along the arc, depending on the number of variables to be determined. In the generalised Hough method many such sets of times are chosen randomly from points on the arcs. The results are presented for example as peaks in an accumulator space for each variable. The method is demonstrated for a 0.18 m radius concrete pipe buried at a nominal I m depth in a road. Using data acquired at 600 MHz. frequency (around 0.16 m wavelength in soil) the estimated radius was 0.174 ± 0.059 m.

The Estimation of Buried Pipe Diameters by Generalized Hough Transform of Radar Data

The generalized Hough transform method is applied to the measurement of the diameters of buried cylindrical pipes by Ground Penetration Radar (GPR). 600 MHz radar scans across long pipes, buried in one metre or so of soil, show complex reflection patterns consisting of a series of inverted hyperbolic arcs. The time of flight t(y) as the radar probe is scanned along an axis, y, perpendicular to the pipe, shows an arc whose shape and position depends on 4 unknown variables: y0, the position of the center of the pipe along the scan, z0, the depth of the pipe center, R0, its radius and V0 the velocity in the medium. Analytic expressions for the solution of these variables have been obtained. They use sets of times ti at corresponding positions yi, along the arc, depending on the number of variables to be determined. In the generalized Hough method many such sets of times are chosen randomly from points on the arcs. The results are presented for example as peaks in an accumulator space for each variable. The method is demonstrated for a 0.18 m radius concrete pipe buried at a nominal 1 m depth in a road. Using data acquired at 600MHz frequency (around 0.16m wavelength in soil) the estimated radius was 0.174 ± 0.059 m.

Three-dimensional seed reconstruction for prostate brachytherapy using Hough trajectories

In order to perform intra-operative or post-implant dosimetry in prostate brachytherapy, the 3D coordinates of the implanted radioactive seeds must be determined. Film or fluoroscopy based seed reconstruction techniques use back projection of x-ray data obtained at two or three x-ray positions. These methods, however, do not perform well when some of the seed images are undetected. To overcome this problem we have developed an alternate technique for 3D seed localization using the principle of Hough transform. The Hough method utilizes the fact that, for each seed coordinate in three dimensions, there exists a unique trajectory in Hough feature space. In this paper we present the Hough transform parametric equations to describe the path of the seed projections from one view to the next and a method to reconstruct the 3D seed coordinates. The results of simulation and phantom studies indicate that the Hough trajectory method can accurately determine the 3D seed positions even from an incomplete dataset.

Point-to-line mappings as Hough transforms

In 1962 [US Patent 3069654], Hough used a linear point-to-line mapping (PTLM) to detect large sets of collinear points in an image, by mapping the points into concurrent lines and detecting peaks where many lines intersect. In 1972, Duda and Hart [Commun. ACM 15 (1972) 11] pointed out that Hough's method is not practical, because the peaks need not lie in a bounded region. They (and others after them) therefore developed methods of detecting sets of collinear points using nonlinear point-to-curve mappings that map collinear points into concurrent curves whose intersections do lie in a bounded range. In this paper we show that any PTLM that maps collinear points into concurrent lines must be linear, and that no such PTLM can map all the sets of collinear points in an image into peaks that lie in a bounded region; thus Duda and Hart's objection applies to any PTLM-based Hough transform.