Hough Transform Research Articles

Filamentary Dust Polarization and the Morphology of Neutral Hydrogen Structures

Filamentary structures in neutral hydrogen (H i) emission are well aligned with the interstellar magnetic field, so H i emission morphology can be used to construct templates that strongly correlate with measurements of polarized thermal dust emission. We explore how the quantification of filament morphology affects this correlation. We introduce a new implementation of the Rolling Hough Transform (RHT) using spherical harmonic convolutions, which enables efficient quantification of filamentary structure on the sphere. We use this Spherical RHT algorithm along with a Hessian-based method to construct H i-based polarization templates. We discuss improvements to each algorithm relative to similar implementations in the literature and compare their outputs. By exploring the parameter space of filament morphologies with the Spherical RHT, we find that the most informative H i structures for modeling the magnetic field structure are the thinnest resolved filaments. For this reason, we find a ∼10% enhancement in the B-mode correlation with polarized dust emission with higher-resolution H i observations. We demonstrate that certain interstellar morphologies can produce parity-violating signatures, i.e., nonzero TB and EB, even under the assumption that filaments are locally aligned with the magnetic field. Finally, we demonstrate that B modes from interstellar dust filaments are mostly affected by the topology of the filaments with respect to one another and their relative polarized intensities, whereas E modes are mostly sensitive to the shapes of individual filaments.

Quality assessment of Low-cost retinal Videos for Glaucoma screening

Low-cost smartphone-compatible portable ophthalmoscopes can capture visuals of the patient’s retina to screen several ophthalmological diseases like glaucoma. The images captured have lower quality and resolution than standard retinography devices but enough for glaucoma screening. Small videos are captured to improve the chance of inspecting the eye properly; however, those videos may not always have enough quality for screening glaucoma, and the patient needs to repeat the inspection later. In this paper, a method for automatic assessment of the quality of videos captured using the D-Eye lens is proposed and evaluated with a personal dataset with 539 videos. Based on two methods developed for retina localization on the images/frames, the Circle Hough Transform method with a precision of 78,12% and the YOLOv7 method with a precision of 99,78%, the quality assessment method automatically decides on the quality of the video by measuring the number of frames of good-quality in each video, according to the chosen threshold.

Fast eye centre localization using combined unsupervised technics

Eye movements offer precious information about persons? state. Video surveillance, marketing, driver fatigue as well as medical diagnosis assistance applications manage eye behavior. We propose a new method for efficiently detecting eye movement. In this paper, we combine circle eye model with eye feature method to improve the accuracy. A set of detectors estimate the eyes centers to increase the localization rate. As a pre-processing stage, the mean of the edges yields the center of the two eye regions. Image treatment operations reduce the ROI. A Circle Hough Transform (CHT) algorithm is adopted in a modified version as a detector to find the circle eye in the image; the circle center found represents the eye's pupil estimation. We introduced the Maximally Stable Extremal Region (MSER) as a second detector, which has never been used for eye localization. Invariant to continuous geometric transformations and affine intensity changes and detected at several scales, MSERs efficiently detect regions of interest, in our case eye regions, and precisely, their centers. Ellipses fit MSERs, and their centroid estimation match eyes center. We demonstrate that the true eye centers can be found by combining these methods. The validation of the proposed method is performed on a very challenging BioID base. The proposed approach compares well with existing state-of-the-art techniques and achieves an accuracy of 82.53% on the BioID database when the normalized error is less than 0.05, without prior knowledge or any learning model.

Combining Hough Transform and Fuzzy Unsupervised Learning Strategy in Automatic Segmentation of Large Bowel Obstruction Area from Erect Abdominal Radiographs

Combining Hough Transform and Fuzzy Unsupervised Learning Strategy in Automatic Segmentation of Large Bowel Obstruction Area from Erect Abdominal Radiographs

Component Recognition and Coordinate Extraction in Two-Dimensional Paper Drawings Using SegFormer

Within the domain of architectural urban informatization, the automated precision recognition of two-dimensional paper schematics emerges as a pivotal technical challenge. Recognition methods traditionally employed frequently encounter limitations due to the fluctuating quality of architectural drawings and the bounds of current image processing methodologies, inhibiting the realization of high accuracy. The research delineates an innovative framework that synthesizes refined semantic segmentation algorithms with image processing techniques and precise coordinate identification methods, with the objective of enhancing the accuracy and operational efficiency in the identification of architectural elements. A meticulously curated data set, featuring 13 principal categories of building and structural components, facilitated the comprehensive training and assessment of two disparate deep learning models. The empirical findings reveal that these algorithms attained mean intersection over union (MIoU) values of 96.44% and 98.01% on the evaluation data set, marking a substantial enhancement in performance relative to traditional approaches. In conjunction, the framework’s integration of the Hough Transform with SQL Server technology has significantly reduced the coordinate detection error rates for linear and circular elements to below 0.1% and 0.15%, respectively. This investigation not only accomplishes the efficacious transition from analog two-dimensional paper drawings to their digital counterparts, but also assures the precise identification and localization of essential architectural components within the digital image coordinate framework. These developments are of considerable importance in furthering the digital transition within the construction industry and establish a robust foundation for the forthcoming extension of data collections and the refinement of algorithmic efficacy.

Passive TDOA Emitter Localization Using Fast Hyperbolic Hough Transform

A fast Hough transform (HT)-based hyperbolic emitter localization system is proposed to process time difference of arrival (TDOA) measurements. The position-fixing problem is provided for cases where the source is known to be on a given plane (i.e., the elevation of the source is known), while the sensors can be deployed anywhere in the three-dimensional space. The proposed solution provides fast evaluation and guarantees the determination of the global optimum. Another favorable property of the proposed solution is that it is robust against faulty sensor measurements (outliers). A fast evaluation method involving the hyperbolic Hough transform is proposed, and the global convergence property of the algorithm is proven. The performance of the algorithm is compared to that of the least-squares solution, other HT-based solutions, and the theoretical limit (the Cramér–Rao lower bound), using simulations and real measurement examples.

Hough Transform based Deep Belief Network and Improved Homomorphic Encryption for Cloud Security based Intrusion Discovery

The enlarge development in information technology is cloud computing, which offers minimized infrastructure cost, lower maintenance, greater flexibility and scalability. Nowadays, the network security plays vital role in enterprises and organizations. The influence vulnerabilities were occurred due to attackers based on network configuration. Because of cloud and IoT growth, enlarge amount of data obtained from IoT sensor and devices are transmitted to cloud data centers. Several security issues like focused web servers in the cloud and information collection mishandling are faced by storage and cloud-based computing when offering us considerable convenience. For that reason, this article proposes a deep learning-based cloud security oriented intrusion discovery. Primarily, the input dataset is pre-processed by using normalization techniques followed by the features are selected using an Adaptive White Shark Optimization (AWSO) algorithm. The normal and intrusion data is classified by using Hough Transform based Deep Belief Network (HT-DBN) after that the sensitive data are secured with the help of an Improved Homomorphic Encryption (IHE) model. The simulation tool of MATLAB is been used to simulate the proposed implementation part and the experimental results outperformed the detection accuracy of 97% than other previous approaches.

Hough Transform based Deep Belief Network and Improved Homomorphic Encryption for Cloud Security based Intrusion Discovery

The enlarge development in information technology is cloud computing, which offers minimized infrastructure cost, lower maintenance, greater flexibility and scalability. Nowadays, the network security plays vital role in enterprises and organizations. The influence vulnerabilities were occurred due to attackers based on network configuration. Because of cloud and IoT growth, enlarge amount of data obtained from IoT sensor and devices are transmitted to cloud data centers. Several security issues like focused web servers in the cloud and information collection mishandling are faced by storage and cloud-based computing when offering us considerable convenience. For that reason, this article proposes a deep learning-based cloud security oriented intrusion discovery. Primarily, the input dataset is pre-processed by using normalization techniques followed by the features are selected using an Adaptive White Shark Optimization (AWSO) algorithm. The normal and intrusion data is classified by using Hough Transform based Deep Belief Network (HT-DBN) after that the sensitive data are secured with the help of an Improved Homomorphic Encryption (IHE) model. The simulation tool of MATLAB is been used to simulate the proposed implementation part and the experimental results outperformed the detection accuracy of 97% than other previous approaches.

Adaptive Hough Transform Circle Detection Application in Developed 2D Coordinate Measuring System

Adaptive Hough Transform Circle Detection Application in Developed 2D Coordinate Measuring System

“Blood Analysis Using Image Processing”

Abstract: The human blood is a health indicator; it delivers necessary substances such as oxygen and substance that provides nourishment is necessary. Hence, segmentation of blood cells and identification of blood type is very important. The human blood consists of the RBCs, WBCs, Platelets and Plasma. Presently, lab technician tests blood groups manually and they use a device called Hemacytometer and microscope to count blood cells. But this method is extremely time consuming, monotonous and leads to the inaccurate results due to human errors. To overcome the problems regarding time, accuracy and cost, a method is proposed based on processing of images acquired from laboratory. The image processing techniques such as Segmentation, Morphological operations and Circular Hough Transform will test with some samples. In healthcare, blood testing is observed to be as one of the most significant medical examination tests. In pathology labs, different types of blood cells are counted to diagnose the diseases patient.

Non-invasive calibration for volumetric flow measurements in confined spaces

The calibration of a multi-camera system is a crucial step of volumetric flow measurements with photogrammetric methods. Conventional calibration methods are based on recording hardware targets, which are placed in the cameras fields of view. Calibrating in confined spaces with those methods is associated with an increased technical or mechanical effort. This work presents a calibration method without the use of a hardware target. Instead, crossing laser beams are introduced into the volume for creating unique calibration points. The underlying algorithms discussed in this paper for detecting the laser beams are: Ransac algorithm, Template Matching (via cross correlation) and Probabilistic Hough Transformation. The algorithms are tested with experimental data and synthetic data.

Intelligent joint multi-parameter optical performance monitoring scheme based on HT images and MT-ResNet for elastic optical network

In elastic optical network (EON), joint multi-parameter optical performance monitoring (OPM) can effectively manage, diagnose, and reduce operational costs for transmission optical links. In this paper, we propose a novel joint monitoring scheme utilizing Hough transform (HT) images combined with multi-task residual neural network (MT-ResNet) for EON. The scheme can realize baud rate identification (BRI), modulation format identification (MFI), residual chromatic dispersion identification (CDI), optical signal-to-noise ratio (OSNR) and residual differential group delay (DGD) estimation at the same time. The HT image is obtained by preprocessing original constellation diagram, which is a key feature of parameter monitoring for EON signals and presents obvious differentiation in parameter space. By optimizing the skip connections in MT-ResNet, we effectively resolve the issue of details information loss or incompleteness caused by the transmission of impaired optical signals in the neural network. The simulation results demonstrate that the identification success rate can reach 100 % for two common BRs, five mainstream MFs, and seven residual CD values with different impairment degrees. The mean absolute errors (MAEs) of OSNR and residual DGD estimates are 0.42 dB and 0.014 times symbol period respectively. The scheme has excellent tolerance for fiber nonlinear effects. In experimental verification, the accuracies of BRI and MFI are 100 %, and the MAEs of corresponding OSNR estimation for PDM-QPSK/16QAM/32QAM are 0.25 dB, 0.36 dB, and 0.40 dB, respectively. Compared with the existing typical schemes, our scheme significantly improves performance and reduces complexity while simultaneously monitoring a large number of parameters.

A novel roundness error evaluation method for high-speed EMU train axles

This article proposes a novel roundness error evaluation method for high-speed electric multiple units (EMU) train axles to evaluate the roundness error of the minimum zone circle. This method utilizes the adaptive ability of Bayesian linear regression to the data to solve the initial center of the circle, avoiding the multiple search process, which not only improves the data utilization and simplifies the solving steps but also has a strong noise immunity. The method first establishes a Hough space model, maps the collected data points to the Hough space, and forms different conical surfaces; then, by Bayesian linear regression, the probability density function is set with the distance from the space point to the conical surface as a parameter, and through continuous iteration, the point with the highest probability value is the more accurate initial center of the circle. Finally, the final optimal center of the circle is obtained by the principle of minimum inclusion region. We conducted experimental studies on axles of different diameters and compared them with other methods. The results prove the practicality and effectiveness of the technique.

Bio-inspired Sky Polarized Orientation based on the atmospheric polarization symmetry mode and Inertial Sensors

This paper presents a sky polarized orientation method based on the symmetry of the atmospheric polarization mode and the inertial sensors. Firstly, a method of extracting solar meridians based on canny operator and Hough Transform is proposed by using the symmetry of atmospheric polarization pattern. Then using the horizontal Angle information of the inertial sensor and the ephemeris information of the sun, a method for solving the yaw Angle of the polarized light is designed. Finally, a single axis rotation experiment and a vehicle-mounted dynamic experiment are carried out to verify and compare the proposed method. Compared with the least square method, the positioning accuracy of the single-axis turntable experiment is improved by 22.61%~73.86%, and that of the vehicle-mounted dynamic experiment is improved by 47.71%, which shows that the method can provide a new orientation means for unmanned vehicles and unmanned aerial vehicles.

Cancelled: Line Segment Detection Based on False Peak Suppression and Local Hough Transform and Application to Nuclear Emulsion

Cancelled: Line Segment Detection Based on False Peak Suppression and Local Hough Transform and Application to Nuclear Emulsion

AN OVERVIEW OF EXISTING LITERATURE ON DOCUMENT SKEW DETECTION

This article presents somewhat detail discussions on document image skew detection methods available in the existing literature. We first categorise the document images into several types and then we discuss different methods available to detect the skew angle of document images belonging to the individual categories. Various methods of document image skew detection include Hough Transform, Principal Components Analysis, Projection Profile based methods, Nearest Neighbor clustering, Connected Component Analysis, Cross Correlation, Radon Transform etc. Discussions on different standard databases of document image samples that have been used by the authors for experimentation purpose are included in the survey. These databases include NIST Federal Register Document Image Database, UW-I, UW-II, UW-III databases, ICDAR DISEC 2013 database, IFN/ENIT database etc. Here, we consider about eight such standard databases. Moreover, we compare different methods with respect to their accuracy and the respective range of skew angles detected by the concerned methods.

Real-time risk assessment of road vehicles based on inverse perspective mapping

Pan/Tilt/Zoom (PTZ) cameras play an important role in traffic scenes due to their wide monitoring fields and high flexibility. However, since the focal length and angle of PTZ cameras change irregularly with the monitoring needs, it is difficult to obtain accurate physical information about the real world from the image information of PTZ cameras. Aiming to address the need for real-time risk assessment of road vehicles in traffic monitoring scenarios, a vehicle position and velocity measurement scheme based on camera inverse perspective transformation is proposed, along with a method for real-time risk assessment based on the position and velocity. Specifically, Firstly, the vehicle target in the video is detected and tracked by deep learning YOLO detection algorithm and optical flow tracking algorithm. According to the obtained trajectory set, the vanishing points in the road direction are calculated by Cascade Hough Transform and the road marking lines are detected. Then, according to the vanishing point and marking line, the camera calibration task is accomplished via exploratory focal length. After camera calibration, the camera-to-road inverse perspective transformation is applied to project the image plane onto the road surface and obtain, the actual position information of vehicles. Finally, the vehicle speed measurement and real-time road risk assessment are achieved by calculating the average of instantaneous velocities across multiple frames. Simulation experiment results in a traffic monitoring scenario demonstrate that this perspective-based method for real-time road vehicle risk assessment achieves good stability and practicality, which meets the requirements for vehicle speed measurement and real-time road risk assessment.

Enhancing Microdroplet Image Analysis with Deep Learning.

Microfluidics is a highly interdisciplinary field where the integration of deep-learning models has the potential to streamline processes and increase precision and reliability. This study investigates the use of deep-learning methods for the accurate detection and measurement of droplet diameters and the image restoration of low-resolution images. This study demonstrates that the Segment Anything Model (SAM) provides superior detection and reduced droplet diameter error measurement compared to the Circular Hough Transform, which is widely implemented and used in microfluidic imaging. SAM droplet detections prove to be more robust to image quality and microfluidic images with low contrast between the fluid phases. In addition, this work proves that a deep-learning super-resolution network MSRN-BAM can be trained on a dataset comprising of droplets in a flow-focusing microchannel to super-resolve images for scales ×2, ×4, ×6, ×8. Super-resolved images obtain comparable detection and segmentation results to those obtained using high-resolution images. Finally, the potential of deep learning in other computer vision tasks, such as denoising for microfluidic imaging, is shown. The results show that a DnCNN model can denoise effectively microfluidic images with additive Gaussian noise up to σ = 4. This study highlights the potential of employing deep-learning methods for the analysis of microfluidic images.

Implementing circularity measurements in industry 4.0-based manufacturing metrology using MQTT protocol and Open CV: A case study.

In the context of Industry 4.0, manufacturing metrology is crucial for inspecting and measuring machines. The Internet of Things (IoT) technology enables seamless communication between advanced industrial devices through local and cloud computing servers. This study investigates the use of the MQTT protocol to enhance the performance of circularity measurement data transmission between cloud servers and round-hole data sources through Open CV. Accurate inspection of circular characteristics, particularly roundness errors, is vital for lubricant distribution, assemblies, and rotational force innovation. Circularity measurement techniques employ algorithms like the minimal zone circle tolerance algorithm. Vision inspection systems, utilizing image processing techniques, can promptly and accurately detect quality concerns by analyzing the model's surface through circular dimension analysis. This involves sending the model's image to a computer, which employs techniques such as Hough Transform, Edge Detection, and Contour Analysis to identify circular features and extract relevant parameters. This method is utilized in the camera industry and component assembly. To assess the performance, a comparative experiment was conducted between the non-contact-based 3SMVI system and the contact-based CMM system widely used in various industries for roundness evaluation. The CMM technique is known for its high precision but is time-consuming. Experimental results indicated a variation of 5 to 9.6 micrometers between the two methods. It is suggested that using a high-resolution camera and appropriate lighting conditions can further enhance result precision.

A Multi-Domain Joint Novel Method for ISAR Imaging of Multi-Ship Targets

As a key object on the ocean, regulating civilian and military ship targets more effectively is a very important part of maintaining maritime security. One of the ways to obtain high-resolution images of ship targets is the inverse synthetic aperture radar (ISAR) imaging technique. However, in the actual ISAR imaging process, ship targets in a formation often lead to complicated motion conditions. Due to the close distance between the ship targets, the rough imaging results of the targets cannot be completely separated in the image domain, and the small differences in motion parameters lead to overlapping phenomena in the Doppler history. Therefore, for situations in which ship formation targets with little difference in motion parameters are included in the same radar beam, this paper proposes a multi-domain joint ISAR separation imaging method for multi-ship targets. First, the method performs echo separation using the Hough transform (HT) with the minimum entropy autofocus method in the image domain. Secondly, the time–frequency curve is extracted in the time–frequency domain using the short-time Fourier transform (STFT) for time–frequency analysis, which solves the problem of the ship formation targets being aliased on both echo and Doppler history after range compression and achieves the purpose of separating the echo signals of the sub-ship targets with high accuracy. Eventually, better-focused images of each target are obtained via further motion compensation and precise imaging. Finally, the effectiveness of the proposed method is verified using a simulation and measured data.