Hough Transform Technique Research Articles

Combined image Hough transform based simultaneous multi-parameter optical performance monitoring for intelligent optical networks

The fast-emerging flexible optical networks require intelligent monitoring techniques for supervising the signal quality performance to achieve the desired efficiency. In this paper, we propose a multi-task convolutional neural network (MT-CNN) for multi-parameter monitoring using a unique combined image Hough Transform (CIHT) technique. The proposed system supports simultaneous modulation format identification (MFI), transmission distance detection (TDD), roll-off factor identification (ROFI), Laser linewidth detection (LLWD), centre wavelength detection (CWLD) along with the optical signal-to-noise ratio (OSNR) estimation. The proposed scheme realizes the multi-parameter optical performance monitoring using the advantage of Hough transform (HT) for efficient feature extraction. Rigorous simulations to prove the advantage of using Hough transform, and data augmentation has been performed to conclude the results achieved. The simulation results demonstrate 100% accuracy after 112 epochs for MFI, TDD, ROFI, LLWD, and CWLD, and the mean absolute error (MAE) for OSNR estimation has reduced to 0.354 dB after 180 epochs. Also, rigorous simulations with various conditions are performed to demonstrate the merits of the proposed method.

H i filaments as potential compass needles? Comparing the magnetic field structure of the Small Magellanic Cloud to the orientation of GASKAP-H i filaments

ABSTRACT High-spatial-resolution H i observations have led to the realization that the nearby (within few hundreds of parsecs) Galactic atomic filamentary structures are aligned with the ambient magnetic field. Enabled by the high-quality data from the Australian Square Kilometre Array Pathfinder (ASKAP) radio telescope for the Galactic ASKAP H i survey, we investigate the potential magnetic alignment of the $\gtrsim\!{10}\, {\rm pc}$-scale H i filaments in the Small Magellanic Cloud (SMC). Using the Rolling Hough Transform technique that automatically identifies filamentary structures, combined with our newly devised ray-tracing algorithm that compares the H i and starlight polarization data, we find that the H i filaments in the north-eastern end of the SMC main body (‘Bar’ region) and the transition area between the main body and the tidal feature (‘Wing’ region) appear preferentially aligned with the magnetic field traced by starlight polarization. Meanwhile, the remaining SMC volume lacks starlight polarization data of sufficient quality to draw any conclusions. This suggests for the first time that filamentary H i structures can be magnetically aligned across a large spatial volume ($\gtrsim\!{\rm kpc}$) outside of the Milky Way. In addition, we generate maps of the preferred orientation of H i filaments throughout the entire SMC, revealing the highly complex gaseous structures of the galaxy likely shaped by a combination of the intrinsic internal gas dynamics, tidal interactions, and star-formation feedback processes. These maps can further be compared with future measurements of the magnetic structures in other regions of the SMC.

Design and development of fusion-based expert system for multimodal biometric recognition with facemask authentication

A multimodal biometric system can be processed on more than one biometric feature and make the authentication process more impactful in comparison with the traditional identification system. Here in this paper, with the help of discrete cosine transformation (DCT), Hough transformation technique, and discrete wavelet transformation (DWT) we have designed a decision-level fusion-based authentication. In our proposed authentication model, the system will take the face, ear, and iris images individually and doing DCT, DWT, and Hough transformation, finally apply decision level fusion for the biometric features extraction. Our proposed system has more authentication capability due to iris authentication features, which cannot be easily tampered with. This experimental process clearly shows that the proposed multimodal systems have more efficiency, reliability, and are more robust than other existing biometric multimodal systems and are able to authenticate with or without wearing a surgical face mask, useful for hazardous and contaminated environments.

Computer Vision and Artificial Intelligence Based Puzzle Solver

This document gives formatting instructions for authors preparing papers for publication in the International Journal. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type you’re In the last decade or so, solving the Sudoku puzzle has become everyone's passion or you can say hobby. The simplicity of the puzzle's structure and the low requirement of mathematical skills caused people to possess enormous interest in accepting challenges to unravel the puzzle. Therefore, developers have tried to hunt out algorithms so on get the variability of puzzles for human players so as that they could be even solved by programming. AI-based real-time Computer Vision and puzzle solver using webcam allows providing the pc system with the Sudoku puzzle in real-time and provides the optimum solution thereto. The system not only involves the use of AI but also makes use of Computer vision, thus combining the 2 major concepts. During this system the pc tries to analyse the environment by capturing the multiple-image bursts from the important time, and from those images, it might detect the Sudoku grid lines. For the detection of the grid, the use of the Hough Transform technique has been made. Then the numbers are detected using OCR technology i.e. Optical Character Recognition. The system gets the whole knowledge of the puzzle and then computes the last word solution by making the use of AI-based strategies for getting the optimal solution in efficient manner to the Sudoku Puzzle Problem.

Iris recognition approach for identity verification with DWT and multiclass SVM.

The iris has been proven to be one of the most stable and accurate biometrics. It has been widely used in recognition systems to determine the identity of the individual who attempts to access secured or restricted areas (e.g., airports, ATM, datacenters). An iris recognition (IR) technique for identity authentication/verification is proposed in this research. Iris image pre-processing, which includes iris segmentation, normalization, and enhancement, is followed by feature extraction, and matching. First, the iris image is segmented using the Hough Transform technique. The Daugman’s rubber sheet model is the used to normalize the segmented iris area. Then, using enhancing techniques (such as histogram equalization), Gabor wavelets and Discrete Wavelets Transform should be used to precisely extract the prominent characteristics. A multiclass Support Vector Machine (SVM) is used to assess the similarity of the images. The suggested method is evaluated using the IITD iris dataset, which is one of the most often used iris datasets. The benefit of the suggested method is that it reduces the number of features in each image to only 88. Experiments revealed that the proposed method was capable of collecting a moderate quantity of useful features and outperformed other methods. Furthermore, the proposed method's recognition accuracy was found to be 98.92% on tested data.

Numerical Reader System for Digital Measurement Instruments Embedded Industrial Internet of Things

In industrial factories, many measuring instruments are used to display, for instance, pressure, voltage, temperature or humidity. Human errors are the main problem and often occur in many processes mostly done manually, such as data acquisition. Therefore, the problem of how we obtain such data automatically and correctly in real-time is important. In this paper, a numeral recognition system (NRS) is proposed based on an optical character recognition (OCR) method. The NRS embedded industrial Internet of things (IIoT) is used to serve a real-time service. Moreover, digital image processing (DIP) together with the multi-layer perceptron (MLP) is applied to efficiently recognize the numeral data. Furthermore, it is very common that the instruments' screens can face the rotation problem. This problem can be solved using the histogram of oriented gradients (HOG) and Hough transform (HT) techniques. In addition, realistic conditions under various noise types are considered such as salt and pepper (SP) noise, Gaussian noise, and Speckle noise. The system performances are evaluated in terms of confusion matrices and accuracies. The strong contribution of our proposed NRS system is that it works excellently in any situations and achieve up to 95.13 percent accuracy. From the actual experiments, we achieve an average about 95 percent accuracy. Although the NRS with the HOG and HT technique takes a bit longer computation time and more memory usage to process the images than another NRS, the system provides better results. Our proposed system is suitable for a real-time service due to low computation time.

Angle histogram of Hough transform as shape signature for visual object classification - (AHOC)

This work presents a new method for object classification using Hough transform (HT) and angle histogram as an object signature. Several methods are reported in the literature that exploit HT and other techniques as a pre-processing step to characterise objects to be used in detection, recognition, classification, etc. HT is a powerful technique to extract shape features from 2D objects; it has been used in many studies and implemented successfully in many applications. Our study is unique by post processing HT voting space using a binary threshold then computing an angle histogram of the resulting angle space as a shape signature of objects. Our image set consisted of 25 simple geometric shapes and six complex natural object classes of: trees, people, cars, airplanes, houses and horses. The method was trained and tested using 225 images from six different classes and found to be robust with a classification accuracy of 95.83%.

Nautical radar clutter suppression and small target detection based on image spectrum filtering and Hough transform

Here, a new clutter suppression and small target detection method was presented for nautical radars based on image spectrum filtering and Hough transform (HT). It is well known that sea clutter can be decomposed into texture component (TC) and spiky component (SC). First, the raw echo image was transferred to spectrum field, taking use of the narrow band feature of TC clutter in the image spectrum field, a 2D filter named as invert clutter response filter was designed to filter the energy of TC clutter in image spectrum field and then recovering the echo image. After that, a 2D CFAR detector was used to get a coarse detection report. Since the influence of the remaining SC clutter, quite a lot of false-alarmed detections may appear for the coarse detections. To solve this problem, the HT technique was proposed to extract real target track lines from many but distributed SC clutter caused false alarms. Measured and simulated results show that the proposed method is quite effective.

Finding a suitable threshold value for an iris-based authentication system

<span>Authentication is the first line of defense of any information technology systems. One of the popular methods used today is biometric, and iris authentication is gaining popularity. However, the threshold value is deemed to be secure and appropriate has not been thoroughly studied. Threshold is a value that defines the acceptable amount of the correct bits of the image before securely passing the authentication process. Therefore, the main aim of this research was to find a secure and suitable threshold value used in iris authentication system, where iris localization was done by using Circle Hough Transform technique. Iris image databases v.4 from the Chinese Academy of Sciences Institute of Automatic (CASIA) were used in this research. The way to find the appropriate threshold was to test for the right balance of the GAR, FRR and FAR values when trying to verify the person’s identity. The results of the test revealed that the appropriate threshold had the value of 72.9246 percent of all the available bits of the iris image. Both had a high GAR and very low FAR and FRR values. It can be concluded that the obtained threshold value was suitable and secure.</span>

Automated Spatiotemporal Analysis of Fibrils and Coronal Rain Using the Rolling Hough Transform

A technique is presented that automates the direction characterization of curvilinear features in multidimensional solar imaging data sets. It is an extension of the Rolling Hough Transform (RHT) technique presented by Clark, Peek, and Putman (Astrophys. J. 789, 82, 2014), and it excels at rapid quantification of spatial and spatiotemporal feature orientation even for low signal to noise applications. It operates on a pixel-by-pixel basis within a data set and reliably quantifies orientation even for locations not centered on a feature ridge, which is used here to derive a quasi-continuous map of the chromospheric fine structure projection angle. For time-series analysis, a procedure is developed that uses a hierarchical application of the RHT to automatically derive apparent motion of coronal rain observed off-limb. Essential to the success of this technique is this paper's formulation for the RHT error analysis as it provides a means to properly filter results.

Recognition of feature curves on 3D shapes using an algebraic approach to Hough transforms

Feature curves are largely adopted to highlight shape features, such as sharp lines, or to divide surfaces into meaningful segments, like convex or concave regions. Extracting these curves is not sufficient to convey prominent and meaningful information about a shape. We have first to separate the curves belonging to features from those caused by noise and then to select the lines, which describe non-trivial portions of a surface. The automatic detection of such features is crucial for the identification and/or annotation of relevant parts of a given shape. To do this, the Hough transform (HT) is a feature extraction technique widely used in image analysis, computer vision and digital image processing, while, for 3D shapes, the extraction of salient feature curves is still an open problem.Thanks to algebraic geometry concepts, the HT technique has been recently extended to include a vast class of algebraic curves, thus proving to be a competitive tool for yielding an explicit representation of the diverse feature lines equations. In the paper, for the first time we apply this novel extension of the HT technique to the realm of 3D shapes in order to identify and localize semantic features like patterns, decorations or anatomical details on 3D objects (both complete and fragments), even in the case of features partially damaged or incomplete. The method recognizes various features, possibly compound, and it selects the most suitable feature profiles among families of algebraic curves.

Tracking within Hadronic Showers in the CALICE SDHCAL prototype using a Hough Transform Technique

The high granularity of the CALICE Semi-Digital Hadronic CALorimeter (SDHCAL) provides the capability to reveal the track segments present in hadronic showers. These segments are then used as a tool to probe the behaviour of the active layers in situ, to better reconstruct the energy of these hadronic showers and also to distinguish them from electromagnetic ones. In addition, the comparison of these track segments in data and the simulation helps to discriminate among the different shower models used in the simulation. To extract the track segments in the showers recorded in the SDHCAL, a Hough Transform is used after being adapted to the presence of the dense core of the hadronic showers and the SDHCAL active medium structure.

Alignment Motion Control for an Automated Human Ear Surgery via Vision‐Servoing

AbstractTo treat a worldwide common ear disease called Otitis Media with Effusion, a robotic device allowing fast and automatic grommet tube insertion has been designed in our earlier works. However, the previous version has to be manually placed close to the tympanic membrane before the insertion procedures. For the purpose of realizing a fully automated surgical process, locating and realizing a suitable insertion path from outside the ear forms a crucial step. It is then desirable to control the working channel to be aligned to the axial direction of auditory canal from the vision channel. In this paper, a fast and robust vision‐based servomechanism is proposed to achieve the planar alignment. To detect and track the region of auditory canal through the image sequences, an image segmentation algorithm based on the phase information and combining with the hough transformation technique is proposed. With the help of Kalman filter, a new look‐and‐move servomechanism can be achieved by utilizing the visual and motion measurements simultaneously, resulting in an effective and robust servoing performance.

Discrete-Fracture-Network Generation From Microseismic Data by Use of Moment-Tensor- and Event-Location-Constrained Hough Transforms

Summary Reservoir simulation and prediction of production associated with hydraulic-fracturing require the input of the fracture geometry and the fracture properties such as the porosity and retained permeability. Various methods were suggested and applied for deriving discrete fracture networks (DFNs) from microseismic data as a framework for modeling reservoir performance. Although microseismic data are the best diagnostics for revealing the volume of rock fractured, its incompleteness in representing the deformation induced presents a challenge to calibrate and represent complex fracture networks created and connected during hydraulic-fracture stimulation. We present an automated method to generate DFN models constrained by the microseismic locations and fracture plane orientations derived from moment-tensor analysis. We use a Hough-transform technique to find significant planar features from combinations of the microseismic source locations. We have modified the technique with an equal-probability voting scheme to remove an inherent bias for horizontal planes. The voting mechanism is a general grid search in the space of fracture strike, dip, and location (φ,θ,r, respectively) with grid-cell sizes scaled by uncertainty estimates of φ,θ,r. We constrain fracture orientations with weighting on the basis of the moment-tensor orientations of neighboring events and their associated uncertainties. With two case studies, we demonstrate that our automated technique can reliably extract the complex fracture network on the basis of good matches with the event-cloud trends and the input moment-tensor orientations. We also tested the sensitivity of the technique to event-location uncertainty. With increasing location uncertainty, the details of the fracture network extracted are diminished with events grouping to larger-scale features, but the general shape and orientation of the fracture network obtained are insensitive to the location uncertainty.

A Precise Lane Detection Algorithm Based on Top View Image Transformation and Least-Square Approaches

The next promising key issue of the automobile development is a self-driving technique. One of the challenges for intelligent self-driving includes a lane-detecting and lane-keeping capability for advanced driver assistance systems. This paper introduces an efficient and lane detection method designed based on top view image transformation that converts an image from a front view to a top view space. After the top view image transformation, a Hough transformation technique is integrated by using a parabolic model of a curved lane in order to estimate a parametric model of the lane in the top view space. The parameters of the parabolic model are estimated by utilizing a least-square approach. The experimental results show that the newly proposed lane detection method with the top view transformation is very effective in estimating a sharp and curved lane leading to a precise self-driving capability.

An Improved Method for Optic Disc Localization

An accurate approach for localization and segmentation of an optic disk (OD) in the retinal images is one of the most imperative tasks in an automated screening system. The retinal fundus images analysis is extensively used in the diagnosis and treatment of several eye diseases such as glaucoma and diabetic retinopathy. This research brings out a new algorithm that has not been used before to detect and segment the optic disk in all categories of retinal images, specifically healthy retinal images plus anomalous, or in other words fundus images affected due to diseases. The technique adopted for the separation of the optic disk is centered on histogram specification, mathematical morphological operations includes erosion as well as dilation along with proper thresholding and detection of circles using the Hough Transform technique. The proposed procedure has been tested on standard databases provided for ophthalmic image processing researches on internet, Diaretdb0, Diaretdb1 and local databases collected from the National Eye Hospital and the Vision Care (Pvt) Ltd. The proposed algorithm is able to locate the OD in 90% of all tested images.

Real-Time Computer Vision System for Continuous Face Detection and Tracking

The ever-increasing number of traffic accidents due to a diminished driver’s vigilance level has become a problem of serious concern to society. With the ever growing traffic conditions, this problem will further deteriorate. For this issue, development of system which can actively monitors driver vigilance level and alert the driver for any insecure driving condition is essential. So this paper gives detailed information about driver vigilance level monitoring system. The ultimate goal of the system is to detect and alert the driver from insecure sleepy or low concentration driving condition. The system consists of two main modules including drivers face and eye detection module and drivers face tracking module. Viola Jones face detection with AdaBoost (AdaptiveBoosting) method and Circular Hough Transform technique are integrated in the drivers face and eye detection module. In the drivers face tracking module, CAMSHIFT (Continuously Adaptive Mean Shift) algorithm has been used for continuous face tracking of driver. The main components of the system consist of a video camera, a specially designed hardware system based on Raspberry Pi for real-time image processing and controlling the alarm system. In the proposed system, only one video camera is used in practice yet an achievement of fast and accurate detection results are obtained.

Results of the CALICE SDHCAL technological prototype

The SDHCAL prototype was completed in 2012, and exposed to beams of pions, electrons of different energies at the SPS of CERN for a total time period of 5 weeks. The data are being analyzed within the CALICE Collaboration. Preliminary results indicate that a highly granular hadronic calorimeter conceived for PFA application is also a powerful tool to measure hadronic particle energy. In addition it was found to discriminate efficiently pions from electrons. The use of multi-threshold readout mode shows a clear improvement of the resolution at energies exceeding 30 GeV with respect to the binary readout mode. New ideas to improve on the energy resolution using the topology of hadronic showers such as the Hough Transform technique are studied.

English

This paper discusses about the design and fabrication of vision based Autonomous Underwater Cable Tracking (AUCT) system and its working principle based on the propulsion technique. The AUCT system consists of three levels of controllers namely, higher level controller, Middle level controller and the Lower level controller to navigate the Autonomous Underwater Vehicle (AUV). A single CCD camera and an Ultrasonic distance sensor are used to determine the relative position of the underwater cable with respect to the Autonomous Underwater Vehicle (AUV) in a 3 dimensional space. Visual data provides two dimensions and ultrasonic distance sensor data provides the 3rd dimension. An image filtering technique to reduce some undesirable features is underwater images, is used based on the morphological operator. A real-time algorithm is developed to determine the position of the cable in the image plane by cascading an estimator governed by AUV dynamics, with help of Hough transformation technique. The proposed system will perform very well other than in situations where the cable is covered by waterweeds for a long distance. This system can be used as a solution to the problem of underwater positioning especially in pre-determined areas.

Geometrical motifs search in proteins: A parallel approach

We present CMS, an algorithm used to search for geometric motifs in proteins.Complete cross-proteins analysis calls for parallel processing.Data parallel problem decomposition favors a shared-memory implementation.OpenMP implementation meets expectations, but scales only up to 8 threads.Hybrid OpenMP/MPI approach required for further analysis. The analysis of the 3D structures of proteins is a very important problem in life sciences, since the geometric set-up of proteins has a deep relevance in many biological processes. The complexity of the analysis and the continuous increase in the number of proteins whose 3D structure is known, call for efficient and quick algorithms. Parallel processing is becoming an enabling tool for such research. A key component in the geometric description of a protein is the structural motif, a 3D element which appears in a variety of molecules and is usually made of just a few simpler structures, the secondary structures elements (SSEs).This paper is an extended version of Ferretti and Musci (2013), and presents the Cross Motif Search (CMS) and the Complete CMS (CCMS) algorithms, two highly optimized and efficient parallel methods to detect the presence and location of all common motifs of secondary structures in a given protein pair (CMS) or across an arbitrary large dataset of proteins (CCMS). The analysis builds on existing approaches, such as Secondary Structure Co-Occurrences (SSC), based on the General Hough Transform (GHT) technique. The main difference between our proposal and the state of the art is the innovative focus that CMS puts on the geometric description of the structural motifs, which could be simply viewed as vectors in a 3D space, rather than on the topological/biological description employed by competing algorithms, such as ProSMoS, PROMOTIF or MASS. The advantage of a geometrical approach is that it enables to retrieve the exact location of the common substructures in a protein pair.The paper analyzes all possible forms of serial and parallelism optimization of the proposed algorithms, both shared memory and message passing. It introduces a complete parallel implementation of CMS, based on OpenMP, and discusses its scalability on shared-memory architectures. Both small-scale and medium-scale testing shows that the methods produces very interesting results in real applications, and scales nicely up to the eight-processor limit. More in-depth testing also shows that the scalability limit is due to the inner structure of the problem, and that the similarities among proteins and the chosen tolerance for the analysis highly affect the overall performance.