Improved fuzzy identification method based on Hough transformation and fuzzy clustering

This paper presents an approach that is useful for the identification of a fuzzy model in SISO system. The initial values of cluster centers are identified by the Hough transformation, which considers the linearity and continuity of given input-output data, respectively. For the premise parts parameters identification, we use fuzzy-C-means clustering method. The consequent parameters are identified based on recursive least square. This method not only makes approximation more accurate, but also let computation be simpler and the procedure is realized more easily. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.

Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.

Ensemble-based optimization of hydraulically fractured horizontal well placement in shale gas reservoir through Hough transform parameterization

Shale gas reservoirs have been successfully developed due to the advancement of the horizontal well drilling and multistage hydraulic fracturing techniques.However,the optimization design of the horizontal well drilling,hydraulic fracturing,and operational schedule is a challenging problem.An ensemble-based optimization method(EnOpt)is proposed here to optimize the design of the hydraulically fractured horizontal well in the shale gas reservoir.The objective is to maximize the net present value(NPV)which requires a simulation model to predict the cumulative shale gas production.To accurately describe the geometry of the hydraulic fractures,the embedded discrete fracture modeling method(EDFM)is used to construct the shale gas simulation model.The efects of gas absorption,Knudsen difusion,natural and hydraulic fractures,and gas-water two phase fow are considered in the shale gas production system.To improve the parameter continuity and Gaussianity required by the EnOpt method,the Hough transformation parameterization is used to characterize the horizontal well.The results show that the proposed method can efectively optimize the design parameters of the hydraulically fractured horizontal well,and the NPV can be improved greatly after optimization so that the design parameters can approach to their optimal values.

Measurement of the high energyγ-rays from heavy ion reactions usingČerenkov detector

The energetic bremsstrahlung photons up to 100 MeV produced in heavy ion collisions can be used as a sensitive probe for short-range correlation in atomic nuclei. The energy of the γ-rays can be measured by collecting the Čerenkov light in the medium induced by the fast electrons generated in the Compton scattering or electromagnetic shower of the incident γray. Two types of detectors based on pure water and lead glass as sensitive materials were designed for this purpose. The γresponse and optical photon propagation in the detectors were simulated based on electromagnetic and optical processes in Geant4. The inherent energy resolutions of 0.022(4) + 0.51(2)∕E^(1/2)_(γ) for water and 0.0026(3) + 0.446(3)∕E^(1/2)_(γ) for lead glass were obtained. The geometry sizes of the lead glass and water were optimized to 30 cm × 30 cm × 30 cm and 60 cm × 60 cm ×120 cm, respectively, to detect high-energy γ-rays at 160 MeV. The Hough transform method was applied to reconstruct the direction of the incident γ-rays, providing the ability to experimentally distinguish the high-energy γ-rays produced in the reactions on the target from random background cosmic-ray muons.

Adaptive modified hough transform track initiator forHFSWR tracking of fast and small targets

High frequency surface wave radar (HFSWR) is well proved to have over the horizon (OTH) detection capability to weak aerial targets, such as concealed airplanes or cruise missiles. The most important problem of detection of fast and small targets using HFSWR is earlier warning, i.e. enlargement of detection range oftargets. Therefore, the detection threshold should be decreased as low as possible, but numerous false alarms are brought about at the same time. On this condition, conventional track initiation techniques, which normally require the probability of false alarm to be at the level of 10-6, will initiate enormous false tracks and lead to abnormal operation of tracking system. An adaptive modified hough transform (AMHT) track initiator is proposed accordingly and the relation of detection range to the performance of track initiator is analyzed in this paper. Simulations are performed to confirm the capability of track initiation to fast and small targets in dense clutter by AMHT track initiator. The tolerable probability of false alarm of detector can reach the level of 10 -3 . And it performs better than track initiator based on modified hough transform (MHT).

Detection of weak target for MIMO radar based on Hough transform

An effective method of multiple input multiple output （MIMO） radar weak target detection is proposed based on the Hough transform. The detection time duration is divided into multiple coherent processing intervals （CPIs）. Within each CPI, conventional methods such as fast Fourier transform （FFT） is exploit to coherent inte- grating in same range cell. Furthermore, noncoherent integration through several range cells can be implemented by Hough transform among all CPIs. Thus, higher integration gain can be obtained. Simulation results are also given to demonstrate that the detection performance of weak moving target can be dramatically improved.

Weld extraction from real-time radiograph based on bidirectional accumulator Hough Transform

An effective approach is presented to extract welds from real-time radiographs, Firstly an algorithm based on an adaptive bidirectional threshold was proposed to segment the gradient image into ternary image, and then the bidirectional accumulator Hough Transform was developed to extract weld edges from the ternary image. Different values of the coefficient proposed in the threshold algorithm were tested, and the proposed approach was applied to extract welds from real-time radiographic images of different types of welds with defects. Results show that the proposed method is adaptive and effective to extract welds from real-time radiographs of linear welds.

A NEW METHOD OF BAD POINTS ELIMINATION BASED ON HOUGH TRANSFORM

In experimental tests, besides data in range of allowable error, the experimenters usually get some unexpected wrong data called bad points. In usual experimental data processing, the method of bad points exclusion based on automatic programming is seldom taken into consideration by researchers. This paper presents a new method to reject bad points based on Hough transform, which is modified to save computational and memory consumptions. It is fit for linear data processing and can be extended to process data that is possible to be transformed into and from linear form; curved lines, which can be effectively detected by Hough transform. In this paper, the premise is the distribution of data, such as linear distribution and exponential distribution, is predetermined. Steps of the algorithm start from searching for an approximate curve line that minimizes the sum of parameters of data points. The data points, whose parameters are above a self-adapting threshold, will be deleted. Simulation experiments have manifested that the method proposed in this paper performs efficiently and robustly.

A MODIFIED DYNAMIC GENERALIZED HOUGHTRANSFORM FOR LINE DETECTING

ＡＭＯＤＩＦＩＥＤＤＹＮＡＭＩＣＧＥＮＥＲＡＬＩＺＥＤＨＯＵＧＨＴＲＡＮＳＦＯＲＭＦＯＲＬＩＮＥＤＥＴＥＣＴＩＮＧＹａｎｇＬｕｘｉ（杨绿溪）ＨｅＺｈｅｎｙａ（何振亚）（ＤｅｐａｒｔｍｅｎｔｏｆＲａｄｉｏＥｎｇｉｎｅｅｒｉｎｇ）ＡＭＯＤＩＦＩＥＤＤＹＮ...

Scattering Center Extraction of UWB Radio Fuze's Target Based on Polar Hough Transform

In order to extract the feature information of ultra wide-band (UWB) radio fuze target and give full play to the warhead's strike ability, a method based on polar Hough transform for scattering centers extraction of the target was proposed in this paper. It firstly utilized the fuze scanning to obtain the distance and azimuth information of the target's main scattering centers at different times, i.e. the track information of scattering centers under the polar coordinates, then used the polar Hough transform to transform the track into the parameter space in order to accumulate the dots and drew 3-D parameter space diagram, in which the peak points corresponded to the target's scattering centers. The simulation results indicate that the method can not only extract scattering centers efficiently and accurately, but also has strong anti-noise performance, and the algorithm is simple and easy to be implemented in engineering.

Shell Casing Size Measurement Method Based on Improved Hough Transform

In order to improve the accuracy and efficiency of the measurement of the Shell Case size,a measurement system based on machine vision is introduced.Through the preprocessing,threshold segmentation algorithm and edge detection of the shell case image.Correlated size measurement of lines and circles in contour using binary search method and Hough transform.The experiment of detecting four sizes of 100 qualified shells shows that the system can realize the rapid and accurate detection of shell casings,and can be used to assess their quality performance.It effectively improves the detection efficiency and further improves the automation level of detection,which has a good application prospect.

An Efficient Bar Code Image Recognition Algorithm for Sorting System

In the sorting system of the production line,the object movement,fixed angle of view,light intensity and other reasons lead to obscure blurred images.It results in bar code recognition rate being low and real time being poor.Aiming at the above problems,a progressive bar code compressed recognition algorithm is proposed.First,assuming that the source image is not tilted,use the direct recognition method to quickly identify the compressed source image.Failure indicates that the compression ratio is improper or the image is skewed.Then,the source image is enhanced to identify the source image directly.Finally,the inclination of the compressed image is detected by the barcode region recognition method and the source image is corrected to locate the barcode information in the barcode region recognition image.The results of multitype image experiments show that the proposed method is improved by 5+times computational efficiency compared with the former methods,and can recognize fuzzy images better.

A New Method for Correcting Vehicle License Plate Tilt

In the course of vehicle license plate （VLP） automatic recognition, tilt correction is a very crucial process. According to Karhunen-Loeve （K-L） transformation, the coordinates of characters in the image are arranged into a two-dimensional covariance matrix, on the basis of which the centered process is carried out. Then, the eigenvector and the rotation angle α are computed in turn. The whole image is rotated by -α. Thus, image horizontal tilt correction is performed. In the vertical tilt correction process, three correction methods, which are K-L transformation method, the line fitting method based on K -means clustering （LFMBKC）, and the line fitting based on least squares （LFMBLS）, are put forward to compute the vertical tilt angle θ. After shear transformation （ST） is imposed on the rotated image, the final correction image is obtained. The experimental results verify that this proposed method can be easily implemented, and can quickly and accurately get the tilt angle. It provides a new effective way for the VLP image tilt correction as well.

Localized Model and Arithmetic System Based on Two Image Sensors Under Complex Circumstance

Two image sensors simulate directly the way of disposing images with the human＇s two eyes, so it has important value to apply in many domains, such as object identification, small unmaned aerial vehicle （UAV）, workpiece localization, robot navigation and so on. The object localization based on two image sensots is studied in this paper. It concentrates on how to apply two charge coupled device （CCD） image sensors to object localization of sphere in complex environments. At first a space model of the two image sensors is set up, then Hough transformation is adopted to get localizated model and arithmetic system. An experiment platform is built in order to prove the correctness and feasibility of that localization algorithm.

Improved multi-target radar TBD algorithm

Considering radar detection for multi-target recognition, a track before detect （TBD） algorithm based on Hough transform is adopted for identifying and tracking multi-target radar. By increasing the dimensions of the target characteristic parameters, the target detection and track accuracy is increased. Also, by multilevel filtering processing, the diverging points of the echo signal are condensed, which improves the performance of identifying and tracking multiple targets. Simulation results show that compared with traditional TBD algorithms, the presented algorithm has better performance in the aspects of multi-target tracking, detecting and distinguishing.

Vision-Based System of AUV for An Underwater Pipeline Tracker

This paper describes a new framework for detection and tracking of underwater pipeline, which includes software system and hardware system. It is designed for vision system of AUV based on monocular CCD camera. First, the real-time data flow from image capture card is pre-processed and pipeline features are extracted for navigation. The region saturation degree is advanced to remove false edge point group after Sobel operation. An appropriate way is proposed to clear the disturbance around the peak point in the process of Hough transform. Second, the continuity of pipeline layout is taken into account to improve the efficiency of line extraction. Once the line information has lbeen obtained, the reference zone is predicted by Kalman filter. It denotes the possible appearance position of the pipeiine in the image. Kalman filter is used to estimate this position in next frame so that the information of pipeline of each frame can be known in advance. Results obtained on real optic vision data in tank experiment are displayed and discussed. They show that the proposed system can detect and track the underwater pipeline online, and is effective and feasible.

Velocity estimation of moving targets with stepped-frequency radar based on Doppler frequency difference

By analyzing the signal model of stepped-frequency waveform, a novel method for velocity measurement is proposed. The method is based on Doppler frequency difference which is achieved by using Hough transform. As the estimated velocity is inversely proportional to the frequency step size instead of the carrier frequency of the transmitted signal as the pulse-Doppler （PD） processing, the new algorithm can achieve much wider unambiguous velocity range. Furthermore, non-coherent integration of the sub-pulses with different carrier frequencies can be implemented by Hough trans- form to improve the anti-noise performance. Besides, field experimental results show that the high range resolution profile （HRRP） of a bullet with high speed can be reconstructed correctly without distortion.

Parameter estimation of maneuvering targets in OTHR based on sparse time-frequency representation

This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar（OTHR）. In this method, the sparse time-frequency distribution of the radar echo is obtained by solving a sparse optimization problem based on the short-time Fourier transform. Then Hough transform is employed to estimate the parameter of the targets. The proposed algorithm has the following advantages： Compared with the Wigner-Hough transform method, the computational complexity of the sparse optimization is low due to the application of fast Fourier transform（FFT）. And the computational cost of Hough transform is also greatly reduced because of the sparsity of the time-frequency distribution. Compared with the high order ambiguity function（HAF） method, the proposed method improves in terms of precision and robustness to noise. Simulation results show that compared with the HAF method, the required SNR and relative mean square error are 8 dB lower and 50 dB lower respectively in the proposed method. While processing the field experiment data, the execution time of Hough transform in the proposed method is only 4% of the Wigner-Hough transform method.

Robust Lane Detection and Tracking Based on Machine Vision

Lane detection based on machine vision,a key application in intelligent transportation,is generally characterized by gradient information of lane edge and plays an important role in advanced driver assistance systems(ADAS).However,gradient information varies with illumination changes.In the complex scenes of urban roads,highlight and shadow have effects on the detection,and non-lane objects also lead to false positives.In order to improve the accuracy of detection and meet the robustness requirement,this paper proposes a method of using top-hat transformation to enhance the contrast and filter out the interference of non-lane objects.And then the threshold segmentation algorithm based on local statistical information and Hough transform algorithm with polar angle and distance constraint are used for lane fitting.Finally,Kalman filter is used to correct lane lines which are wrong detected or missed.The experimental results show that computation times meet the real-time requirements,and the overall detection rate of the proposed method is 95.63%.