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.

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.

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.

A MODIFIED DYNAMIC GENERALIZED HOUGHTRANSFORM FOR LINE DETECTING

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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.

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.

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 transform 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.

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%.

Stand parameter extraction based on video point cloud data

Monitoring sample plots is important for the sustainable management of forest ecosystems.Acquiring resource data in the field is labor-intensive,time-consuming and expensive.With the rapid development of hardware technology and photogrammetry,forest researchers have turned two-dimensional images into three-dimensional point clouds to obtain resource information.This paper presents a method of sample plot analysis using two charge-coupled device(CCD) cameras based on video photography.A handheld CCD camera was used to shoot the sample plot by surrounding a central tree.Video-based point clouds were used to detect and model individual tree trunks in the sample plots and the DBH of each was estimated.The experimental results were compared with field measurement data.The results show that the relative root mean squared error(rRMSE) of the DBH estimates of individual trees was 2.1-5.7%,acceptable for practical applications in traditional forest inventories.The rRMSE of height estimates was2.7-36.3%.Average DBH and heights,and tree density and volume were calculated.Video-based methods require compact observation instruments,involve low costs during field investigations,acquire data with high efficiency,and point cloud data can be processed automatically.Furthermore,this method can directly extract information on the relative position of trees,which is important to show distribution visually and provides a basis for researchers to regulate stand density.Additionally,video photography with its unique advantages is a technology warranting future attention for forest inventories and ecological construction.

Posture Adjustment of Microphone Based on Image Recognition in Automatic Welding System

As the requirements of production process is getting higher and higher with the reduction of volume,microphone production automation become an urgent need to improve the production efficiency.The most important part is studied and a precise algorithm of calculating the deviation angle of four types microphones is proposed,based on the feature extraction and visual detection.Pretreatment is performed to achieve the real-time microphone image.Canny edge detection and typical feature extraction are used to distinguish the four types of microphones,categorizing them as type M1 and type M2.And Hough transformation is used to extract the image features of microphone.Therefore,the deviation angle between the posture of microphone and the ideal posture in 2Dplane can be achieved.Depending on the angle,the system drives the motor to adjust posture of the microphone.The final purpose is to realize the high efficiency welding of four different types of microphones.

Design of a Tree Ring Structure Analysis System to Estimate the Accurate Age of Tree Species in Sri Lanka

Determination of an age in a particular tree species can be considered as a vital factor in forest management.In this research we have introduced a novel scheme to determine the accurate age of the tree species in Sri Lanka.This is initially developed for the tree species called‘Hora’(Dipterocarpus zeylanicus)in wet zone of Sri Lanka.Here the core samples are extracted and further analyzed by means of the different image processing techniques such as Gaussian kernel blurring,use of Sobel filters,double threshold analysis,Hough line tran sformation and etc.The operations such as rescaling,slicing and measuring are also used in line with image processing techniques to achieve the desired results.Ultimately a Graphical user interface(GUI)is developed to cater for the user requirements in a user friendly environment.It has been found that the average growth ring identification accuracy of the proposed system is 93%and the overall average accuracy of detecting the age is 81%.Ultimately the proposed system will provide an insight and contributes to the forestry related activities and researches in Sri Lanka.

Lapel Pattern Recognition in Flat Sketches Based on Lapel Model

Taking lapels as examples,the purpose of this paper is to study the problem of recognizing the same clothing component in the flat sketches. Supposing that the four lines in a lapel are concurrent and symmetrical,a mathematical lapel model is built. Then,a novel method for lapel recognition in fashion flat sketches based on the lapel model is proposed. In the image preprocessing stage, the images need to be cropped for two times in order to remove the blank background and extract the region of interest,respectively. In the concurrent recognition stage,line detection,θ selection,and curve fitting are applied to limiting candidate lines. In the symmetrical recognition stage, k-means clustering algorithm is employed to divide the selected lines into four clusters. The threshold values of the difference of corresponding weighted θ are set as lapel recognition criteria. Experiments demonstrate that the recognition accuracy of the proposed method is obtained at about 91. 7%.

Multi-Target Track Initiation in Heavy Clutter

In the heavy clutter environment, the information capacity is large,the relationships among information are complicated, and track initiationoften has a high false alarm rate or missing alarm rate. Obviously, it is adifficult task to get a high-quality track initiation in the limited measurementcycles. This paper studies the multi-target track initiation in heavy clutter.At first, a relaxed logic-based clutter filter algorithm is presented. In thealgorithm, the raw measurement is filtered by using the relaxed logic method.We not only design a kind of incremental and adaptive filtering gate, but alsoadd the angle extrapolation based on polynomial extrapolation. The algorithm eliminates most of the clutter and obtains the environment with highdetection rate and less clutter. Then, we propose a fuzzy sequential Houghtransform-based track initiation algorithm. The algorithm establishes a newmeshing rule according to system noise to balance the relationship between thegrid granularity and the track initiation quality. And a flexible superpositionmatrix based on fuzzy clustering is constructed, which avoids the transformation error caused by 0–1 voting method in traditional Hough transform.In addition, the algorithm allows the superposition matrixes of nonadjacentcycles to be associated to overcome the shortcoming that the track can’t beinitiated in time when the measurements appear in an intermittent way. Anda slope verification method is introduced to detect formation-intensive serialtracks. Last, the sliding window method is employed to feedback the trackinitiation results timely and confirm the track. Simulation results verify thatthe proposed algorithms can initiate the tracks accurately in heavy clutter.

Study on Detection and Recognition of Speed Signs

Speed sign detection and recognition are the important part of the driving assistant systems.Combining gradient-based random Hough transform with BP network,a method is proposed to detect and recognize speed signs.Firstly,the gradient-based random Hough transform is used to detect and locate the speed signs.Then four contour features of the 'digit' inside the speed signs are extracted and recognized using the method of BP network.The results show that this method can detect and recognize the speed signs accurately and efficiently.

Study on the vehicle license plate tilt correction

The shortage of current different approaches of the vehicle license plate(VLP) tilt correction is analyzed in the paper and a new rotary correction method put forward based on the former ways of the VLP tilt correction in the horizontal direction and the vertical direction Owing to the VLP tilt taking place in the vertical direction,the array of the image’s pixels of the same column is broken,and even different rows come into being superposition.The VLP tilt taking place in the horizontal direction,by which the array of the image’s pixels of the same row broken,and so much as different columns come into being superposition.

Apple detection from apple tree image based on BP neural network and Hough transform

Using machine vision to accurately identify apple number on the tree is becoming the key supporting technology for orchard precision production management.For adapting to the complexity of the field environment in various detection situations,such as illumination changes,color variation,fruit overlap,and branches and leaves shading,a robust algorithm for detecting and counting apples based on their color and shape modes was proposed.Firstly,BP(back propagation)neural network was used to train apple color identification model.Accordingly the irrelevant background was removed by using the trained neural network model and the image only containing the apple color pixels was acquired.Then apple edge detection was carried out after morphological operations on the obtained image.Finally,the image was processed by using circle Hough transform algorithm,and apples were located with the help of calculating the center coordinates of each apple edge circle.The validation experimental results showed that the correlation coefficient of R2 between the proposed approaches based counting and manually counting reached 0.985.It illustrated that the proposed algorithm could be used to detect and count apples from apple trees’images taken in field environment with a high precision and strong anti-jamming feature.

Low transverse momentum track reconstruction based on the Hough transform for the BESIII drift chamber

Background The multilayer drift chamber(MDC)is one of the key detectors at the BESIII,which is mainly responsible for the precise measurement of charged particles.High reconstruction efficiency of charged particles with a wide range of momentums is a basic requirement for the MDC reconstruction software.Purpose The purpose is to improve the tracking efficiency at the low transverse momentum region.Methods The Hough transform is a mathematical method that can map hits in a detector to parameter space to find tracks globally.In this paper,we have used it as the basis for developing a new tracking algorithm to find tracks in MDC.In addition,the drift distances of hits are considered when implementing Hough transform.Results This tracking algorithm is implemented in the BESIII offline software system,and its performance is checked using both Monte Carlo and data.The results show that this tracking method improves reconstruction efficiency at the low transverse momentum region.Conclusions The tracking algorithm based on Hough transform is proved to be beneficial for the MDC low transverse momentum tracking,and has been used in BESIII offline data reconstruction .

The analysis of lane detection algorithms using histogram shapes and Hough transform

Purpose–The purpose of this paper is to develop a lane detection analysis algorithm by Hough transform and histogram shapes,which can effectively detect the lane markers in various lane road conditions,in driving system for drivers.Design/methodology/approach–Step 1:receiving image:the developed system is able to acquire images from video files.Step 2:splitting image:the system analyzes the splitting process of video file.Step 3:cropping image:specifying the area of interest using crop tool.Step 4:image enhancement:the system conducts the frame to convert RGB color image into grayscale image.Step 5:converting grayscale image to binary image.Step 6:segmenting and removing objects:using the opening morphological operations.Step 7:defining the analyzed area within the image using the Hough transform.Step 8:computing Houghline transform:the system operates the defined segment to analyze the Houghline transform.Findings–This paper presents the useful solution for lane detection by analyzing histogram shapes and Hough transform algorithms through digital image processing.The method has tested on video sequences filmed by using a webcam camera to record the road as a video file in a form of avi.The experimental results show the combination of two algorithms to compare the similarities and differences between histogram and Hough transform algorithm for better lane detection results.The performance of the Hough transform is better than the histogram shapes.Originality/value–This paper proposed two algorithms by comparing the similarities and differences between histogram shapes and Hough transform algorithm.The concept of this paper is to analyze between algorithms,provide a process of lane detection and search for the algorithm that has the better lane detection results.