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random sample consensus (RANSAC) algorithm for enhancing overlapped etched track counting

IET IMAGE PROCESSING 2015年第2期9卷 97-106页

作者： Ghergherehchi, Mitra Kim, Seung Yeol Afarideh, Hossein Kim, Yoon Sang Sungkyunkwan Univ Coll Elect & Elect Engn Suwon South Korea Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan South Korea Amirkabir Univ Technol Energy Engn & Phys Dept Tehran Iran

In this study, a method to enhance the accuracy of overlapped etched track analysis is proposed. Counting tracks by eye is not an easy task and automated tracks counting systems are attractive key for this problem. This method supplements the deficiencies of the conventional track analysis method. A computer programme named KoreaTech Track Measurement System written in C++, which is the authors' previous method, has been upgraded. In the proposed track analysis method, the track images captured from solid state nuclear track detectors are geometrically analysed and the number of tracks is counted. A damaged etching track shape can be restored on the track image to improve the analysis accuracy. For track restoration, the effective points are differentiated from the damaged track image. The track image is then restored by estimating the radii (small object removal) or their axis (ellipse, circle and non-circle) using the random sample consensus method. Using the restored track image, the track parameters are obtained from the ellipse and then approximated to the contour of the track image to analyse the track image. Then, the total number of tracks including the overlapped tracks is counted. To verify the proposed track analysis method, experiments using actual etching track images are conducted and the results are discussed.

关键词： random processes image enhancement image restoration solid-state nuclear track detectors nuclear engineering computing shape recognition image capture C plus plus language random sample consensus algorithm overlapped etched track counting enhancement automated track counting system KTTMS C plus captured image tracking solid state nuclear track detector SSNTD damaged etching track shape restoration track image restoration radii estimation axis estimation track analysis method RANSAC algorithm

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Wind power data cleaning using RANSAC-based polynomial and linear regression with adaptive threshold

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SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Yang, Haineng Tang, Jie Shao, Wu Yin, Jintian Liu, Baiyang Shaoyang Univ Sch Elect Engn Shaoyang 422000 Peoples R China Shaoyang Polytech Sch Automot & Intelligent Mfg Shaoyang 422000 Peoples R China

As the global demand for clean energy continues to rise, wind power has become one of the most important renewable energy sources. However, wind power data often contains a high proportion of dense anomalies, which not only significantly affect the accuracy of wind power forecasting models but may also mislead grid scheduling decisions, thereby jeopardizing grid security. To address this issue, this paper proposes an adaptive threshold robust regression model (RPR model) based on the combination of the random sample consensus (RANSAC) algorithm and polynomial linear regression for wind power data cleaning. The model successfully captures the nonlinear relationship between wind speed and power by extending the polynomial features of wind speed and power, enabling the linear regression model to handle the nonlinearity. By combining the RANSAC algorithm and polynomial linear regression, a robust polynomial regression model is constructed to tackle anomalous data and enhance the accuracy of data cleaning. During the cleaning process, the model first fits the raw data by randomly selecting a minimal sample set, then dynamically adjusts the decision thresholds based on the median of residuals and median absolute deviation (MAD), ensuring effective identification and cleaning of anomalous data. The model's robustness allows it to maintain efficient cleaning performance even with a high proportion of anomalous data, addressing the limitations of existing methods when handling densely distributed anomalies. The effectiveness and innovation of the proposed method were validated by applying it to real data from a wind farm operated by Longyuan Power. Compared to other commonly used cleaning methods, such as the Bidirectional Change Point Grouping Quartile Statistical Model, Principal Contour Image Processing Model, DBSCAN Clustering Model, and Support Vector Machine (SVM) Model, experimental results showed that the proposed method delivered the best performance in improving da

关键词： Wind power Data cleaning random sample consensus algorithm Anomalous data Polynomial regression Adaptive threshold robust regression

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Fast and robust homography estimation method with algebraic outlier rejection

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IET IMAGE PROCESSING 2018年第4期12卷 552-562页

作者： Qi, Naixin Zhang, Shengxiu Cao, Lijia Yang, Xiaogang Li, Chuanxiang He, Chuan Xian Res Inst High Technol Xian 710025 Shaanxi Peoples R China Sichuan Univ Sci & Engn Coll Automat & Informat Engn Zigong 64300 Peoples R China Artificial Intelligence Key Lab Sichuan Prov Zigong 643000 Peoples R China

To solve the homography estimation problem containing outliers and noise, a fast, robust, and accurate method is proposed. In this method, the outliers are rejected based on the differing characteristics of algebraic errors between outliers and inliers, and the homography is estimated by minimising the residual vector. The advantage of this method is in integrating the outlier rejection into the estimation pipeline. The computational complexity of the proposed method is not increased, and the random sample consensus algorithm is not needed to extract the inliers, as was previously necessary. Since the outlier rejection process is based on an algebraic criterion without computing the re-projection error at each step, the speed of the proposed method is improved. Several simulations based on synthetic and real images illustrate the performance of the proposed method in terms of subjective visual quality, objective quality measurement, and computational time. The experimental results demonstrate that the proposed method achieves accurate, efficient and robust homography estimation under different image transformation degrees and different outlier ratios.

关键词： vectors minimisation estimation theory computational complexity random processes image processing homography estimation method algebraic outlier rejection algebraic error characteristics residual vector minimisation estimation pipeline computational complexity random sample consensus algorithm reprojection error objective quality measurement subjective visual quality image transformation degree

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Fast and robust approaches for lane detection using multi-camera fusion in complex scenes

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第12期14卷 1582-1593页

作者： Xiong, Hui Yu, Dameng Liu, Jinxin Huang, Heye Xu, Qing Wang, Jianqiang Li, Keqiang Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China

Due to the limited sensing ability with the single-view camera and the real-time requirement for multi-view scenarios or deep learning-based methods in complex scenes, the output of lane detection is not applicable for the actual lane departure warning system. To tackle this challenge, the authors propose a fast and robust approach for lane detection based on well-designed multi-camera fusion, integrating vanishing point estimation, and specified feature fitting strategies. To meet real-time demand, several simple but effective image processing means are introduced and improved. Concretely, on account of statistical information, the authors' method carries out an improved region of interest selection to speed up the detection. Afterwards, they used the B-spline fitting lane line on the strength of the random sample consensus algorithm for the front view image detection and improved the Hough algorithm for the two rear-view images correspondingly. Using coordinate conversion and self-designed fusion strategy, they get the robust lane information based on symmetrical lane detection from the left/right sides of both front and side views. Experimental results in newly introduced multi-camera scenarios show that their multi-camera fusion framework contributes to significant improvement in accuracy and robustness in comparison with traditional methods.

关键词： feature extraction image sensors edge detection splines (mathematics) learning (artificial intelligence) Hough transforms cameras sensor fusion B-spline fitting lane line rear-view images coordinate conversion self-designed fusion strategy robust lane information symmetrical lane detection multicamera fusion framework complex scenes single-view camera real-time requirement multiview scenarios deep learning-based methods actual lane departure warning system image processing feature fitting strategies statistical information random sample consensus algorithm front view image detection Hough algorithm

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An Accurate Point Set Matching Method for Hull Blocks With Constraints

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MARINE TECHNOLOGY SOCIETY JOURNAL 2023年第1期57卷 23-34页

作者： Guan, Guan Liao, Hongling Yang, Qu Sun, Weidi Sun, Yuzhou Dalian Univ Technol Sch Naval Architecture Dalian Peoples R China Amer Bur Shipping China Ltd Shanghai Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian Peoples R China Shanghai Dredging Equipment Ind Co Ltd Shanghai Peoples R China Dalian Univ Technol Sch Naval Architecture Dalian 116023 Peoples R China

To effectively improve the precision matching technique of measurement point set and design point set for hull blocks, an accurate point set matching method for hull blocks with engineering constraints (verticality, levelness, symmetry, flat-ness, etc.) and elimination error points is presented. It is divided into initial matching and refine matching. In initial matching, the improved random sample consensus algorithm is proposed to eliminate error points rapidly and accurately;the improved Coherent Point Drift algorithm is proposed to obtain more accurate initial matching. In refine matching, the Analytic Hierarchy Process is used to ob-tain each weight of engineering constraints automatically, and the weight vector is introduced into the multi-optimization objective function to achieve the more rea-sonable matching results. The results proved that this method can rapidly and automatically eliminate error points, and get more accurate and reasonable re-sults meeting engineering constraints. It can provide the basis for the subse-quent assembly of hull blocks.

关键词： hull blocks point set matching random sample consensus algorithm coherent point drift algorithm engineering constraint

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Automated traffic sign and light pole detection in mobile LiDAR scanning data

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IET INTELLIGENT TRANSPORT SYSTEMS 2019年第5期13卷 803-815页

作者： Javanmardi, Mohammadreza Song, Ziqi Qi, Xiaojun Utah State Univ Dept Comp Sci Logan UT 84322 USA Utah State Univ Dept Civil & Environm Engn Logan UT 84322 USA

Detection of traffic signs and light poles using light detection and ranging (LiDAR) data has demonstrated a valid contribution to road safety improvements. In this study, the authors propose a fast and reliable method, which can identify various traffic signs and light poles in mobile LiDAR data. Specifically, they first use the surface reconstruction algorithm to extract the normal vectors of the points as one of the characteristic features and apply k-means on the characteristic features of the points to automatically segment the data into road or non-road points. They then employ sliding cuboids to search for high-elevated objects that are located near the borders and on top of the road points. They further employ the random sample consensus algorithm to remove outliers and keep the points that fall on the perpendicular planes to the road trajectory. Finally, they introduce a modified seeded region growing algorithm to remove noisy points and incorporate the shape information to reject the false objects. A set of extensive experiments have been carried out on the datasets that are captured by Utah Department of Transportation from I-15 highway. The results demonstrate the robustness of the proposed method in detecting almost all traffic signs and light poles.

关键词： feature extraction image segmentation optical radar object detection traffic engineering computing road safety image denoising automated traffic sign light pole detection mobile LiDAR scanning data surface reconstruction algorithm random sample consensus algorithm road trajectory noisy points road safety Utah Department of Transportation

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High confidence detection for moving target in aerial video

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IET IMAGE PROCESSING 2019年第14期13卷 2724-2734页

作者： Tian, Yumin Peng, Chenhui Wang, Di Wan, Bo Xidian Univ Sch Comp Sci & Technol 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China

The moving target detection and tracking in aerial video is a challenge task because of its moving background, smaller target sizes, lower resolution and limited onboard computing resources. In this study, a high confidence detection method based on background compensation and three-frame-difference method is designed, which can detect moving objects in a dynamic background accurately. First, the authors use local feature extraction and matching for image registration and demonstrate that speed-up robust feature key points are suitable for the stabilisation task. Then, they estimate the global camera motion parameters using affine transformation which are obtained by the random sample consensus algorithm. Finally, they detect moving object by three-frame-difference method. As the detection results of the frame-difference method generally exists 'empty' and noise, in order to select the two higher-quality differential images to perform the logic AND operation, they add image quality assessment to the three-frame-difference method to obtain more accurate moving objects. Moreover, the edge detection algorithm and morphological processing are integrated together to further boost the overall detecting performance. The extensive empirical evaluations on aerial videos demonstrate that the proposed detector is very promising for the various challenging scenarios.

关键词： image segmentation affine transforms image motion analysis video signal processing image registration edge detection cameras feature extraction object detection video surveillance limited onboard computing resources high confidence detection method background compensation three-frame-difference method dynamic background local feature extraction speed-up robust feature key points stabilisation task global camera motion parameters random sample consensus algorithm detection results higher-quality differential images image quality assessment accurate moving objects edge detection algorithm detecting performance aerial video moving target detection tracking challenge task moving background smaller target sizes

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Detecting and tracking a road-drivable area with three-dimensional point clouds and IoT for autonomous applications

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SERVICE ORIENTED COMPUTING AND APPLICATIONS 2024年第4期18卷 321-331页

作者： Enad, Mahmood H. Bashi, Omar I. Dallal Jameel, Shymaa Mohammed Alhasoon, Asaad A. Al Kubaisi, Yasir Mahmood Hameed, Husamuldeen K. Al Furat Al Awsat Tech Univ Tech Inst Karbala Dept Elect Tech Najaf Iraq Northern Tech Univ Med Tech Inst Mosul Iraq Iraqi Commiss Comp & Informat Informat Baghdad 10009 Iraq Minist Sci & Technol Res Baghdad Iraq Dubai Acad Hlth Corp Dept Sustainabil Management Dubai U Arab Emirates High Inst Telecommun & Post Dept Telecommun & Informat Baghdad Iraq

This work presents a Light Detection and Ranging (LiDAR)-based point cloud method for detecting and tracking road edges. Initially, this work explores the progress in detecting road curb issues. A dataset (called PandaSet) with a Pandar64 sensor to capture different city scenes is used. LiDAR point cloud, as part of an IoT ecosystem, detects the road curb and requires distinguishing the right and left road curbs with regard to the ego car. The curb point's features use random sample consensus (RANSAC)-based polynomial quadratic approximation to obtain the prospect curb points to eliminate false positive ones. Through extensive experiments, we demonstrate the effectiveness and reliability of our method under various traffic and environmental conditions. Our results showcase a maximum drift of 1.62 m for left curb points and 0.87 m for right curb points, highlighting the superior accuracy and stability of our approach. This LiDAR-based curb detection framework paves the way for enhanced lane recognition and path planning in autonomous driving applications.

关键词： LiDAR sensor Point cloud processing random sample consensus algorithm Polynomial quadratic approximation Feature extraction

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Comparison of algorithms for Recognition of Cylindrical Features in a Voxel-Based Approach for Tolerance Inspection

Comparison of Algorithms for Recognition of Cylindrical Feat...

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International Conference on Design Tools and Methods in Industrial Engineering (ADM)

作者： Bici, Michele Campana, Francesca Sapienza Univ Roma DIMA Via Eudossiana 18 I-00184 Rome Italy

ISBN: (纸本)9783030311544;9783030311537

In injection molding production, automatic inspections are needed to control defects and evaluate the assigned functional tolerances of components and dies. With the "Smart Manufacturing" approach as a point of view, this paper resumes part of a wider research aiming the integration and the automation of a Reverse Engineering inspection process in components and die set-up. The paper compares two fitting approaches for recognition of portions of cylindrical surfaces. Therefore, they are evaluated in the respect of an automatic voxel-based feature recognition of 3D dense cloud of points for tolerance inspection of injection-molded parts. The first approach is a 2D Levenberg Marquardt algorithm coupled with a first guess evaluation made by the Kasa algebraic form. The second one is a 3D fitting based on the random sample consensus algorithm (RANSAC). The evaluation has been made according to the ability of the approaches of working on points associated to the voxel structure that locally divides the cloud to characterize planar and curved surfaces. After the presentation of the overall automatic recognition, the cylindrical surface algorithms are presented and compared trough test cases.

关键词： Tolerance inspection Injection molding random sample consensus algorithm

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A New 3D Multi-modality Medical Bone Image Registration algorithm 17

A New 3D Multi-modality Medical Bone Image Registration Algo...

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International Conference on Video and Image Processing (ICVIP)

作者： Tao, Huanjie Lu, Xiaobo Southeast Univ Sch Automat 2 Sipailou Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781450353830

Three-dimensional (3D) multi-modality medical bone image registration is an important technology in surgical application, especially in large computer-aided orthopedic surgery. To improve registration accuracy, we propose a new 3D multi-modality medical bone image registration algorithm based on local features through analyzing the bone structure. In this method, the image Hessian matrix is introduced for local features extraction, and the local behavior of the 3D bone image is described by the eigenvalues of Hessian matrix. This method can automatically extract and select the most representative feature points (blob-like structure) in different scales. Then we adopt the idea of triangle matching to get stereo matching point pairs. Improve random sample consensus (RANSAC) algorithm is adopted to remove wrong matching point pairs. We use the right matching point pairs to establish rigid transformation model and solve this non-linear model by Levenberg-Marquardt algorithm to get geometric transformation parameters. Simulated experiments and real experiments demonstrate that the proposed method can achieve a high image registration accuracy.

关键词： Image registration multi-modality medical image triangle matching Hessian matrix random sample consensus algorithm

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