• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.829-837
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• 2014

This paper develops an automatic go recording system based on image processing. We use Random Sample Consensus to detect the circular shape of stone and propose a set of methods to improve the computational overhead of RANSAC. The proposed scheme is not affected by the changes of stone location, illumination, and camera distance, which is different from existing methods. We implemented the proposed scheme into a working system and confirmed that the recording is feasible and the problems have been improved.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.9-14
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• 2015

Outlier detection and removal is a crucial step needed for various image processing applications such as image registration. Random Sample Consensus (RANSAC) is known to be the best algorithm so far for the outlier detection and removal. However RANSAC requires a cosiderable computation time. To drastically reduce the computation time while preserving the comparable quality, a outlier detection and removal method based on modified K-means is proposed. The original K-means was conducted first for matching point pairs and then cluster merging and member exclusion step are performed in the modification step. We applied the methods to various images with highly repetitive patterns under several geometric distortions and obtained successful results. We compared the proposed method with RANSAC and showed that the proposed method runs 3~10 times faster than RANSAC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.388-393
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• 2019

As a fiiting method to data with outliers, RANSAC(RANdom SAmple Consensus) based algorithm is widely used in fitting of line, circle, ellipse, etc. CUDA is currently most widely used GPU with massive parallel processing capability. This paper proposes an efficient CUDA implementation of multiple planes fitting using RANSAC with 3d points data, of which one set of 3d points is used for one plane fitting. The performance of the proposed algorithm is demonstrated compared with CPU implementation using both artificially generated data and real 3d heights data of a PCB. The speed-up of the algorithm over CPU seems to be higher in data with lower inlier ratio, more planes to fit, and more points per plane fitting. This method can be easily applied to a wide variety of other fitting applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.637-639
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• 2021

As autonomous driving research is actively progressing, lane detection is an essential technology in ADAS (Advanced Driver Assistance System) to locate a vehicle and maintain a route. Lane detection is detected using an image processing algorithm such as Hough transform and RANSAC (Random Sample Consensus). This paper implements a linear shape detection algorithm using OpenCV on Raspberry Pi 3 B+. Thresholds were set through OpenCV Gaussian blur structure and Canny edge detection, and lane recognition was successful through linear detection algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.195-204
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• 2018

Lane keeping assist and departure prevention system are the key functions of ADAS. In this paper, we propose lane detection method which uses Gaussian function based RANSAC. The proposed method consists mainly of IPM (inverse perspective mapping), Canny edge detector, and Gaussian function based RANSAC (Random Sample Consensus). The RANSAC uses Gaussian function to extract the parameters of straight or curved lane. The proposed RANSAC is different from the conventional one, in the following two aspects. One is the selection of sample with different probability depending on the distance between sample and camera. Another is the inlier sample score that assigns higher weights to samples near to camera. Through simulations, we show that the proposed method can achieve good performance in various of environments.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• Korean Journal of Remote Sensing
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• v.21 no.6
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• pp.505-516
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• 2005

The Communications, Oceanography and Meteorology Satellite(COMS) will be launched in 2008. Ground processing for COMS includes the process of automatic image navigation. Image navigation requires landmark detection by matching COMS images against landmark chips. For automatic image navigation, a matching must be performed automatically However, if matching results contain errors, the accuracy of Image navigation deteriorates. To overcome this problem, we propose use of a robust estimation technique called Random Sample Consensus (RANSAC) to automatically detect erroneous matching. We tested GOES-9 satellite images with 30 landmark chips that were extracted from the world shoreline database. After matching, mismatch results were detected automatically by RANSAC. All mismatches were detected correctly by RANSAC with a threshold value of 2.5 pixels.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.405-406
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• 2006

This paper proposes the new algorithm for the extraction of the corresponding points. Our algorithm is based on RANSAC(Random Sample Consensus) with EM(Expectation-Maximization). In the procedure of RANSAC, N-points are selected by the result of EM instead of the random selection. EM+SAC algorithm is applied to the correspondence for the mosaicing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1648-1672
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• 2020

There are several types of image registration in the sense of stitching separated images that overlap each other. One of these is feature-based registration by a common feature descriptor. In this study, we generate a mosaic of images using feature-based registration for drone aerial images. As a feature descriptor, we apply the scale-invariant feature transform descriptor. In order to investigate the authenticity of the feature points and to have the mapping function, we employ the sample consensus method; we consider the sensed image's inherent characteristic such as the geometric congruence between the feature points of the images to propose a novel hypothesis estimation of the mapping function of the stitching via some optimally chosen initial candidate inliers in the sample consensus method. Based on the experimental results, we show the efficiency of the proposed method compared with benchmark methodologies of random sampling consensus method (RANSAC); the well-ordering property defined in the context and the extensive stitching examples have supported the utility. Moreover, the sample consensus scheme proposed in this study is uncomplicated and robust, and some fatal miss stitching by RANSAC is remarkably reduced in the measure of the pixel difference.

• Journal of KIISE
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• v.43 no.5
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• pp.562-568
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• 2016

Point cloud data can be expressed in a specific coordinate system of a data set with a large number of points, to represent any form that generally has different characteristics in the three-dimensional coordinate space. This paper is aimed at finding a cylindrical pipe in the point cloud of the three-dimensional coordinate system using RANSAC, which is faster than the conventional Hough Transform method. In this study, the proposed cylindrical pipe is estimated by combining the results of parameters based on two mathematical models. The two kinds of mathematical models include a sphere and line, searching the sphere center point and radius in the cylinder, and detecting the cylinder with straightening of center. This method can match cylindrical pipe with relative accuracy; furthermore, the process is rapid except for normal estimation and segmentation. Quick cylinders matching could benefit from laser scanning and reverse engineering construction sectors that require pipe real-time estimates.