• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.321-329
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• 2015

This paper addresses a new algorithm for a stereo visual odometry to measure the ego-motion of a vehicle. The new algorithm introduces an inlier grouping method based on Delaunay triangulation and vanishing point computation. Most visual odometry algorithms rely on RANSAC in choosing inliers. Those algorithms fluctuate largely in processing time between images and have different accuracy depending on the iteration number and the level of outliers. On the other hand, the new approach reduces the fluctuation in the processing time while providing accuracy corresponding to the RANSAC-based approaches.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.180-187
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• 2007

In this paper, we propose an automatic method which combines two or more images acquired by camera on the air-vehicle into a larger image mosaics. The shift, scaling, rotation factors between two images can be calculated by using the correspondences between the points of the images. In order to estimate these factors, we find the relative positions of two images with respect to each other by using the SIFT descriptor and the RANSAC algorithm. After estimating the factors, the images can be merged into a single image mosaic by warping the target image. To avoid seams when mosaics are constructed from overlapped images, we apply the average gray level value of points within a overlapped zone. We have tested our proposed method on various image sets and have confirmed that our method produced good result subjectively.

• 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.

• 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.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.10-22
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• 2010

This study proposes a new method of multiple homographies estimation between two images. With a large proportion of outliers, RANSAC is a general and very successful robust parameter estimator. However it is limited by the assumption that a single model acounts for all of the data inliers. Therefore, it has been suggested to sequentially apply RANSAC to estimate multiple 2D projective transformations. In this case, because outliers stay in the correspondence data set through the estimation process sequentially, it tends to progress slowly for all models. And, it is difficult to parallelize the sequential process due to the estimation order by the number of inliers for each model. We introduce a guided sequential RANSAC algorithm, using the local model instances that have been obtained from RANSAC procedure, which is able to reduce the number of random samples and deal simultaneously with multiple models.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1273-1283
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• 2012

In this paper, vision-based positioning algorithm for melon harvesting robot is presented. RGB value of the input image was converted into HSI value then, melon area was extracted after performing the binarization using HUE value. After morphological filtering was applied to remove noise, outermost boundary points were obtained using border following and convex hull method. Elliptical fitting for melons was perform by the RANSAC algorithm, the center point of ellipse, the length of the short and long axis, and rotation angle were obtained. We verified the effectiveness of the proposed method by various simulation experiments and confirmed actual feasibility of the proposed method by applying to the real melon.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.345-353
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• 2010

A general solution in global self-position location of robot is to generate multiple hypothesis in self-position of robot, which is to look for the most positive self-position by evaluating each hypothesis based on features of observed landmark. Markov Localization(ML) or Monte Carlo Localization(MCL) to be the existing typical method is to evaluate all pairs of landmark features and generated hypotheses, it can be said to be an optimal method in sufficiently calculating resources. But calculating quantities was proportional to the number of pairs to evaluate in general, so calculating quantities was piled up in wide environments in the presence of multiple pairs if using these methods. First of all, the positive and promising pairs is located and evaluated to solve this problem in this paper, and the newly locating method to make effective use of calculating time is proposed. As the basic method, it is used both RANSAC(RANdom SAmple Consensus) algorithm and preemption scheme to be efficiency method of RANSAC algorithm. The calculating quantity on each observation of robot can be suppressed below a certain values in the proposed method, and the high location performance can be determined by an experimental on verification.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.27-32
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• 2013

It plays an important role to detect the shape of an ellipse in many application areas of image processing. But it is very difficult to detect the ellipse in the real image because the noise was involved in the image, other objects obscured the ellipse or the ellipses were overlap with each other. In this paper, we extract the boundary (edge) to detect ellipse in the image and perform the grouping process in order to reduce amount of information. As a result, the speed of the ellipse detection was improved. Also in order to the ellipse detection, we selected the five ellipse parameters at random And then to select the optimal parameters of the ellipse, the linear least-squares approximation is applied. To verify the ellipse detection, RANSAC algorithm is applied. After the algorithm proposed in this study was implemented, the results applied to the real images showed an aocuracy of 75% and speed was very fast to compared with other researches. It mean that the proposed algorithm was valuable to detect the ellipses in the image.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.318-320
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• 2012

증강현실 콘텐츠를 2D 이미지기반으로 저작할 때, 작성된 증강현실 콘텐츠를 카메라 시점과 일치시켜 합성하기 위해 호모그래피를 이용한다. 이때 증강현실 콘텐츠를 이질감 없이 합성하기위해 정확한 호모그래피 행렬을 추정해야 한다. 그러나 호모그래피 행렬 추정 시 사용되는 특징점들이 선형을 이루거나, 특정 영역에 군집을 이루는 경우 정확한 호모그래피 행렬을 추정하지 못하는 문제가 발생한다. 본 논문에서는 이러한 문제를 해결하기 위해 선형제약, 거리제약을 적용한 CSP 기반의 RANSAC 알고리즘을 제안한다. 실험결과 호모그래피 행렬 추정 시 CSP를 적용한 RANSAC 알고리즘이 기존의 랜덤샘플링 또는 삼각형의 넓이를 이용한 샘플링을 적용한 RANSAC 알고리즘보다 정확도가 향상됨을 보였다.

• 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.