• 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.

• Journal of IKEEE
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• v.19 no.4
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• pp.460-464
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• 2015

This paper proposes a panoramic image stitching that operates in real time at the embedded environment by applying ROI and PROSAC algorithm. The conventional panoramic image stitching applies SURF or SIFT algorithm which contains complicated operations and a lots of data, at the overall image to detect feature points. Also it applies RANSAC algorithm to remove outliers, so that an additional verification time is required due to its randomness. In this paper, unnecessary data are eliminated by setting ROI based on the characteristics of panorama images, and PROSAC algorithm is applied for removing outliers to reduce verification time. The proposed method was implemented on the ORDROID-XU board with ARM Cortex-A15. The result shows an improvement of about 54% in the processing time compared to the conventional method.

• 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 Electronics and Information Engineers
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• v.53 no.6
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• pp.122-129
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• 2016

There have been a lot of researches on object recognition in computer vision. The SURF(Speeded Up Robust Features) algorithm based on feature detection is faster and more accurate than others. However, this algorithm has a shortcoming of making an error due to feature point mismatching when extracting feature points. In order to increase a success rate of object recognition, we have created an object recognition system based on SURF and RANSAC(Random Sample Consensus) algorithm and proposed the pattern recognition filtering. We have also presented experiment results relating to enhanced the success rate of object recognition.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.555-560
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• 2008

두 장의 2차원 영상을 가지고 3차원을 재구성하기 위해서는 스테레오 정합을 이용한다. 이러한 이유로 그 동안에 많은 스테레오 정합에 대한 연구가 진행되었다. 스테레오 정합은 컴퓨터 기술의 발전과 더불어 좀 더 빠르고 높은 정확성을 보이고 있다. 하지만 속도와 정확성을 동시에 만족시키면서 대형영상에서도 동작할 수 있게 메모리을 적게 사용하는 방법은 많지가 않다. 본 논문에서는 이런 요구 조건을 만족시키기 위하여 새로운 스테레오 정합방법을 제시한다. 우리가 제시하는 새로운 방법은 다중 방향성 Greedy 알고리즘과 RANSAC을 반복적으로 사용하여 영상전체에 대한 스테레오 정합을 시도하는 방법이다. 우선 Greedy 알고리즘을 이용하여 여러 방향의 scan-line을 따라 깊이값 영상을 구한다. 그리고 이 여러 장의 깊이값 영상들의 분포를 RANSAC을 이용하여 신뢰영역을 찾아낸다. 구해진 신뢰영역을 바탕으로 Greedy 알고리즘과 RANSAC을 수 차례 반복하여 신뢰영역을 확장해 나가면 최종 깊이값 영상을 얻는다. 우리가 제안하는 알고리즘은 적은 메모리로도 큰 영상의 정합이 가능하고, 속도와 정확도 측면에서도 우수한 결과를 보인다.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.2
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• pp.61-66
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• 2019

Augmented reality is a technology that makes a virtual object appear as if it exists in reality by composing a virtual object in real time with the image captured by the camera. In order to augment the virtual object on the object existing in reality, the homography of images utilized to estimate the position and orientation of the object. The homography can be estimated by applying the RANSAC algorithm to the feature points of the images. But the homography estimation method using the RANSAC algorithm has a problem that accurate homography can not be estimated when there are many feature points in the background. In this paper, we propose a method to filter feature points of a background when the object is near and the background is relatively far away. First, we classified the depth image into relatively near region and a distant region using the Otsu's method and improve homography estimation performance by filtering feature points on the relatively distant area. As a result of experiment, processing time is shortened 71.7% compared to a conventional homography estimation method, and the number of iterations of the RANSAC algorithm was reduced 69.4%, and Inlier rate was increased 16.9%.

• Journal of IKEEE
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• v.16 no.3
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• pp.182-188
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• 2012

Here we propose an image quality assessment (IQA) based on histogram of oriented gradients (HOG). This method makes use of the characteristic that the histogram of gradient image describes the state of input image. In the proposed method, the image quality is derived by the slope of the HOG obtained from the target image. The line representing the HOG is measured by a random sample consensus (RANSAC) on the HOG. Simulation results based on the LIVE image quality assessment database suggest that the proposed method aligns better with how the human visual system perceives image quality than several state-of-the-art IQAs.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.671-678
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• 2006

In this paper, we propose a method for robustly estimating camera motion using fuzzy classification from the correspondences between two images. We use a RANSAC(Random Sample Consensus) algorithm to obtain accurate camera motion estimates in the presence of outliers. The drawback of RANSAC is that its performance depends on a prior knowledge of the outlier ratio. To resolve this problem the proposed method classifies samples into three classes(good sample set, bad sample set and vague sample set) using fuzzy classification. It then improves classification accuracy omitting outliers by iteratively sampling in only good sample set. The experimental results show that the proposed approach is very effective for computing a homography.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.158-164
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• 2015

Lane detection is one of the key parts among autonomous vehicle technologies because lane keeping and path planning are based on lane detection. Camera is used for lane detection but there are severe limitations such as narrow field of view and effect of illumination. On the other hands, Lidar sensor has the merits of having large field of view and being little influenced by illumination because it uses intensity information. Existing researches that use methods such as Hough transform, histogram hardly handle multiple lanes in the co-occuring situation of lanes and road marking. In this paper, we propose a method based on RANSAC and regularization which provides a stable and precise detection result in the co-occuring situation of lanes and road marking in highway scenarios. This is performed by precise lane point extraction using circular model RANSAC and regularization aided least square fitting. Through quantitative evaluation, we verify that the proposed algorithm is capable of multi lane detection with high accuracy in real-time on our own acquired road data.