• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2343-2352
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• 2015

In this paper, we propose novel motion field estimation method using U-disparity map and forward-backward error removal in vehicles environment. Generally, in an image obtained from a camera attached in a vehicle, a motion vector occurs according to the movement of the vehicle. but this motion vector is less accurate by effect of surrounding environment. In particular, it is difficult to extract an accurate motion vector because of adjacent pixels which are similar each other on the road surface. Therefore, proposed method removes road surface by using U-disparity map and performs optical flow about remaining portion. forward-backward error removal method is used to improve the accuracy of the motion vector. Finally, we predict motion of the vehicle by applying RANSAC(RANdom SAmple Consensus) from acquired motion vector and then generate motion field. Through experimental results, we show that the proposed algorithm performs better than old schemes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.893-898
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• 2013

It is always a dream to recreate the experience of a particular place, the Panorama Virtual Reality has been interpreted as a kind of technology to create virtual environments and the ability to maneuver angle for and select the path of view in a dynamic scene. In this paper we examined an efficient method for Image registration and stitching of captured imaged. Two approaches are studied in this paper. First, dynamic programming is used to spot the ideal key points, match these points to merge adjacent images together, later image blending is used for smooth color transitions. In second approach, FAST and SURF detection are used to find distinct features in the images and nearest neighbor algorithm is used to match corresponding features, estimate homography with matched key points using RANSAC. The paper also covers the automatically choosing (recognizing, comparing) images to stitching method.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.481-487
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• 2023

Recently, interest in UAM (Urban Air Mobility) has surged as a critical solution to urban traffic congestion and air pollution issues. However, efficient UAM operation requires accurate 3D Point Cloud data processing, particularly in separating the ground and objects. This paper proposes and validates a method for effectively separating ground and objects in a UAM environment, taking into account its dynamic and complex characteristics. Our approach combines attitude information from MEMS sensors with ground plane estimation using RANSAC, allowing for ground/object separation that isless affected by GPS errors. Simulation results demonstrate that this method effectively operates in UAM settings, marking a significant step toward enhancing safety and efficiency in urban air mobility. Future research will focus on improving the accuracy of this algorithm, evaluating its performance in various UAM scenarios, and proceeding with actual drone tests.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.513-516
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• 2011

기존의 실시간 파노라마 합성 알고리즘에서는 매칭점과 입력 영상에서의 outlier를 구분하고 제거하기가 어렵기 때문에 노이즈가 많은 영상 또는 반복적인 패턴이 많은 영상에서 왜곡이 쉽게 발생하는 문제가 있다. 따라서 본 논문에서는 기존의 실시간 파노라마 합성 프레임웍에서 실시간 합성 조건을 만족시키면서 효과적으로 매칭점과 입력 영상에서의 outlier를 제거하는 방법을 제안한다. 이를 위해서 선형 모델에서 outlier을 제거하는 데 주로 사용되는 RANSAC 알고리즘을 실시간 파노라마 합성에서 사용되는 비선형 모델에 적용 가능하도록 수정하고 속도 향상을 위해서 사용되는 모델의 파라미터를 줄이는 방법을 제안한다. 이를 통하여 매칭점 중에 존재하는 outiler를 제거하고 전체 매칭점 중에서 inlier 비율을 이용하여 입력되는 영상시퀀스에서 outlier 영상을 제거하는 방법을 제안한다. 실험 결과 기존의 방법에 비해서 합성 결과의 왜곡이 줄어드는 것을 확인하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.65-68
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• 2015

본 논문에서는 자연스러운 파노라마 영상 생성을 위해 FAST(features from accelerated segment test)를 이용한 특징점 기반의 파노라마 영상 생성 기법을 제안한다. 다수의 영상을 이용해 자연스러운 파노라마 영상을 만들기 위해 실린더 투영을 수행 한 후 추출된 특징점들을 RANSAC(random sample consensus)을 이용해 정합 시 오차율을 최소화한다. 서로 다른 방향에서 얻은 다수의 영상을 합성할 때 정합 경계 주변의 이질감을 보완하기 위해 블렌딩 기법을 사용함으로써 자연스러운 파노라마 영상을 생성한다. 다수의 영상으로 실험을 한 결과 왜곡이 보정되고 자연스러운 파노라마 영상을 생성할 수 있었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.177-180
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• 2015

본 논문에서는 비디오 영상을 입력 했을 때 특징점 추적을 통한 다수 영상의 고속 스트칭 기법을 제안한다. 빠른 속도로 특징점 추출을 위해서 FAST(Features from Accelerated Segment Test) 기법을 사용한다. 특징점 정합과정은 기존의 방법과는 다른 새로운 방법을 제안한다. Mean shift 를 통해 특징점이 포함된 영역을 추적하여 벡터(vector)를 구한다. 이 벡터를 사용하여 추출한 특징점들을 정합하는데 사용한다. 마지막으로 이상점(outlier)을 제거하기 위해 RANSAC(RANdom Sample Consensus) 기법을 사용한다. 입력된 두 영상의 호모그래피(homography) 변환 행렬을 구하여 하나의 파노라마 영상을 생성한다. 실험을 통해 제안하는 기법이 기존의 기법보다 속도가 향상되는 것을 확인하였다.

• Journal of KIBIM
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• v.14 no.2
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• pp.1-12
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• 2024

The paper proposes a methodology that combines the Random Sample Consensus (RANSAC) algorithm and the Point Cloud Encoder-Decoder Network (PCEDNet) algorithm to automatically extract the length of infrastructure elements from point cloud data acquired through 3D LiDAR scans of retaining walls. This methodology is expected to significantly improve time and cost efficiency compared to traditional manual measurement techniques, which are crucial for the data-driven analysis required in the precision-demanding construction sector. Additionally, the extracted positional and dimensional data can contribute to enhanced accuracy and reliability in Scan-to-BIM processes. The results of this study are anticipated to provide important insights that could accelerate the digital transformation of the construction industry. This paper provides empirical data on how the integration of digital technologies can enhance efficiency and accuracy in the construction industry, and offers directions for future research and application.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1247-1255
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• 2016

The safety and autonomous flight function of micro UAV or drones is crucial to its commercial application. The requirement of own building stable drones is still a non-trivial obstacle for researchers that want to focus on the intelligence function, such vision and navigation algorithm. The paper present a GCS using commercial drone and hardware platforms, and open source software. The system follows modular architecture and now composed of the communication, UI, image processing. Especially, lane-keeping algorithm. are designed and verified through testing at a sports stadium. The designed lane-keeping algorithm estimates drone position and heading in the lane using Hough transform for line detection, RANSAC-vanishing point algorithm for selecting the desired lines, and tracking algorithm for stability of lines. The flight of drone is controlled by 'forward', 'stop', 'clock-rotate', and 'counter-clock rotate' commands. The present implemented system can fly straight and mild curve lane at 2-3 m/s.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.70-77
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• 2014

This paper proposes a novel localization algorithm based on ego-motion which used Lucas-Kanade Optical Flow and warping image obtained through fish-eye lenses mounted on the robots. The omnidirectional image sensor is a desirable sensor for real-time view-based recognition of a robot because the all information around the robot can be obtained simultaneously. The preprocessing (distortion correction, image merge, etc.) of the omnidirectional image which obtained by camera using reflect in mirror or by connection of multiple camera images is essential because it is difficult to obtain information from the original image. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we extract motion vectors using Lucas-Kanade Optical Flow in preprocessed image. Third, we estimate the robot position and angle using ego-motion method which used direction of vector and vanishing point obtained by RANSAC. We confirmed the reliability of localization algorithm using ego-motion based on fisheye warping image through comparison between results (position and angle) of the experiment obtained using the proposed algorithm and results of the experiment measured from Global Vision Localization System.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.161-170
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• 2012

This paper presents an efficient and robust approach for determining the correspondence between unbalanced stereo images. The disparity vectors were used instead of feature points, such as corners, to calculate a correspondence relationship. For a faster and optimal estimation, the vectors were classified into several regions, and the homography of each region was calculated using the RANSAC algorithm. The correspondence image was calculated from the images transformed by each homography. Although it provided good results under normal conditions, it was difficult to obtain reliable results in an unbalanced stereo pair. Therefore, a balancing method is also proposed to minimize the unbalance effects using the histogram specification and structural similarity index. The experimental results showed that the proposed approach outperformed the baseline algorithms with respect to the speed and peak-signal-to-noise ratio. This work can be applied to practical fields including 3D depth map acquisition, fast stereo coding, 2D-to-3D conversion, etc.