• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.602-608
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• 2015

This paper proposes an algorithm for precise detection of corner points on a coplanar checkerboard in order to perform stereo camera calibration using a single frame. Considering the conditions of automobile production lines where a stereo camera is attached to the windshield of a vehicle, this research focuses on a coplanar calibration methodology. To obtain the accurate values of the stereo camera parameters using the calibration methodology, precise localization of a large number of feature points on a calibration target image should be ensured. To realize this demand, the idea with respect to a checkerboard pattern design and the use of a Homography matrix are provided. The calibration result obtained by the proposed method is also verified by comparing the depth information from stereo matching and a laser scanner.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.401-403
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• 2021

본 시스템은 같은 환자로부터 촬영한 기존 안저영상과 초광각 안저영상을 Paired Dataset으로 지니고 있으며, 영상의 크기 및 해상도를 똑같이 맞추고, 황반부와 신경유두 및 혈관의 위치를 미세조정하는 과정을 자동화하는 것을 목표로 하고 있다. 이 과정은 황반부를 중심으로 하여 영상을 잘라내어 이미지의 크기를 맞추는 과정(Scaling)과, 황반부를 중심으로 잘라낸 한 쌍의 영상을 포개었을 때 황반부, 신경 유두, 혈관 등의 위치가 동일하도록 미세조정하는 과정(Warping)이 있다. Scaling Stage에선 기존 안저영상과 초광각 안저영상의 촬영범위가 현저하게 차이나기 때문에, 황반변성 부위를 잘 나타내도록 사전에 잘라낼 필요가 있으며, 이를 신경유두의 Object Detection을 활용할 예정이다. Warping Stage에선 동일한 위치에 같은 황반변성 정보가 내포되어야 하므로 규격조정 및 위치조정 과정이 필수적이며, 이후 안저영상 내의 특징들을 매칭하는 작업을 하기 위해 회전, 회절, 변환 작업 등이 이루어지며, 이는 Homography Estimation을 통하여 이미지 변환 matrix를 구하는 방법으로 진행된다. 자동조정된 안저영상 데이터는 추후에 GAN을 이용한 안저영상 생성모델을 위한 학습데이터로 이용할 예정이며, 현재로선 2500쌍의 데이터를 대상으로 실험을 진행중이지만, 최종적으로 3만 쌍의 안저영상 데이터를 목표로 하고 있다.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.5
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• pp.415-420
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• 2004

We present a new algorithm for the calibration of a camera and the recovery of 3D scene structure up to a scale from image sequences using known angles between lines in the scene. Traditional method for calibration using scene constraints requires various scene constraints due to the stratified approach. Proposed method requires only one type of scene constraint of known angle and also it directly recovers metric structure up to an unknown scale from projective structure. Specifically, we recover the matrix that is the homography between the projective structure and the Euclidean structure using angles. Since this matrix is a unique one in the given set of image sequences, we can easily deal with the problem of varying intrinsic parameters of the camera. Experimental results on the synthetic and real images demonstrate the feasibility of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.49 no.4
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• pp.10-16
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• 2012

For high speed visual inspection in semiconductor industries, it is essential to acquire two-dimensional images on regions of interests with a large field of view (FOV) and a high resolution simultaneously. In this paper, an imaging system is newly proposed to achieve high quality image in terms of precision and FOV, which is composed of single lens, a beam splitter, two camera sensors, and stereo image grabbing board. For simultaneously acquired object images from two camera sensors, Zhang's camera calibration method is applied to calibrate each camera first of all. Secondly, to find a mathematical mapping function between two images acquired from different view cameras, the matching matrix from multiview camera geometry is calculated based on their image homography. Through the image homography, two images are finally registered to secure a large inspection FOV. Here the inspection system of using multiple images from multiple cameras need very fast processing unit for real-time image matching. For this purpose, parallel processing hardware and software are utilized, such as Compute Unified Device Architecture (CUDA). As a result, we can obtain a matched image from two separated images in real-time. Finally, the acquired homography is evaluated in term of accuracy through a series of experiments, and the obtained results shows the effectiveness of the proposed system and method.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.11
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• pp.1088-1095
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• 2009

In this paper, we describe the reorientation method of distorted road sign by using projection transformation for improving recognition rate of road sign. RSR (Road Sign Recognition) is one of the most important topics for implementing driver assistance in intelligent transportation systems using pattern recognition and vision technology. The RS (Road Sign) includes direction of road or place name, and intersection for obtaining the road information. We acquire input images from mounted camera on vehicle. However, the road signs are often appeared with rotation, skew, and distortion by perspective camera. In order to obtain the correct road sign overcoming these problems, projection transformation is used to transform from 4 points of image coordinate to 4 points of world coordinate. The 4 vertices points are obtained using the trajectory as the distance from the mass center to the boundary of the object. Then, the candidate areas of road sign are transformed from distorted image by using homography transformation matrix. Internal information of reoriented road signs is segmented with arrow and the corresponding indicated place name. Arrow area is the largest labeled one. Also, the number of group of place names equals to that of arrow heads. Characters of the road sign are segmented by using vertical and horizontal histograms, and each character is recognized by using SAD (Sum of Absolute Difference). From the experiments, the proposed method has shown the higher recognition results than the image without reorientation.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.728-737
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• 2015

Stitching algorithm obtain a descriptor of the feature points extracted from multiple images, and create a single image through the matching process between the each of the feature points. In this paper, a feature extraction and matching techniques for the creation of a high-speed panorama using video input is proposed. Features from Accelerated Segment Test(FAST) is used for the feature extraction at high speed. A new feature point matching process, different from the conventional method is proposed. In the matching process, by tracking region containing the feature point through the Mean shift vector required for matching is obtained. Obtained vector is used to match the extracted feature points. In order to remove the outlier, the RANdom Sample Consensus(RANSAC) method is used. By obtaining a homography transformation matrix of the two input images, a single panoramic image is generated. Through experimental results, we show that the proposed algorithm improve of speed panoramic image generation compared to than the existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1950-1958
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• 2016

Today, technique for precisely positioning vehicles is very important in C-ITS(Cooperative Intelligent Transport System), Self-Driving Car and other information technology relating to transportation. Though the most popular technology for vehicle positioning is the GPS, its accuracy is not reliable because of large delay caused by multipath effect, which is very bad for realtime traffic application. Therefore, in this paper, we proposed the Vision-Based High Accuracy Vehicle Positioning Technology. At the first step of proposed algorithm, the ROI is set up for road area and the vehicles detection. Then, center and four corners points of found vehicles on the road are determined. Lastly, these points are converted into aerial view map using homography matrix. By analyzing performance of algorithm, we find out that this technique has high accuracy with average error of result is less than about 20cm and the maximum value is not exceed 44.72cm. In addition, it is confirmed that the process of this algorithm is fast enough for real-time positioning at the $22-25_{FPS}$.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.49-56
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• 2023

This study proposed a damage location estimation method for automated image-based port infrastructure inspection. Memory efficiency was improved by calculating the homography matrix using feature detection technology and outlier removal technology, without going through the 3D modeling process and storing only damage information. To develop an algorithm specialized for port infrastructure, the algorithm was optimized through ground-truth coordinate pairs created using images of port infrastructure. The location errors obtained by applying this to the sample and concrete wall were (X: 6.5cm, Y: 1.3cm) and (X: 12.7cm, Y: 6.4cm), respectively. In addition, by applying the algorithm to the concrete wall and displaying it in the form of an exterior damage map, the possibility of field application was demonstrated.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.111-114
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• 2017

본 논문은 동영상 스티칭의 속도 정확도를 향상시키기 위해 호모그래피 행렬 생성과 센서 데이터 활용을 통한 동영상 스티칭 방법을 제안한다. 본 논문에서는 임의의 호모그래피 행렬을 선형으로 생성하여 이미지를 스티칭 하는 방법을 설명하고, 이 과정에서 스티칭 정확도가 낮아지는 단점을 센서 데이터 활용을 통해 보완하는 방법을 소개한다. 1만 쌍의 모든 프레임에서 호모그래피 행렬을 생성 시키는 방법과 본 논문에 제안한 임의의 호모그래피 생성 방법을 비교하였을 때 평균 2.6초 걸리는 스티칭 시간을 약 1.5초 단축시켜 빠른 스티칭을 가능하게 하였다. 또한 선형 호모그래피 행렬만을 사용한 스티칭 한 결과보다 선형 호모그래피 행렬과 센서데이터를 함께 사용하였을 때의 정확도가 28.2% 개선되었음을 확인하였다.

• The Journal of Korea Robotics Society
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• v.10 no.1
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• pp.16-23
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• 2015

This paper presents a method of improving the pose recognition accuracy of objects by using Kinect sensor. First, by using the SURF algorithm, which is one of the most widely used local features point algorithms, we modify inner parameters of the algorithm for efficient object recognition. The proposed method is adjusting the distance between the box filter, modifying Hessian matrix, and eliminating improper key points. In the second, the object orientation is estimated based on the homography. Finally the novel approach of Auto-scaling method is proposed to improve accuracy of object pose estimation. The proposed algorithm is experimentally tested with objects in the plane and its effectiveness is validated.