• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.5
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• pp.59-67
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• 2004

Gait is defined as a manner or characteristics of walking. Recently, the study on extracting features of the gait to identify the individual has been progressed actively, within the computer vision community. Even if the camera is fixed, gait features extracted from images are varied according to the direction of walking. In this paper, we propose the method which compensates for the drawback of the gait recognition which is dependant on the direction. First, we search a direction of walking and estimate the planar homography with simple operations. Through synthesizing canonical viewed images by using the estimated homography, viewpoint variation by the direction of walking is compensated. In this paper, we segment gait silhouette into sub-regions and use averaged feature and its variation of each region to recognition experiment. Experimental results show that the proposed method is robust for directional variation of the gait.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.302-305
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• 2012

평면 추적(planar tracking) 기반의 카메라 추적에 있어, 특징 검출자의 반복성과 특징 기술자(descriptor)의 정합 성능에 따라서 떨림 현상(jitter)이 발생한다. 특히, 모바일 환경에서와 같은 연산력이 부족한 환경에서 고속화를 위해 특징 검출 및 기술 알고리즘을 간략화 시킬 경우, 이러한 떨림 현상은 심각한 문제가 된다. 본 논문에서는 이러한 문제를 해결하기 위해 입력 영상을 워핑(warping)하여 특징 점을 재검출한 후 카메라 영상과 참조 영상(reference image) 사이의 호모그래피를 보완하는 방법을 제안한다. 실험을 통해 제안된 방법이 특징 검출 및 기술 알고리즘의 성능을 보완하여 떨림 현상을 크게(70% 이상) 감소시킴을 확인하였다.

• Journal of KIISE
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• v.42 no.10
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• pp.1185-1194
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• 2015

A spherical panoramic image does not conform to the pin-hole camera model, and, hence, it is not possible to utilize previous techniques consisting of plane-to-plane transformation. In this paper, we propose a new method to estimate the planar geometric transformation between the planar image and a spherical panoramic image. Our proposed method estimates the transformation parameters for latitude, longitude, rotation and scaling factors when the matching pairs between a spherical panoramic image and a planar image are given. A planar image is projected into a spherical panoramic image through two steps of nonlinear coordinate transformations, which makes it difficult to compute the geometric transformation. The advantage of using our method is that we can uncover each of the implicit factors as well as the overall transformation. The experiment results show that our proposed method can achieve estimation errors of around 1% and is not affected by deformation factors, such as the latitude and rotation.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.51-60
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• 2007

This paper describes a visual tracking control law of an Unmanned Aerial Vehicle(UAV) for monitoring of structures and maintenance of bridges. It presents a control law based on computer vision for quasi-stationary flights above a planar target. The first part of the UAV's mission is the navigation from an initial position to a final position to define a desired trajectory in an unknown 3D environment. The proposed method uses the homography matrix computed from the visual information and derives, using backstepping techniques, an adaptive nonlinear tracking control law allowing the effective tracking and depth estimation. The depth represents the desired distance separating the camera from the target.

• Journal of Sensor Science and Technology
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• v.21 no.2
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• pp.96-102
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• 2012

This paper addresses photometric distortion problems of a compact 3D scanning sensor which is composed of a micro-size and inexpensive camera-projector system. Recently, many micro-size cameras and projectors are available. However, erroneous 3D scanning results may arise due to the poor and nonlinear photometric properties of the sensors. This paper solves two inherent photometric distortions of the sensors. First, the response functions of both the camera and projector are derived from the least squares solutions of passive and active calibration, respectively. Second, vignetting correction of the vision camera is done by using a conventional method, however the projector vignetting is corrected by using the planar homography between the image planes of the projector and camera, respectively. Experimental results show that the proposed technique enhances the linear properties of the phase patterns that are generated by the sensor.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1451-1454
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• 2015

RANSAC 알고리즘은 컴퓨터 비전 분야에서 호모그래피 행렬을 추정하는데 많이 사용되고 있다. CS-RANSAC 알고리즘은 RANSAC 알고리즘에 제약조건을 설정하여 정확도를 높인 알고리즘이지만 샘플링 단계에서 정확한 호모그래피를 추정하는데 불필요한 특징점을 선택하여 알고리즘의 효율성을 저하시키는 경우가 있다. 따라서 본 논문에서는 Symmetric Transfer Error로 특징점이 참정보인지 평가하고 불필요한 특징점을 필터링하여 CS-RANSAC 알고리즘의 속도와 정확도를 증가시키는 방법을 제안한다. 실험은 제안하는 알고리즘의 수행시간과 오차율을 비교하였고, 실험 결과 본 논문에서 제안한 방법이 기존 CS-RANSAC 알고리즘보다 수행시간이 평균적으로 약 5% 단축되었고 정확도는 약 14% 향상 되었다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.6
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• pp.123-130
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• 2014

In this study, a vision-based displacement measurement system is developed to accurately measure the displacement of a structure with locating the camera at arbitrary position. The previous vision-based system brings error when the optical axis of a camera has an angle with the measured structure, which limits the applicability at large structures. The developed system measures displacement by processing the images of a target plate that is attached on the measured position of a structure. To measure displacement regardless of the angle between the optical axis of the camera and the target plate, planar homography is employed to match two planes in image and world coordinate systems. To validate the performance of the present system, a laboratory test is carried out using a small 2-story shear building model. The result shows that the present system measures accurate displacement of the structure even with a camera significantly angled with the target plate.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.36-42
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• 2016

Recently, many 3D contents have been produced through the multiview camera system. In this system, since a difference of the viewpoint between color and depth cameras is inevitable, the camera parameter plays the important role to adjust the viewpoint as a preprocessing step. The conventional camera calibration method is inconvenient to users since we need to choose pattern features manually after capturing a planar chessboard with various poses. Therefore, we propose a semi-auto camera calibration method using a circular sampling and an homography estimation. Firstly, The proposed method extracts the candidates of the pattern features from the images by FAST corner detector. Next, we reduce the amount of the candidates by the circular sampling and obtain the complete point cloud by the homography estimation. Lastly, we compute the accurate position having the sub-pixel accuracy of the pattern features by the approximation of the hyper parabola surface. We investigated which factor affects the result of the pattern feature detection at each step. Compared to the conventional method, we found the proposed method released the inconvenience of the manual operation but maintained the accuracy of the camera parameters.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.42-52
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• 2017

Panorama image is a single image obtained by combining images taken at several viewpoints through matching of corresponding points. Existing panoramic image generation methods that find the corresponding points are extracting local invariant feature points in each image to create descriptors and using descriptor matching algorithm. In the case of video sequence, frames may be a lot, so therefore it may costs significant amount of time to generate a panoramic image by the existing method and it may has done unnecessary calculations. In this paper, we propose a method to quickly create a single panoramic image from a video sequence. By assuming that there is no significant changes between frames of the video such as in locally, we use the FAST algorithm that has good repeatability and high-speed calculation to extract feature points and the Lucas-Kanade algorithm as each feature point to track for find the corresponding points in surrounding neighborhood instead of existing descriptor matching algorithms. When homographies are calculated for all images, homography is changed around the center image of video sequence to warp images and obtain a planar panoramic image. Finally, the spherical panoramic image is obtained by performing inverse transformation of the spherical coordinate system. The proposed method was confirmed through the experiments generating panorama image efficiently and more faster than the existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.610-630
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• 2020

A large number of people suffered from traffic accidents each year, so people pay more attention to traffic safety. However, the traditional methods use laser sensors to calculate the vehicle distance at a very high cost. In this paper, we propose a method based on deep learning to calculate the vehicle distance with a monocular camera. Our method is inexpensive and quite convenient to deploy on the mobile platforms. This paper makes two contributions. First, based on Light-Head RCNN, we propose a new vehicle detection framework called Light-Car Detection which can be used on the mobile platforms. Second, the planar homography of projective geometry is used to calculate the distance between the camera and the vehicles ahead. The results show that our detection system achieves 13FPS detection speed and 60.0% mAP on the Adreno 530 GPU of Samsung Galaxy S7, while only requires 7.1MB of storage space. Compared with the methods existed, the proposed method achieves a better performance.