• Journal of Digital Contents Society
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• v.15 no.4
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• pp.521-526
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• 2014

The purpose of this study is to propose the efficient method of 3D data registration. Three-dimensional data including the two-dimensional image acquisition apparatus and the position information are acquired at an arbitrary angle with each other. This paper proposes the more accurate and faster matching method by using this information. Four image points founded from 2D images match the volumetric size of the model and compute the homography of the axis for registration between two 3D data sets. The advantages of the proposed algorithm are the repeating process is unnecessary and the process time is faster than prvious method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.381-390
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• 2006

This paper proposes a new technque of the camera calibration to be computed a homography between the planar patterns taken by a single image to be located at the three planar patterns from uncalibrated images. It is essential to calibrate a camera for 3-dimensional reconstruction from uncalibrated image. Since the proposed method should be computed from the homography among the three planar patterns from a single image, it is implemented to more easily and simply to recover 3D reconstruction of an object than the conventional. Experimental results show the performances of the proposed method are the better than the conventional. We demonstrate examples of recovering 3D reconstruction using the proposed algorithm from uncalibrated images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.5
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• pp.1162-1170
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• 2014

In this paper, an accurate remote gaze tracking method with two cameras is presented using a modified Starburst algorithm and honography normalization. Starburst algorithm, which was originally developed for head-mounted systems, often fails in detecting accurate pupil centers in remote tracking systems with a larger field of view due to lots of noises. A region of interest area for pupil is found using template matching, and then only within this area Starburst algorithm is applied to yield pupil boundary candidate points. These are used in improved RANSAC ellipse fitting to produce the pupil center. For gaze estimation robust to head movement, an improved homography normalization using four LEDs and calibration based on high order polynomials is proposed. Finally, it is shown that accuracy and robustness of the system is improved using two cameras rather than one camera.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.848-855
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• 2008

In this paper, we describe correspondence among multiple images taken by multiple cameras. The correspondence among multiple views is an interesting problem which often appears in the application like visual surveillance or gesture recognition system. We use the principal axis and the ground plane homography to estimate foot of human. The principal axis belongs to the subtracted silhouette-based region of human using subtraction of the predetermined multiple background models with current image which includes moving person. For the calculation of the ground plane homography, we use landmarks on the ground plane in 3D space. Thus the ground plane homography means the relation of two common points in different views. In the normal human being, the foot of human has an exactly same position in the 3D space and we represent it to the intersection in this paper. The intersection occurs when the principal axis in an image crosses to the transformed ground plane from other image. However the positions of the intersection are different depend on camera views. Therefore we construct the correspondence that means the relationship between the intersection in current image and the transformed intersection from other image by homography. Those correspondences should confirm within a short distance measuring in the top viewed plane. Thus, we track a person by these corresponding points on the ground plane. Experimental result shows the accuracy of the proposed algorithm has almost 90% of detecting person for tracking based on correspondence of intersections.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.477-483
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• 2019

In this paper, we propose vanishing-point position-improvement algorithms by using multiple images and a homography matrix. Vanishing points can be detected from a single image, but the positions of detected vanishing points can be improved if we adjust their positions by using information from multiple images. More accurate indoor space information detection is possible through vanishing points with improved positional accuracy. To adjust a position, we take three images and detect the information, detect the homography matrix between the walls of the images, and convert the vanishing point positions using the detected homography. Finally, we find an optimal position among the converted vanishing points and improve the vanishing point position. The experimental results compared an existing algorithm and the proposed algorithm. With the proposed algorithm, we confirmed that the error angle to the vanishing point position was reduced by about 1.62%, and more accurate vanishing point detection was possible. In addition, we can confirm that the layout detected by using improved vanishing points through the proposed algorithm is more accurate than the result from the existing algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.203-205
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• 2010

파노라마 영상을 재구성하는 기존의 방법은 Labeling을 이용하여 객체를 비교한 후에 결합시키는 방법을 적용하였으나 시간이 많이 소요되고 각각의 이미지를 Labeling하는 과정에서 개체 간의 불일치가 발생하여 정확히 영상을 결합할 수 없는 경우가 발생한다. 따라서 본 논문에서는 처리 속도 개선을 위하여 전체 이미지의 1/3만 Labeling한 후에 객체 간을 비교하여 결함시킨다. 그리고 각도가 틀린 경우에는 특징점을 찾아내는 SURF 알고리즘을 적용하여 각각의 이미지에서 Labeling한 사각형의 4개의 포인터에 대해 1개의 중심점을 구하여 Homography를 이용하여 2개의 영상을 자연스럽게 정합한다. 본 논문에서 제안한 파노라마 영상 재구성 방법의 성능을 평가하기 위하여 다양한 이미지를 대상으로 실험한 결과, 기존의 방법보다 영상을 재구성하는데 효과적인 것을 확인하였다. 그리고 처리 속도 측면에서도 개선되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2299-2311
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• 2013

As portable multimedia devices become more popular and smaller, the use of portable projectors is also rapidly increasing. However, when portable projectors are used in mobile environments in which a dedicated planar screen is not available, the problem of geometric distortion of the projected image often arises. In this paper, we present a geometric image compensation method for portable projectors to compensate for geometric distortions of images projected on various types of planar or nonplanar projection surfaces. The proposed method is based on extraction of the two-dimensional (2D) geometric information of a projection area, setting of the compensation area, and prewarping using 2D homography. The experimental results show that the proposed method allows effective compensation for waved and arbitrarily shaped projection areas, as well as tilted and bent surfaces that are often found in the mobile environment. Furthermore, the proposed method is more computationally efficient than conventional image compensation methods that use 3D geometric information.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.297-301
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• 2005

In this paper, we propose a method to estimate camera motion parameter based on invariant point features. Typically, feature information of image has drawbacks, it is variable to camera viewpoint, and therefore information quantity increases after time. The LM(Levenberg-Marquardt) method using nonlinear minimum square evaluation for camera extrinsic parameter estimation also has a weak point, which has different iteration number for approaching the minimal point according to the initial values and convergence time increases if the process run into a local minimum. In order to complement these shortfalls, we, first propose constructing feature models using invariant vector of geometry. Secondly, we propose a two-stage calculation method to improve accuracy and convergence by using homography and LM method. In the experiment, we compare and analyze the proposed method with existing method to demonstrate the superiority of the proposed algorithms.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.143-146
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• 2000

본 논문에서는 다수의 실내 영상으로부터 영상을 촬영한 카메라의 속성정보와 실내 환경에 대한 기하정보를 추출하는 방법을 제안한다. BSP-Tree를 이용하여 주어진 실영상을 각각의 부분 영역이 실제로도 평면 영역에 해당되도록 분할하였으며, 특징점 대응을 통해 각 분할된 영역의 영상간 대응을 찾고 이로부터 각 분할 영역의 homography를 계산하였다 또한 간단한 가정을 통해 계산된 homography로부터 각 분할영역에 대응된 평면의 방정식과 각 영상을 촬영한 카메라의 속성을 찾아낼 수 있믐을 보였다. 본 논문에서 제안한 방법은 현재 본 연구팀이 구현 중인 영상기반 모델링 시스템에서 핵심적인 기능을 수행하리라 기대된다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2363-2371
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• 2015

In this paper, we propose a new feature matching algorithm which is robust to the viewpoint change by using the FAST(Features from Accelerated Segment Test) feature detector and the SIFT(Scale Invariant Feature Transform) feature descriptor. The original FAST algorithm unnecessarily results in many feature points along the edges in the image. To solve this problem, we apply the principal curvatures for refining it. We use the SIFT descriptor to describe the extracted feature points and calculate the homography matrix through the RANSAC(RANdom SAmple Consensus) with the matching pairs obtained from the two different viewpoint images. To make feature matching robust to the viewpoint change, we classify the matching pairs by calculating the Euclidean distance between the transformed coordinates by the homography transformation with feature points in the reference image and the coordinates of the feature points in the different viewpoint image. Through the experimental results, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load.