• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.397-400
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• 2017

Recently, a lot of images containing various global movements have been generated by the activation of the photographic equipment such as the drone and the action cam. In this case, when the motion such as rotation, scaling is generated, it is difficult to expect a high coding efficiency in the conventional inter-picture prediction method using the 2D motion vector. In this paper, we propose a video coding method that reflects global motion through homography reference pictures. As a proposed method, there are 1) a method of generating a new reference picture by grasping a global motion relation between a current picture and a reference picture by homography, and 2) a method of utilizing a homography reference picture for inter-picture prediction. The experiment was applied to the HEVC reference software HM 14.0, and the experimental result showed an increase in encoding efficiency of 6.6% based on RA. Especially, the results using the videos with rotational motion have a maximum coding efficiency of 32.6%, which is expected to show high efficiency in video, which is often represented by complex global motion such as drones.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.983-988
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• 2011

This paper presents a robust method for ground plane detection in vision-based applications based on a monocular sequence of images with a non-stationary camera. The proposed method, which is based on the reliable estimation of the homography between two frames taken from the sequence, aims at designing a practical system to detect road surface from traffic scenes. The homography is computed using a feature matching approach, which often gives rise to inaccurate matches or undesirable matches from out of the ground plane. Hence, the proposed homography estimation minimizes the effects from erroneous feature matching by the evaluation of the difference between the predicted and the observed matrices. The method is successfully demonstrated for the detection of road surface performed on experiments to fill an information void area taken place from geometric transformation applied to captured images by an in-vehicle camera system.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.215-224
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• 2013

An efficient homography estimation method for large sized images is proposed. Estimating an accurate homography is one of the most important parts in image stitching processes. Since hardwares have been advanced, it has been passible to take higher resolution images. However, computational cost for estimating homography has been also increased. Specifically, when too many features exist in the images, it requires lots of computations to estimate a correct homography. Furthermore, there is a high probability of obtaining an incorrect homography. Therefore, we propose a numerical method to extract the appropriate correspondences from several down-scaled images to estimate and compensate the homography numerically for restoring an original homography. Also, if there is an unbalance in color tone between the reference and the target images, we make them balanced by using local information of the overlapped regions. Experimental results show that proposed method is three times faster in 3.2 mega pixel images, five times faster in 8mega pixel images than the conventional approach. Therefore, we believe that the proposed method can be a useful tool to efficiently estimate a homography.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.6
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• pp.307-312
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• 2016

Markerless tracking for augmented reality using Homography can augment virtual objects correctly and naturally on live view of real-world environment by using correct pose and direction of camera. The RANSAC algorithm is widely used for estimating Homography. CS-RANSAC algorithm is one of the novel algorithm which cooperates a constraint satisfaction problem(CSP) into RANSAC algorithm for increasing accuracy and decreasing processing time. However, CS-RANSAC algorithm can be degraded performance of calculating Homography that is caused by selecting feature points which estimate low accuracy Homography in the sampling step. In this paper, we propose feature point filtering method based on CS-RANSAC for efficient planar Homography estimating the proposed algorithm evaluate which feature points estimate high accuracy Homography for removing unnecessary feature point from the next sampling step using Symmetric Transfer Error to increase accuracy and decrease processing time. To evaluate our proposed method we have compared our algorithm with the bagic CS-RANSAC algorithm, and basic RANSAC algorithm in terms of processing time, error rate(Symmetric Transfer Error), and inlier rate. The experiment shows that the proposed method produces 5% decrease in processing time, 14% decrease in Symmetric Transfer Error, and higher accurate homography by comparing the basic CS-RANSAC algorithm.

• 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 the Semiconductor & Display Technology
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• v.14 no.3
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• pp.67-72
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• 2015

This paper proposes an object tracking system based on keypoints using homography in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information. Tracking module tracks an object using homography information that generate by being matched on the learned object keypoints to the current object keypoints. Then update the window included the object for defining object's pose. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track objects with updating object's pose for the use of mobile platform.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.804-809
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• 2004

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

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.789-792
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• 2011

자동차를 위한 전방향(omnidirectional) 감시 시스템, 교통 정보 수집 시스템 등 다양한 비젼 시스템에 카메라가 장착되어 사용되고 있다. 최근에는 운전자의 편의를 위하여 광각 카메라의 비선형적인 방사 왜곡을 해결하는 왜곡 보정 작업 등의 영상처리 시스템이 많이 발전하여 운전자의 사각지대를 효율적으로 최소화하고 있다. 그러나 기존의 연구에서는 카메라로부터 입력되는 왜곡 영상을 보정하는데에 별도의 H/W인 DSP(Digital Signal Processes) 또는 SOC(System On Chp) 형태의 전용 H/W를 추가하여 임베디드 시스템의 성능을 보완하고 있다. 하지만 위와 같은 별도의 H/W를 추가하여 임베디드 시스템의 성능을 보완할 경우 시스템이 복잡해지고 가격이 비싸진다는 단점이 있다. 본 논문에서는 이러한 문제점을 보완하기 위하여 왜곡 보정 알고리즘과 호모그래피(Homography) 알고리즘의 연산량을 감소시켜 임베디드 시스템 환경에서 추가의 H/W 비용없이 왜곡 보정을 수행하는 알고리즘을 제안하고, 제안한 알고리즘을 구현하여 실제 시스템에 적용한 결과를 제시하여 구현 타당성을 검증한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.503-506
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• 2011

최근 다양하게 등장하고 있는 운전자를 위한 보조 장치 중 운전자의 주차를 위해 사용되는 장치들 중 하나인 차량용 카메라는 주로 차량 후방의 번호판 위에 주로 위치하여 운전자의 주차에 도움을 주는 역할에 사용이 되고 있다. 최근에는 이러한 카메라를 이용하여 전 후방 및 좌 우측을 모두 보여주기 위한 AVM(Around View Monitor) 시스템이 개발되었다. 그러나 다수의 카메라를 사용하는 AVM시스템에서 운전자에게 통합된 영상을 제공하기 위해서는 카메라의 방사왜곡보정과 호모그래피(Homography) 알고리즘을 통해 정합하는 과정이 필요하다. 본 논문에서는 호모그래피 과정에서 결과 영상의 품질을 개선하기 위한 방법을 제안한다. 또한, 제안하는 방법을 구현하여 기존의 8DOF(Degree of Freedom)을 사용한 방법과 결과 영상의 품질을 비교함으로써 개선된 영상을 제공할 수 있음을 제시한다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.3
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• pp.27-33
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• 2012

Tele-presence and tele-operation techniques are used to build up an immersive scene and control environment for the distant user. This paper presents a novel tele-presence system using the camera tracking based on planar homography. In the first step, the user wears the HMD(head mounted display) with the camera and his/her head motion is estimated. From the panoramic image by the omni-directional camera mounted on the mobile robot, a viewing image by the user is generated and displayed through HMD. The homography of 3D plane with markers is used to obtain the head motion of the user. For the performance evaluation, the camera tracking results by ARToolkit and the homography based method are compared with the really measured positions of the camera.