• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2844-2851
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• 2015

How to recover from the multiple 2D images into 3D object has been widely studied in the field of computer vision. In order to improve the accuracy of the recovered 3D shape, it is more important that noise must be minimized and the number of image frames must be guaranteed. However, potential noise is implied when tracking feature points. And the number of image frames which is consisted of an observation matrix usually decrease because of tracking failure, occlusions, or low image resolution, and so on. Therefore, it is obviously essential that the number of image frames must be secured by recovering the missing feature points under noise. Thus, we propose the analytic approach which can control directly the error distance and orientation of missing feature point by the geometrical properties under noise distribution. The superiority of proposed method is demonstrated through experimental results for synthetic and real object.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.47-55
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• 2012

In this paper, we propose a method to reconstruct accurate 3D shape object by using multi-view images which are disturbed by motion blur. In multi-view deblurring, more precise PSF estimation can be done by using the geometric relationship between multi-view images. The proposed method first estimates initial 2D PSFs from individual input images. Then 3D PSF candidates are projected on the input images one by one to find the best one which are mostly consistent with the initial 2D PSFs. 3D PSF consists with direction and density and it represents the 3D trajectory of object motion. 야to restore 3D shape by using multi-view images computes the similarity map and estimates the position of 3D point. The estimated 3D PSF is again projected to input images and they replaces the intial 2D PSFs which are finally used in image deblurring. Experimental result shows that the quality of image deblurring and 3D reconstruction improves significantly compared with the result when the input images are independently deblurred.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.4-5
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• 2016

최근 현실 세계의 기반 위에 가상의 정보를 증강하여 사용자와 상호작용하며 즐기는 증강 현실 컨텐츠가 대중들에게 많은 인기를 얻고 있다. 이러한 증강 현실 콘텐츠는 현실 세계를 기반으로 한다는 점에서 실제의 3차원 공간을 정확하게 복원하는 것이 중요하다. 초기의 3차원 복원 방법으로 RGB-D 카메라를 이용한 KinectFusion 방법이 제안되었고 많은 연구자들에 의해 다루어지고 있다. 하지만 기존의 방법은 시간이 흐름에 따라 누적되는 오차에 의해 3차원 모델이 정확하게 복원되지 않는 객체 표류 문제가 발생한다. 이러한 문제는 깊이 카메라 센서의 잡음 때문에 정확하지 않은 표면 법선 벡터가 계산되는 것에 기인한다. 본 논문에서는 이러한 문제를 해결하기 위해 잡음에 강건한 표면 법선 벡터를 계산하는 방법을 제안한다. 실험결과에서는 기존의 방법과 비교하여 제안하는 방법이 절대 궤적 오차 (absolute trajectory error)가 감소하는 것을 확인 했고 카메라 궤적이 정확하게 예측되는 것을 확인할 수 있었다.

• Smart Media Journal
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• v.11 no.9
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• pp.75-80
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• 2022

Recently, research and development on immersive virtual reality(VR) technology to provide a realistic experience is being widely conducted. To provide realistic experience in immersive virtual reality for VR participants, virtual environments should consist of high-realistic environments using 3D reconstruction. In this paper, to acquire 3D information in real space using multiple cameras in the reconstruction process, we propose a novel method of optimal camera placement for accurate reconstruction to minimize distortion of 3D information. Through our approach in this paper, real 3D information can obtain with minimized errors during environment reconstruction, and it is possible to provide a more immersive experience with the created virtual environment.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.592-602
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• 2003

This paper aims to describe the basic structure of novel object-based 3D audio broadcasting system To overcome current uni-directional audio broadcasting services, the object-based 3D audio broadcasting system is designed for providing the ability to interact with important audio objects as well as realistic 3D effects based on the MPEG-4 standard. The system is composed of 6 sub-modules. The audio input module collects the background sound object, which is recored by 3D microphone, and audio objects, which are recorded by monaural microphone or extracted through source separation method. The sound scene authoring module edits the 3D information of audio objects such as acoustical characteristics, location, directivity and etc. It also defines the final sound scene with a 3D background sound, which is intended to be delievered to a receiving terminal by producer. The encoder module encodes scene descriptors and audio objects for effective transmission. The decoder module extracts scene descriptors and audio objects from decoding received bistreams. The sound scene composition module reconstructs the 3D sound scene with scene descriptors and audio objects. The 3D sound renderer module maximizes the 3D sound effects through adapting the final sound to the listner's acoustical environments. It also receives the user's controls on audio objects and sends them to the scene composition module for changing the sound scene.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.70-78
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• 2021

Estimating 3D information from a single image is one of the essential problems in numerous applications. Since a 2D image inherently might originate from an infinite number of different 3D scenes, thus 3D reconstruction from a single image is notoriously challenging. This challenge has been overcame by the advent of recent deep convolutional neural networks (CNNs), by modeling the mapping function between 2D image and 3D information. However, to train such deep CNNs, a massive training data is demanded, but such data is difficult to achieve or even impossible to build. Recent trends thus aim to present deep learning techniques that can be trained in a weakly-supervised manner, with a meta-data without relying on the ground-truth depth data. In this article, we introduce recent developments of weakly-supervised deep learning technique, especially categorized as scene 3D reconstruction and object 3D reconstruction, and discuss limitations and further directions.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.23-28
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• 2017

Shape from focus (SFF) is a technique used to reconstruct 3D shape of objects from a sequence of images obtained at different focus settings of the lens. In this paper, a new shape from focus method for 3D reconstruction of microscopic objects is described, which is based on gradient operator in Mathematical Morphology. Conventionally, in SFF methods, a single focus measure is used for measuring the focus quality. Due to the complex shape and texture of microscopic objects, single measure based operators are not sufficient, so we propose morphological operators with multi-structuring elements for computing the focus values. Finally, an optimal focus measure is obtained by combining the response of all focus measures. The experimental results showed that the proposed algorithm has provided more accurate depth maps than the existing methods in terms of three-dimensional shape recovery.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.193-202
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• 2008

In this study, we want to composite the source image and the target image when the environment includes water surface in the target image such as lake, sea, etc. The water surface is different from other common environment. On the water surface, the object must be reflected or refract and sometimes is deformed by the wave of water. In order to composite the object in the source image onto the water image, we analyze the water surface of the target image and let the object be synthesized realistically based on the wave of water. Our composite process consists of three steps. First. we use Shape-from-Shading technique to extract the normal vector of the water surface in the target image. Next, the source image is deformed according to the normal vector map. Finally, we composite the deformed object onto the target image.

• Korean Journal of Optics and Photonics
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• v.24 no.3
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• pp.120-124
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• 2013

We propose a method to reconstruct the two-dimensional object from an image captured through an array of random lenses each of which has random shape, size, and focal power. In the proposed method, the characteristics of the random lens array are estimated by capturing images for known elementary inputs, and then the object is reconstructed by measuring correlations between the random lens images of the object and the elementary inputs. The experimental results show that the original object can be recognized by the proposed reconstruction method. Nevertheless, further quality enhancement is required to increase feasibility and to extend to general three-dimensional object cases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.10
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• pp.911-919
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• 2013

Evaluation of the effect of radial distortion for a holographic stereogram demands conducting an experiment which comprises rendering of a 3D obejc, acquisition of perspective images, rearrangement of the acquired images for hogel images and quality assessment of the observing image reconstructed from the holographic stereogram. We propose numerical implementation of this evaluation by a specially developed algorithm for modeling of all required steps. The modeling is done by using a numerical model of an optical engine for generation of radially distorted hogel images at various degrees of distortion. The distorted images are used to form the holographic stereogram and to make the numerically reconstructed images from the holographic stereogram which are observed by an observer at desired location. The reconstructed images are compared by using PSNR.