• Journal of Digital Contents Society
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• 제5권4호
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• pp.311-315
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• 2004

In this paper, we proposed a robust watermarking algorithm based on fuzzy inference and human visual system. In the first, discrete wavelet transform(DWT) is involved to calculate additive energy strength, then we devised fuzzy inference, which was established by computing contrast and texture degree in gray-level image. Watermark is embeded into the coefficients of 3-level DWT so as to consider a spatial effects. Visual recognizable patterns such as binary image were used as a watermark Consequently, experimental results showed that proposed algorithm is robust in JPEC compression.

• Journal of the Korea Computer Graphics Society
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• 제11권1호
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• pp.55-61
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• 2005

본 논문에서는 가상협업환경에서의 텔레프레센스를 제공하기 위하여 실사 기반 사용자 모습을 가상세계에 투영하여 가상객체와 함께 보여주는 사용자 미러링 기법을 제안한다. 가상협업환경 구축 시 널리 이용되는 $CAVE^{TM}-like$ 시스템과 같은 프로젝션 기반 가상환경에서는 스크린으로 투사되는 빛의 변화로 인하여 강건한 사용자 영역 정의가 쉽지 않다. 본 논문에서는 이와 같은 문제를 해결하기 위하여 적외선 반사 영상을 이용하여 사용자 영역을 정의하고 이 영역의 텍스처 정보를 칼라 카메라 영상을 통해 제공할 수 있도록 하였다. 제안 기법을 이용하면 가상환경 내에 존재하는 사용자를 상대측 가상세계에 미러링 하여 보여줄 수 있으므로 효과적인 텔레프레센스를 제공이 가능하다. 제안된 기법은 이화여자대학교 컴퓨터 그래픽스/가상현실 연구센터에 설치된 $CAVE^{TM}-like$ 시스템 상에서 실험하여 검증하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 한국방송∙미디어공학회 2020년도 하계학술대회
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• pp.546-549
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• 2020

본 논문에서는 최신 비디오 코딩 기술에서 잔차(Residual)신호 변환을 효율적으로 수행하기 위한 부동기저(Basis)를 사용하는 방법을 제안한다. 기존의 DCT-II 나 DST-VII 과 같은 고정 기저를 사용하는 방법은 대부분의 잔차신호들에 대해 효과적으로 비상관화(decorrelation)를 수행하지만 복잡한 잔차 신호일수록 성능이 떨어지는 문제가 있었다. 이러한 압축 성능하락 문제를 줄이기 위하여 PCA(Principle Component Analysis) 방법 중 하나인 KLT(Karhunen-Loeve Transform)를 이용하여 부동(floating) 변환 기저를 유도하는 방법을 제안한다. 기존의 KLT 를 이용한 변환 커널 유도 방법들의 문제점인 부호화기 및 복호화기 계산 복잡도를 줄이기 위하여 KL 커널을 분해가능한(Separable) 2 개의 1 차원 커널로 유도하는 방법을 제안하고, 원본 잔차신호와 유사한 텍스처를 찾아 커널을 예측하는 과정을 간소화하는 방법을 제안한다. 제안하는 방법은 HEVC 에서 실험되었으며 정지영상 코딩 Main-Profile 에서 평균 1.4%가량의 BD-PSNR(Bjontegaard Delta-Peak Signal to Noise Ratio) 성능 향상을 보였으며 특히 스크린 컨텐츠 영상에서 최대 4.5%의 성능 향상을 보인다.

• The Journal of the Korea Contents Association
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• 제14권7호
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• pp.1-9
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• 2014

Previous 2D-3D conversion methods to generate 3D stereo images from 2D sequence consist of labor-intensive procedures in their production pipelines. This paper presents an efficient 2D-3D conversion system based on scene structure reconstruction from image sequence. The proposed system reconstructs a scene space and produces 3D stereo images with texture re-projection. Experimental results show that the proposed method can generate precise 3D contents based on scene structure information. By using the proposed reconstruction tool, the stereographer can collaborate efficiently with workers in production pipeline for 3D contents production.

• Journal of Broadcast Engineering
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• 제26권1호
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• pp.88-98
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• 2021

Recently, the crime prevention and culprit identification even in a low illuminated environment by CCTV is becoming ever more important. In a low lighting situation, CCTV applications capture images under infrared lighting since it is unobtrusive to human eye. Although the infrared lighting leads to advantage of capturing an image with abundant fine texture information, it is hard to capture the color information which is very essential in identifying certain objects or persons in CCTV images. In this paper, we propose a method to acquire color information through DCGAN from an image captured by CCTV in a low lighting environment with infrared lighting and a method to remove color noise in the acquired color image.

• Korean Journal of Remote Sensing
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• 제37권6_1호
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• pp.1527-1543
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• 2021

In this paper, we explore the applicability and utility of a technology that generating clustered solid building models based on point cloud automatically by applying it to various data. In order to improve the quality of the model of insufficient quality due to the limitations of the automatic building modeling technology, we develop the building shape modification and texture correction technology and confirmed the resultsthrough experiments. In order to explore the applicability of automatic building model generation technology, we experimented using point cloud and LiDAR (Light Detection and Ranging) data generated based on UAV, and applied building shape modification and texture correction technology to the automatically generated building model. Then, experiments were performed to improve the quality of the model. Through this, the applicability of the point cloud data-based automatic clustered solid building model generation technology and the effectiveness of the model quality improvement technology were confirmed. Compared to the existing building modeling technology, our technology greatly reduces costs such as manpower and time and is expected to have strengths in the management of modeling results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제46권5호
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• pp.111-119
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• 2009

This paper presents an adaptive image interpolation algorithm using local characteristics. An input image is classified into edge region and flat low frequency region. And then, the edge region is further partitioned into directive edge region and high frequency texture region. A bilinear interpolation is applied to flat low frequency region, cubic convolution is applied to texture region, and new edge directed interpolation to directive edge region, respectively. Simulation results show that the proposed algorithm outperforms the existing interpolation methods in terms of visual quality as well as PSNR.

• Journal of the Korea Society of Computer and Information
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• 제7권1호
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• pp.112-119
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• 2002

We propose an enhanced wavelet packet image coder algorithm which is based on the coefficients partition. The proposed wavelet packet image coder uses the first-order entropy to reduce the total compression time, and achieves low bit rates and rate-distortion performance by the zero-tree based coding using correlations between coefficients partition. This new algorithm represents new parent-children relationships for reducing image reconstruction error using the correlations between each frequency subbands and then the wavelet packet coefficients are Partitioned by a new order. The computer simulations demonstrate higher PSNR under the same bit rate and improved image compression time and enhanced rate control compare with conventional algorithms. From the simulation results, it is shown that the encoding and decoding process of proposed coder are much simple and accurate than present method against texture images , which include many mid-frequency elements.

• Journal of the Institute of Electronics Engineers of Korea CI
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• 제43권3호
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• pp.67-75
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• 2006

Facial expressions exhibit not only facial feature motions, but also subtle changes in illumination and appearance. Since it is difficult to generate realistic facial expressions by using only geometric deformations, detailed features such as textures should also be deformed to achieve more realistic expression. The existing methods such as the expression ratio image have drawbacks, in that detailed changes of complexion by lighting can not be generated properly. In this paper, we propose a nonlinear model for skin color change and a model-based synthesis method for facial expression that can apply realistic expression details under different lighting conditions. The proposed method is composed of the following three steps; automatic extraction of facial features using active appearance model and geometric deformation of expression using warping, generation of facial expression using a model for nonlinear skin color change, and synthesis of original face with generated expression using a blending ratio that is computed by the Euclidean distance transform. Experimental results show that the proposed method generate realistic facial expressions under various lighting conditions.

• Journal of the Korea Society of Computer and Information
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• 제17권7호
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• pp.45-52
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• 2012

In this paper, an crowd density in elevator estimation method based on multi-class Adaboost classifier is proposed. The SOM (Self-Organizing Map) based conventional methods have shown insufficient performance in practical scenarios and have weakness for low reproducibility. The proposed method estimates the crowd density using multi-class Adaboost classifier with texture features, namely, GLDM(Grey-Level Dependency Matrix) or GGDM(Grey-Gradient Dependency Matrix). In order to classify into multi-label, weak classifier which have better performance is generated by modifying a weight update equation of general Adaboost algorithm. The crowd density is classified into four categories depending on the number of persons in the crowd, which can be 0 person, 1-2 people, 3-4 people, and 5 or more people. The experimental results under indoor environment show the proposed method improves detection rate by about 20% compared to that of the conventional method.