• Journal of Biomedical Engineering Research
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• 제22권2호
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• pp.127-132
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• 2001

의료 영상에서 관심 있는 부위를 3차원으로 재구성하여 보는 것은, 정확한 진단을 위해서 매우 중요하다. 이러한 3차원 재구성을 위해서는 관심 있는 영역의 분할이 필수적인 선행작업이다. 본 논문에서는 관도계 기관의 분할을 위해서 슬라이스 영상의 정보를 이용한 3차원 영역 성장법을 제안한다. 제안된 방법은 2차원 슬라이스 영상에서 영역 성장법에 의해 영역을 확장시키고, 그 이웃한 슬라이스들에 씨앗점을 전달하여 재귀적으로 3차원 체적을 확장하여 영상을 분할한다. 이때, 이웃한 슬라이스간의 영역의 크기의 제약을 이용하여 새나감을 방지한다. 제안된 방법을 기관지의 분할에 적용한 결과, 새나감 없이 뾰족한 가지들까지도 성공적으로 분할했으며, 튜브의 중심 축이 고차원 곡선인 경우에도 성공적으로 분할했다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• 제41권5호
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• pp.111-116
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• 2004

It is essential to know the information about the prior model for wavelet coefficients, the probability distribution of noise, and the variance of wavelet coefficients for noise reduction using Bayesian estimation in wavelet domain. In general denoising methods, the signal variance is estimated from the proper prior model for wavelet coefficients. In this paper, we propose a variable window size decision algorithm to estimate signal variance according to image region. Simulation results shows the proposed method have better PSNRs than those of the state of art denoising methods.

• Journal of Korea Multimedia Society
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• 제3권2호
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• pp.157-163
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• 2000

Introduction of motion video including live video into network virtual reality systems makes virtual spaces more attractive. To handle live video in networked virtual reality s)'stems based on VRML, the scalability of networked virtual reality systems becomes very important on the internet where the performance of the network and the end systems varies dynamically. In this paper, a new quality control algorithm suitable for scalable networked virtual reality systems with live video capability is proposed. Our approach is to introduce the notion of the importance of presence (IoP) which represents the importance of objects in virtual spaces. According to IoPs, the degree of the deterioration of each object presentation will be determined in case of the starvation of system resources.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2019년도 추계학술발표대회
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• pp.1077-1079
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• 2019

일반적으로 줄어든 동적 범위를 확장하는 기법은 사진의 동적 범위를 충분히 포함하는 노출이 다른 여러 사진을 합성한다. 본 논문에서는 하나의 사진만으로 동적 범위가 확장하는 방법을 제안하였다. 하나의 사진을 딥러닝을 이용하여 구현한 네트워크가 입력 이미지의 동적 범위를 확장하도록 학습시켰다. 구현된 네트워크를 평가하기 위해 HDRNet의 결과물과 비교를 하였다. 그 결과 제안한 방법으로 얻은 이미지는 HDRNet과 비교하여 영상의 대조비가 향상되는 것을 확인하였다.

• Journal of the Korea Society of Computer and Information
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• 제14권10호
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• pp.83-92
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• 2009

In this paper, an extended snake algorithm using color variance energy is proposed for segmenting an interest object in color image. General snake algorithm makes use of energy in image to segment images into a interesting area and background. There are many kinds of energy that can be used by the snake algorithm. The efficiency of the snake algorithm is depend on what kind of energy is used. A general snake algorithm based on active contour model uses the intensity value as an image energy that can be implemented and analyzed easily. But it is sensitive to noises because the image gradient uses a differential operator to get its image energy. And it is difficult for the general snake algorithm to be applied on the complex image background. Therefore, the proposed snake algorithm efficiently segment an interest object on the color image by adding a color variance of the segmented area to the image energy. This paper executed various experiments to segment an interest object on color images with simple or complex background for verifying the performance of the proposed extended snake algorithm. It shows improved accuracy performance about 12.42 %.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2022년도 춘계학술대회
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• pp.431-433
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• 2022

With the development of IoT technology, various technologies such as artificial intelligence and automation are being grafted into industrial sites, and accordingly, the importance of data processing is increasing. Image denoising is one of the basic processes of image processing, and is used as a preprocessing step in many applications. Various studies have been conducted to remove noise, but various problems arise in the process of noise removal, such as image detail preservation, texture restoration, and special noise removal. In this paper, we propose a digital filter using an extended convolutional mask to preserve image detail during the impulse denoising process. The proposed algorithm uses an extended convolution mask as a filtering mask, and obtains the final output by switching the extension level according to the noise level. Simulation was conducted to evaluate the performance of the proposed algorithm, and the performance was analyzed compared to the existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제34권1C호
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• pp.48-55
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• 2009

In scalable video coding, MCTF plays an important role for time-scalability and SNR-scalability. But there is image quality decreasing as MCTF level is increased because time interval of each frame is extended so that is hard to find suitable motion vector. In this paper, we propose an algorithm to prevent image quality from decreasing with unsuitable motion vector during MCTF update process using JND. We adapt JND to find errors within blocks of image and set a threshold which is used to add high frequency components during update process. We can overcome time-gap between frames and achieve better image quality through the proposed algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.429-431
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• 2019

Noise in image processing has a direct effect on the quality of the image, and adversely affects the processing of the system including algorithms such as image segmentation, edge detection, and image recognition. Therefore, noise reduction plays an important role in the preprocessing process. In this paper, we propose an efficient algorithm to remove noise in high density of Salt and Pepper noise. The proposed algorithm removes noise by gradually expanding the filtering mask according to the density of the noise, and shows excellent noise cancellation performance even in a high density region. In order to evaluate the performance of the proposed algorithm, we compared and analyzed the existing method and the proposed algorithm through simulation.

• The Journal of the Convergence on Culture Technology
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• 제4권3호
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• pp.241-246
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• 2018

Because of the mechanical problems of input camera equipment, image restoration process is performed in order to minimize recognition errors due to the noise problem generated in test data image. The image restoration method resolves the noise problem by examining the numbers and positions of the Direct neighbors and the Indirect neighbors for each pixel constituting the test data. As a result, satisfactory recognition result can be obtained by eliminating the noise problem generated in the test data through the image restoration process as much as possible and also by calculating the differences between the learning data and the test data in the area unit, thereby reducing the possibility of recognition error by the noise problem.

• The KIPS Transactions:PartB
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• 제9B권3호
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• pp.319-326
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• 2002

In this paper, we have designed and implemented cluttering classification systems- unsupervised classifiers-for the processing of satellite remote sensing images. Implemented systems adopt various design patterns which include a factory pattern and a strategy pattern to support various satellite images'formats and to design compatible systems. The clustering systems consist of sequential clustering, K-Means clustering, ISODATA clustering and Fuzzy C-Means clustering classifiers. The systems are tested by using a Landsat TM satellite image for the classification input. As results, these clustering systems are well designed to extract sample data for the classification of satellite images of which there is no previous knowledge. The systems can be provided with real-time base clustering tools, compatibilities and components' reusabilities as well.