• Journal of Digital Convergence
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• v.10 no.7
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• pp.167-172
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• 2012

In vocabulary recognition using HMM(Hidden Markov Model) by model for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate has the disadvantage that require sophisticated smoothing process. Gaussian mixtures in order to improve them with a continuous probability density CHMM (Continuous Hidden Markov Model) model is proposed for the optimization of the library system. In this paper is system configuration thread control in recognition Gaussian mixtures model provides a model to optimize of the CHMM vocabulary recognition. The result of applying the proposed system, the recognition rate of 98.1% in vocabulary recognition, respectively.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.61-73
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• 2012

This paper proposes a histogram specification based image enhancement method, which uses the brightness mean and variance of an image to maximize the entropy of the image. In our histogram specification step, the Gaussian distribution is used to fit the input histogram as well as produce the target histogram. Specifically, the input histogram is fitted with the Gaussian distribution whose mean and variance are equal to the brightness mean(${\mu}$) and variance(${\sigma}2$) of the input image, respectively; and the target Gaussian distribution also has the mean of the value ${\mu}$, but takes as the variance the value which is determined such that the output image has the maximum entropy. Experimental results show that compared to the existing methods, the proposed method preserves the mean brightness well and generates more natural looking images.

• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.1-6
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• 2016

The biased zero-error probability and its related algorithms require heavy computational burden related with some summation operations at each iteration time. In this paper, a recursive approach to the biased zero-error probability and related algorithms are proposed, and compared in the simulation environment of shallow water communication channels with ambient noise of biased Gaussian and impulsive noise. The proposed recursive method has significantly reduced computational burden regardless of sample size, contrast to the original MBZEP algorithm with computational complexity proportional to sample size. With this computational efficiency the proposed algorithm, compared with the block-processing method, shows the equivalent robustness to multipath fading, biased Gaussian and impulsive noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.78-81
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• 2012

In this paper, we proposed the new vehicle license plates detection method which is available in a various fields, including vehicle access control, illegal parking and speeding vehicle crack down. First, we binarize an image by using difference of gaussian filter to find a sequence of numbers of plates. Second, we determine the plate region by labeling repeatedly using the morphological characteristics of the plates. Finally, we use a projective transformation for correcting the distortion that occurs because of the camera or the location of the vehicle.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2265-2271
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• 2014

In modern society digital equipments that are related with various hardware and software are popularized, and digital images are widely applied in the field of production and scientific research. In general, however, images are degraded by the noise in the process of transmission and storage. In this paper, to reduce the influence of mixed noises, the algorithm in which noises in the space area are classified into impulse noise and Gaussian noise and this is processed by applying weighted value, while that is processed by modified nonlinear filter is proposed. And the excellence of the proposed algorithm is judged by PSNR(peak signal to noise ratio).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.104-107
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• 2018

최근 촬영 기기의 기술발전으로 인해 디지털 영상의 해상도가 증가함에 따라 선명한 디지털 영상에 대한 요구가 증가하고 있다. 이러한 요구에도 불구하고 디지털 영상 내 가우시안 잡음 (gaussian noise)은 촬영기기를 통해 영상 획득 및 처리 과정에서 발생하여 화질을 열화 시킨다. 디지털 이미지에서 발생하는 가우시안 잡음을 제거하기 위해서 기존의 저대역 통과 필터 (low-pass filter: LPF)를 사용하면 잡음은 제거되지만, 블러링 현상 (blurring phenomenon)이 나타난다. 이러한 문제점을 개선하기 위해 소벨 연산자 (sobel operator)를 사용하여 영상 내 에지 맵 (edge-map)을 생성하여 에지 영역과 동질 영역을 구분한다. 에지영역에서는 약한 저역 필터 (weak low-pass filter)를 사용하고, 그 외의 이미지 영역에서는 강한 저역 필터 (strong low-pass filter)를 사용하는 알고리듬을 제안하였다. 그리고 다양한 이미지에 대하여 기존 알고리듬과 제안한 알고리듬의 적용한 결과를 통해 주관적 화질 비교하였고 객관적 지표로 최대 신호 대 잡음비 (peak signal-to noise ratio: PSNR)와 구조 유사성 (structural similarity: SSIM)을 사용하여 성능을 평가하였다. 실험결과를 통해 제안된 알고리듬이 잡음 제거 및 외곽선 보존의 우수함을 확인하였다.

• KIPS Transactions on Computer and Communication Systems
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• v.13 no.1
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• pp.1-9
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• 2024

In the development and mass production of polymers, there are many uncontrollable variables. Even small changes in chemical composition, structure, and processing conditions can lead to large variations in properties. Therefore, Traditional linear modeling techniques that assume a general environment often produce significant errors when applied to field data. In this study, we propose a new modeling method (GPR-SEM) that combines Structural Equation Modeling (SEM) and Gaussian Process Regression (GPR) to study the Friction-Coefficient and Flexural-Strength properties of Polyacetal resin, an engineering plastic, in order to meet the recent trend of using plastics in industrial drive components. And we also consider the possibility of using it for materials modeling with nonlinearity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.675-681
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• 2019

Noise removal is a pre-requisite procedure in image processing, and various methods have been studied depending on the type of noise and the environment of the image. However, for image processing with high-frequency components, conventional additive white Gaussian noise (AWGN) removal techniques are rather lacking in performance because of the blurring phenomenon induced thereby. In this paper, we propose an algorithm to minimize the blurring in AWGN removal processes. The proposed algorithm sets the high-frequency and the low-frequency component filters, respectively, depending on the pixel properties in the mask, consequently calculating the output of each filter with the addition or subtraction of the input image to the reference. The final output image is obtained by adding the weighted data calculated using the standard deviations and the Gaussian distribution with the output of the two filters. The proposed algorithm shows improved AWGN removal performance compared to the existing method, which was verified by simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.205-208
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• 2007

Due to the development and wide use of digital multimedia broadcasting (DMB) and Wireless Broadband Internet (WiBro), the digital contents industry using images has been progressed. Therefore, the image processing has been applied in a variety of fields and in order to transmit and conserve accurate information, the degradation phenomenon for images should be removed. As a representative cause of the degradation phenonenon, noise has become known and Gaussian noise occurs in the process of transmission. Diverse researches for Gaussian noise removal have been implemented and a great number of algorithms have been proposed until now. In this paper, for mage restoration an algorithm using the adaptive threshold value is proposed in Gaussian noise environment and the threshold value is established by using the histogram of edge image. And from simulation results, the noise removal performance of the proposed method is proven using mean square error (MSE) and peak signal to noise ratio (PSNR).