• 대한전기학회논문지:시스템및제어부문D
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• 제49권7호
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• pp.389-398
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• 2000

The popular methods to check the identity of individuals include passwords and ID cards. These conventional method for user identification and authentication are not altogether reliable because they can be stolen and forgotten. As an alternative of the existing methods, biometric technology has been paid much attention for the last few decades. In this paper, we propose an efficient system for recognizing the identity of a living person by analyzing iris patterns which have a high level of stability and distinctiveness than other biometric measurements. The proposed system is based on wavelet transform and a competitive neural network with the improved mechanisms. After preprocessing the iris data acquired through a CCD camera, feature vectors are extracted by using Haar wavelet transform. LVQ(Learning Vector Quantization) is exploited to classify these feature vectors. We improve the overall performance of the proposed system by optimizing the size of feature vectors and by introducing an efficient initialization of the weight vectors and a new method for determining the winner in order to increase the recognition accuracy of LVQ. From the experiments, we confirmed that the proposed system has a great potential of being applied to real applications in an efficient and effective way.

• 대한원격탐사학회지
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• 제24권5호
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• pp.437-444
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have tried to enhance classification accuracy. Previous studies have shown that the classification technique based on wavelet transform is more effective than traditional techniques based on original pixel values, especially in complicated imagery. Various basis functions such as Haar, daubechies, coiflets and symlets are mainly used in 20 image processing based on wavelet transform. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we first computed the wavelet coefficients of satellite image using ten different basis functions, and then classified images. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis functions. The energy parameters of wavelet detail bands and overall accuracy are clearly correlated. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• 한국멀티미디어학회논문지
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• 제25권10호
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• pp.1395-1403
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• 2022

Lately, over 4K high definition and high dynamic range (HDR) display devices are popularized, various interpolation and HDR methods have been researched to expand the size and the dynamic range. Since most of the legacy low resolution (LR) images require both an interpolation and a HDR tone mapping methods, the two processes should be subsequently applied. Therefore, the proposed algorithm presents a HDR up-scaling algorithm using undecimated wavelet transform and Retinex method, which transfers a LR image of low dynamic range (LDR) into the high resolution (HR) with HDR. The proposed algorithm consists of an up-scaling scheme increasing the image size and a tone mapping scheme expanding the dynamic range. The up-scaling scheme uses the undecimated version of the simplest Haar wavelet analysis for the 8-directional interpolation and the change region is extracted during the analysis. This region information is utilized in controlling the surround functions' size of the proposed tone mapping using MSRCR, to enhance the pixels of around the edges that are dominant feature of the subjective image quality. As the results, the proposed algorithm can apply an up-scaling and tone mapping processes in accordance with the type of pixel.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.169-172
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• 2008

Land-use or land-cover classification of satellite images is one of the important tasks in remote sensing application and many researchers have been tried to enhance classification accuracy. Previous studies show that the classification technique based on wavelet transform is more effective than that of traditional techniques based on original pixel values, especially in complicated imagery. Various wavelets can be used in wavelet transform. Wavelets are used as basis functions in representing other functions, like sinusoidal function in Fourier analysis. In these days, some basis functions such as Haar, Daubechies, Coiflets and Symlets are mainly used in 2D image processing. Selecting adequate wavelet is very important because different results could be obtained according to the type of basis function in classification. However, it is not easy to choose the basis function which is effective to improve classification accuracy. In this study, we computed the wavelet coefficients of satellite image using 10 different basis functions, and then classified test image. After evaluating classification results, we tried to ascertain which basis function is the most effective for image classification. We also tried to see if the optimum basis function is decided by energy parameter before classifying the image using all basis function. The energy parameter of signal is the sum of the squares of wavelet coefficients. The energy parameter is calculated by sub-bands after the wavelet decomposition and the energy parameter of each sub-band can be a favorable feature of texture. The decision of optimum basis function using energy parameter in the wavelet based image classification is expected to be helpful for saving time and improving classification accuracy effectively.

• 한국정보통신학회논문지
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• 제25권7호
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• pp.896-902
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• 2021

회전된 얼굴 검출은 많은 응용 분야에서 필요하지만 회전에 따른 얼굴 모양의 큰 변화로 인해 여전히 어려운 분야이다. 이 논문에서는 회전의 영향을 받지 않는 극좌표 변환 방법과 변환된 영상을 이용하여 회전얼굴을 효과적으로 검출하는 방법이 제안되었다. 제안한 극좌표계 변환 방법은 회전 각도와 무관하게 눈, 입 등과 같은 얼굴 구성 요소들의 위치가 항상 유지되기 때문에 얼굴 구성요소들 간의 공간 정보가 유지되며, 이로 인해 회전 효과가 제거된다. 극좌표계 변환된 영상을 정면 얼굴 검출에 사용되는 AdaBoost를 이용하여 학습하고 회전 얼굴을 검출하였다. 비얼굴 영상을 LBP를 이용하여 학습하고 검출한 얼굴을 검증하였다. BioID 데이터베이스에 있는 영상을 회전하여 얻은 3600개 얼굴영상에 대한 실험 결과 96.17%의 회전얼굴 검출률을 얻었다. 또한, 다수의 회전 얼굴이 포함된 배경이 있는 영상에서 회전 얼굴들을 정확하게 검출하였다.

• 한국음향학회지
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• 제16권4호
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• pp.64-70
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• 1997

본 논문은 MPEG의 서브밴드 필터링을 웨이블렛 변환으로 대체하였을 때 얼마만큼의 계산량이 감소하는 가를 연구하였다. MPEG 오디오에서는 16비트 PCM 오디오 데이타를 입력으로 받아 서브밴드 필터링후 서브밴드 샘플을 양자화하여 전송하는 것을 기본으로 한다. MPEG의 서브밴드 필터링의 경우 32 대역의 등간격으로 분할한다. 이 경우 32개의 필터가 필요하고 각 필터는 512의 길이를 갖는 필터를 사용한다. 본 연구에서는 분할 대역을 6개로 하고 웨이블렛 필터중 가장 짧은 Haar 필터를 사용하였다. 제안된 시스템은 256kbps 이상의 전송율에서는 MPEG 오디오와 비슷한 수준의 CD 음질을 유지하였으며, 계산량 비교결과 부호화기는 약 40%, 복호화기는 약 70%의 감소를 보였다.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2004년도 추계 종합학술대회 논문집
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• pp.348-351
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• 2004

본 논문은 Haar 웨이브릿 변환과 2D PCA를 이용한 얼굴 인식 방법에 대하여 제안한다. 기존의 PCA는 1 차원 벡터들로 공분산 행렬을 구하는 반면에 2D PCA는 2 차원 영상을 직접적으로 이용하여 공분산 행렬을 구한 후 그것의 고유값에 따른 고유벡터를 구하여 특징 벡터들을 추출하였다. 제안 방법은 얼굴 데이터를 낮은 차원과 강건한 특징을 가지는 얼굴 영상을 얻기 위해 웨이브릿 변환을 이용하여 LL 대역의 영상 데이터로 2D PCA 방법을 적용하여 얼굴을 인식한다. 실험결과는 원래 크기의 얼굴 영상에 2D PCA를 적용한 인식률보다 웨이브릿 변환의 LL 대역의 얼굴 영상에 2D PCA를 적용한 얼굴 인식률이 더 좋음을 보여준다.

• Journal of Positioning, Navigation, and Timing
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• 제8권4호
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• pp.165-173
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• 2019

In this paper, we apply a discrete wavelet packet transform (DWPT) to reduce the influence of interference in global positioning system (GPS) signals and compare the interference mitigation performance of various wavelets. By applying DWPT to the received signal, we can gradually divide the received signal band into low-pass and high-pass bands. After calculating the average power for the separate bands, we can determine whether there is interference by comparing the value with the given threshold. For a band that includes interference, we can reconstruct the whole band signal using inverse DWPT (IDWPT) after applying a nulling method that sets all of the wavelet coefficients to 0. The reconstructed signals are correlated with the pseudorandom noise (PRN) codes to acquire GPS signals. The performance evaluation is based on the number of satellite signals whose peak ratio (defined as the ratio of the first and second correlation peak values in the acquisition stage) exceeds the threshold. In this paper, we compare and evaluate the performance of 6 wavelets including Haar, Daubechies, Symlets, Coiflets, Biorthogonal Splines, and Discrete Meyer.

• 한국멀티미디어학회논문지
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• 제17권12호
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• pp.1393-1401
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• 2014

This paper presents an adaptive image interpolation method using a pixel-based neighbor embedding which is modified from the patch-based neighbor embedding of contemporary super resolution algorithms. Conventional interpolation methods for high resolution detect at least 16-directional edges in order to remove zig-zaging effects and selectively choose the interpolation strategy according to the direction and value of edge. Thus, they require much computation and high complexity. In order to develop a simple interpolation method preserving edge's directional shape, the proposed algorithm adopts the simplest Haar wavelet and suggests a new pixel-based embedding scheme. First, the low-quality image but high resolution, magnified into 1 octave above, is acquired using an adaptive 8-directional interpolation based on the high frequency coefficients of the wavelet transform. Thereafter, the pixel embedding process updates a high resolution pixel of the magnified image with the weighted sum of the best matched pixel value, which is searched at its low resolution image. As the results, the proposed scheme is simple and removes zig-zaging effects without any additional process.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.99-102
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• 1997

A neural spike sorting technique has been developed that also has the capability of template learning. A system of software has been written that first obtains the templates by learning, and then performs the sorting of the spikes into single units. The spike sorting can be done in real time. The template learning consists of spike detection based on the discrete Haar transform (DHT), feature extraction by clustering of spike amplitude and duration, classification based on rms error, and fabrication of templates. The developed algorithms can be implemented into real time systems using digital signal processors.