• 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.

• 한국멀티미디어학회논문지
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• 제20권10호
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• pp.1655-1661
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• 2017

A high-speed directional interpolation algorithm based on the pattern of a $2{\times}2$ pixel block is proposed in this paper. The basic concept of the proposed algorithm is started from UDWT(un-decimated discrete wavelet transform), but there are no transform operations. In order to detect the direction of the edge, 4-pixel differences of two pairs in the $2{\times}2$ block are compared. The $2{\times}2$ block patterns are grouped into total 8 classes, and thereafter the directional interpolation is executed according to the type of the pattern. Since the calculation of the proposed algorithm is very simple and needs a few additions on integer data type, the computation time is almost same as that of bilinear interpolation algorithm. However, experimental results show that the output quality of the proposed one is better than those of the conventional interpolation ones in the objective quality and the computation time.

• ETRI Journal
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• 제42권1호
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• pp.78-89
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• 2020

Human activity recognition (HAR) has become effective as a computer vision tool for video surveillance systems. In this paper, a novel biometric system that can detect human activities in 3D space is proposed. In order to implement HAR, joint angles obtained using an RGB-depth sensor are used as features. Because HAR is operated in the time domain, angle information is stored using the sliding kernel method. Haar-wavelet transform (HWT) is applied to preserve the information of the features before reducing the data dimension. Dimension reduction using an averaging algorithm is also applied to decrease the computational cost, which provides faster performance while maintaining high accuracy. Before the classification, a proposed thresholding method with inverse HWT is conducted to extract the final feature set. Finally, the K-nearest neighbor (k-NN) algorithm is used to recognize the activity with respect to the given data. The method compares favorably with the results using other machine learning algorithms.

• 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.

• 대한전자공학회논문지SP
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• 제48권3호
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• pp.71-78
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• 2011

본 논문은 웨이블릿 도메인 상에서 부모와 자식 부밴드간의 비독립성에 기반한 영상 스테그분석 방법을 제안한다. 제안한 방법은 커버 영상과 비밀 메시지가 삽입된 스테고 영상에 대해 3-레벨 Haar UWT 웨이블릿 변환을 수행하여 12개의 부밴드로 분해한 후 부모와 자식 부밴드간의 통계적 의존성을 분석한다. 이러한 통계적 의존성은 비밀 메시지가 삽입된 스테고 영상의 경우 커버 영상과 상당한 차이를 보이므로 커버 및 스테고 영상을 구분하기 위한 특징으로 사용될 수 있다. 따라서 본 논문에서는 분해된 12개의 각 부모와 자식 부밴드간의 조인트 특성 함수에 대해 첫 9차의 통계적 모멘트를 추출함으로써 총 72차의 통계적 조인트 모멘트를 특징 벡터로 사용한다. 추출된 특징 벡터는 MLP(다층 퍼셉트론 신경망) 분류기에 입력되어 커버 영상과 스테고 영상을 학습하고 판별한다. 제안 방법의 성능 평가를 위해 LSB 및 SS, BSS 삽입 방법에 의한 다양한 삽입률의 스테고 영상을 사용하였으며, 실험 결과 제안한 기법은 기존의 기법에 비해 삽입 정보 유무의 검출율을 향상시킬 뿐만 아니라 판별의 정확도가 높음을 확인할 수 있었다.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1550-1552
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• 2005

In this paper, we have designed a Fingerprint Recognition System based on the Embedded LINUX. The fingerprint is captured using the AS-S2 semiconductor sensor. To extract a feature vector we transform the image of the fingerprint into a column vector. The image is row-wise filtered with the low-pass filter of the Haar wavelet. The feature vectors of the different fingerprints are compared by computing with the probabilistic neural network the distance between the target feature vector and the stored feature vectors in advance. The system implemented consists of a server PC based on the LINUX and a client based on the Embedded LINUX. The client is a Tynux box-x board using a PXA-255 CPU. The algorithm is simple and fast in computing and comparing the fingerprints.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.204-206
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• 2005

In this paper, we have designed a Fingerprint Recognition System based on the Embedded LINUX. The fingerprint is captured using the AS-S2 semiconductor sensor. To extract a feature vector we transform the image of t10he fingerprint into a column vector. The image is row-wise filtered with the low-pass filter of the Haar wavelet. The feature vectors of the different fingerprints are compared by computing with the probabilistic neural network the distance between the target feature vector and the stored feature vectors in advance. The system implemented consists of a server PC based on the LINUX and a client based on the Embedded LINUX. The client is a Tynux box-x board using a PXA-255 CPU. The algorithm is simple and fast in computing and comparing the fingerprints.

• 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.

• 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.