• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.239-240
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• 2008

본 논문에서는 Subtractive clustering 알고리즘을 이용한 Fuzzy Radial Basis Function Neural Network (FRBFNN)의 규칙 수를 자동적으로 생성하는 방법을 제시한다. FRBFNN은 멤버쉽 함수로써 기존 RBFNN에서 가우시안이나 타원형 형태의 특정 RBF를 사용하는 구조와 달리 Fuzzy C-Means clustering 알고리즘에서 사용하는 거리에 기한 멤버쉽 함수를 사용하여 전반부의 공간 분할 및 활성화 레벨을 결정하는 구조이다. 본 논문에서는 데이터의 밀집도에 기반을 두어 클러스터링을 하는 Subtractive clustering 알고리즘을 사용하여 퍼지 규칙의 수와 같은 의미를 갖는 분할할 입력공간의 수와 분할된 입력공간의 중심값을 동정하며, Least Square Estimator (LSE) 알고리즘을 사용하여 후반부 다항식의 계수를 추정 한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.287-290
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• 2002

본 논문에서는 디지털 컨텐츠 저작권 보호를 위해 강인성과 비가시성의 유지를 위한 보다 효과적인 방법으로 웨이브릿 변환에서 적응적 워터마크 삽입강도를 갖는 지각적 데이터 은닉 모델을 제안한다. 먼저 영상을 9/7 쌍직교 웨이브릿 필터를 사용해 4레벨로 다해상도 분해한다. 다음으로 연속부대역 양자화(successive subband quantization)를 통한 시각적 중요계수(perceptually significant coefficient: PSC)들을 선정하여 선택된 계수들에 대해서만 워터마크 정보를 삽입한다. 지각 모델은 정상상태의 일반화 가우시안 모델(generalized gaussian model)로 추정된 NVF(noise visibility function)로 에지와 텍스쳐영역 그리고 평탄영역에 따라 각각 적응적으로 삽입되게 한다. 이는 각 서브밴드 내의 분산과 형상계수(shape parameter)에 의해 결정된다. 적응적 워터마크의 삽입강도를 갖기 위해 에지와 텍스쳐영역의 삽입강도는 각 서브밴드의 주파수 감도(frequency sensitivity)로 결정되고, 평탄영역의 삽입강도는 영상의 국부적 특성에 근거한 통계적 가중치를 사용한다. 삽입되는 워터마크는 랜덤시퀀스로 N(0,1)이다. 여러 가지 공격에 대한 실험으로 제안한 방법의 비가시성과 강인성을 확인한다.

• The Journal of Information Technology
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• v.2 no.2
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• pp.127-138
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• 1999

The wavelet transform is widely used in signal processing application. In this paper, a wavelet domain adaptive algorithm(WPTNLMS) is derived and its performances are evaluated in non-stationary environment. Where the input signals are decomposed by the wavelet packet transform for the multi-resolution adaptive processing. And the NLMS is used as an adaptive algorithm in wavelet domain. The proposed technique is applied to noise cancellation of the Doppler signal which is added with white Gaussian noise.

• Smart Media Journal
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• v.1 no.2
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• pp.34-39
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• 2012

In this paper, post-processing step is presented to remove noises and develop a fully automated scheme to segment flowers in natural scene images. The scheme to segment flowers using a level set algorithm in the natural scene images produced unexpected and isolated noises because the level set relies only on the color and edge information. The experimental results shows that the proposed method successfully removes noises in the foreground and background.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.703-705
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• 2005

본 논문에서는 기울기 크기 기반의 3차원 영역확장 알고리즘을 사용하여 small animal PET(Positron Emission Tomography) 영상으로부터 종양을 분할하는 연구를 수행하였다. 픽셀 값의 범위가 다양하고 저해상도의 특성을 갖는 PET영상으로부터 대상영역을 정확하게 분할하기 위해서 전처리(preprocessing)과정으로 영상 픽셀값의 분포를 펼쳐줌으로써 영상의 가시화를 높이는 히스토그램 스트레칭(histogram stretching) 기법을 적용하고 대상영역과 픽셀값이 유사한 인접영역과의 경계를 찾기 위해 가우시안의 1차 미분 함수를 사용하여 계산된 기울기 크기(gradient magnitude) 기반의 3차원 영역확장(region growing) 알고리즘을 제안한다. 제안한 알고리즘은 영역확장의 결과에 가장 큰 영향을 미치는 적절한 동질성 기준의 선택으로 대상영역의 분할을 성공적으로 수행하여 일반적인 영역확장의 단점을 보완하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.334-337
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• 2003

본 논문에서는 멀티웨이브릿 변환영역에서 스토케스틱 모델과 지각적 동조특성을 이용한 적응 디지털 워터마크 은닉 방법을 제안한다. 워터마크는 4레벨로 분해된 멀티웨이브릿 변환영역에서 최저주파 영역과 최고주파 대역들을 제외한 중간 및 고주파 영역에, 인간 시각 시스템(human visual model ： HVS)을 이용한 JND(just noticeable difference) 특성과 NVF(noise visibility function)를 이용한 통계적 특성을 기반으로 정상상태 가우시안 모델에 따라 지각적 동조 특성을 이용하여 적응적으로 은닉된다. 실험 결과 제안한 방법에서 에지나 텍스쳐 영역에 더 강하게 삽입할 수 있었고, 평탄영역에서 보다 적응적으로 은닉할 수 있었으므로 우수한 비가시성과 강인성을 확인하였다

• Progress in Medical Physics
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• v.22 no.1
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• pp.42-51
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• 2011

Nuclear medicine images (SPECT, PET) were widely used tool for assessment of myocardial viability and perfusion. However it had difficult to define accurate myocardial infarct region. The purpose of this study was to investigate methodological approach for automatic measurement of rat myocardial infarct size using polar map with adaptive threshold. Rat myocardial infarction model was induced by ligation of the left circumflex artery. PET images were obtained after intravenous injection of 37 MBq $^{18}F$-FDG. After 60 min uptake, each animal was scanned for 20 min with ECG gating. PET data were reconstructed using ordered subset expectation maximization (OSEM) 2D. To automatically make the myocardial contour and generate polar map, we used QGS software (Cedars-Sinai Medical Center). The reference infarct size was defined by infarction area percentage of the total left myocardium using TTC staining. We used three threshold methods (predefined threshold, Otsu and Multi Gaussian mixture model; MGMM). Predefined threshold method was commonly used in other studies. We applied threshold value form 10% to 90% in step of 10%. Otsu algorithm calculated threshold with the maximum between class variance. MGMM method estimated the distribution of image intensity using multiple Gaussian mixture models (MGMM2, ${\cdots}$ MGMM5) and calculated adaptive threshold. The infarct size in polar map was calculated as the percentage of lower threshold area in polar map from the total polar map area. The measured infarct size using different threshold methods was evaluated by comparison with reference infarct size. The mean difference between with polar map defect size by predefined thresholds (20%, 30%, and 40%) and reference infarct size were $7.04{\pm}3.44%$, $3.87{\pm}2.09%$ and $2.15{\pm}2.07%$, respectively. Otsu verse reference infarct size was $3.56{\pm}4.16%$. MGMM methods verse reference infarct size was $2.29{\pm}1.94%$. The predefined threshold (30%) showed the smallest mean difference with reference infarct size. However, MGMM was more accurate than predefined threshold in under 10% reference infarct size case (MGMM: 0.006%, predefined threshold: 0.59%). In this study, we was to evaluate myocardial infarct size in polar map using multiple Gaussian mixture model. MGMM method was provide adaptive threshold in each subject and will be a useful for automatic measurement of infarct size.

• Journal of Broadcast Engineering
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• v.18 no.2
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• pp.311-318
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• 2013

This paper deals with a noise reduction algorithm which uses the binary masking in the time-frequency domain. To improve speech intelligibility in noise, noise-masked speech is decomposed into time-frequency units and mask "0" is assigned to masker-dominant region removing time-frequency units where noise is dominant compared to speech. In the previous research, Gaussian mixture models were used to classify the speech-dominant region and noise-dominant region which correspond to mask "1" and mask "0", respectively. In each frequency band, data were collected and trained to build the Gaussian mixture models and detection procedure is performed to the test data where each time-frequency unit belongs to speech-dominant region or noise-dominant region. In this paper, we consider the correlation of masks in the frequency domain and propose a post-processing method which exploits the Viterbi algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.24-29
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• 2014

Digital mammography is the most common technique for the early detection of breast cancer. To diagnose the breast cancer in early stages and treat efficiently, many image enhancement methods have been developed. This paper presents a multi-scale contrast enhancement method in the Laplacian pyramid for the digital mammogram. The proposed method decomposes the image into the contrast measures by the Gaussian and Laplacian pyramid, and the pyramid coefficients of decomposed multi-resolution image are defined as the frequency limited local contrast measures by the ratio of high frequency components and low frequency components. The decomposed pyramid coefficients are modified by the contrast measure for enhancing the contrast, and the final enhanced image is obtained by the composition process of the pyramid using the modified coefficients. The proposed method is compared with other existing methods, and demonstrated to have quantitatively good performance in the contrast measure algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.2C
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• pp.181-187
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• 2012

Millimeter wave (MMW) readily penetrates fabrics, thus it can be used to detect objects concealed under clothing. A passive MMW imaging system can operate as a stand-off type sensor that scans people in both indoors and outdoors. However, because of the diffraction limit and low signal level, the imaging system often suffers from low image quality. Therefore, suitable statistical analysis and computational processing would be required for automatic analysis of the images. In this paper, a real-time concealed object detection is addressed by means of the multi-level segmentation. The histogram of the image is modeled with a Gaussian mixture distribution, and hidden object areas are segmented by a multi-level scheme involving $k$-means, the expectation-maximization algorithm, and a decision rule. The complete algorithm has been implemented in C++ environments on a standard computer for a real-time process. Experimental and simulation results confirm that the implemented system can achieve the real-time detection of concealed objects.