• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.851-854
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• 2004

본 논문에서는 독립성분분석(Independent Component Analysis, 이하 ICA)기법과 Kurtosis에서의 제안된 GBD 알고리즘을 이용한 영상 분류 방법을 제안한다. 이 제시된 방법은 기존의 GBD 알고리즘과 비교해서 영상이 급격히 변화하는 부분의 정보를 잃지 않게 개선할 수 있었다. 모의실험 결과로부터 제안된 GBD 알고리즘을 적용하여 영상을 분류할 때 편차가 줄어들어 영상간의 상대적 식별을 용이하게 하여 빨리 수렴이 되는 것을 모의실험을 통하여 확인 할 수 있었다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.320-324
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• 2002

본 연구에서는 모멘트와 뉴우턴법 및 모멘트와 할선법에 각각 기초한 고정점 알고리즘의 신경망 기반 독립성분분석 기법을 제안하였다. 여기서 뉴우턴법과 할선법은 각각 엔트로피에 기초한 목적함수의 근을 구하는 근사화 방법으로 빠른 경신을 위함이고, 모멘트는 근사화에 의한 역혼합행렬의 경신과정에서 발생하는 발진을 줄여 좀 더 빠른 학습을 위함이다. 제안된 기법을 256×256 픽셀(pixel)의 8개 지문영상으로부터 임의의 혼합행렬에 따라 발생되는 영상들을 각각 대상으로 시뮬레이션 한 결과, 모멘트와 할선법에 기초한 알고리즘이 모멘트와 뉴우턴에 기초한 알고리즘보다 우수한 분리성능과 빠른 학습속도가 있음을 확인하였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.210-213
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• 2003

본 연구에서는 뉴우턴법의 고정점 알고리즘에 적응 조정이 가능한 학습파라미터를 이용한 신경망 기반 독립성분분석기법을 제안하였다. 이는 고정점 알고리즘의 1차 미분을 이용하는 뉴우턴법에서 역혼합행렬의 경신 상태에 따라 학습율과 모멘트가 적응조정되도록 함으로써 분리속도와 분리성능을 개선시키기 위함이다. 제안된 기법을 512$\times$512 픽셀의 10개 영상으로부터 임의의 혼합행렬에 따라 발생되는 영상들의 분리에 적용한 결과, 기존의 고정점 알고리즘에 의한 결과보다 우수한 분리성능과 빠른 분리속도가 있음을 확인하였다.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.7-14
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• 2006

This paper presents an efficient face recognition method using both first moment of image and basis images. First moment which is a method for finding centroid of image, is applied to exclude the needless backgrounds in the face recognitions by shifting to the centroid of face image. Basis images which are the face features, are respectively extracted by principal component analysis(PCA) and fixed-point independent component analysis(FP-ICA). This is to improve the recognition performance by excluding the redundancy considering to second- and higher-order statistics of face image. The proposed methods has been applied to the problem for recognizing the 48 face images(12 persons*4 scenes) of 64*64 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed methods has a superior recognition performances(speed, rate) than conventional PCA and FP-ICA without preprocessing, the proposed FP-ICA has also better performance than the proposed PCA. The city-block has been relatively achieved more an accurate similarity than Euclidean or negative angle.

• The Korean Journal of Nuclear Medicine
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• v.38 no.6
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• pp.486-491
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• 2004

Purpose: factor analysis and independent component analysis (ICA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial $H_2^{15}O$ PET data. In this study, we quantified patients' blood flow using the ensemble ICA method. Materials and Methods: Twenty subjects underwent $H_2^{15}O$ PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of $555{\sim}740$ MBq $H_2^{15}O$. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. Results: Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was $1.2{\pm}0.40$ ml/min/g in rest, $1.85{\pm}1.12$ ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1:2.7 in average. Perfusion reserve was significantly different between the regions with and without stenosis detected by the coronary angiography (P<0.01). In 66 segment with stenosis confirmed by angiography, the segments with reversible perfusion decrease in perfusion SPECT showed lower perfusion reserve values in $H_2^{15}O$ PET. Conclusions: Myocardial blood flow could be estimated using an ICA method with ensemble learning. We suggest that the ensemble ICA incorporating non-negative constraint is a feasible method to handle dynamic image sequence obtained by the nuclear medicine techniques.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.515-518
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• 2001

본 논문에서는 전처리단계로 영평균 정규화 기법과 주요성분분석 기법을 도입하여 다층신경망을 이용한 고신뢰성의 회귀분석 모델을 제안한다. 영평균 정규화 기법은 데이터의 1차적 통계성을 고려하여 알고리즘을 간략화시키며, 주요성분분석 기법은 입력 데이터의 2차적 통계성을 고려하여 독립인 특징들의 집합으로 변환시켜 학습데이터의 차원을 감소시킬 수 있어 고차원의 학습데이터에 따른 회귀분석 모델의 제약을 해결할 수 있었다. 제안된 기법의 신경망을 3개의 독립변수를 가진 암모니아 제조공정문제와 10개의 독립변수를 가진 자동차 연비문제에 각각 적용하여 시뮬레이션한 결과, 단순정규화나 PCA를 적용하지 않는 경우보다 제안된 기법의 학습속도와 회귀성능이 더욱 더 우수함을 확인할 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.29 no.4
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• pp.270-277
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• 2010

In this paper, we proposed an algorithm that improves tone quality of noisy audio signals by using ICA(Independent Component Analysis) algorithm and perceptual filters. Many algorithms have been proposed to eliminate the noise from the audio signals, such as spectral subtraction method, perceptual filter, etc. The perceptual filter uses a noise that is acquired from silent ranges in the input signal. In this case, the improvement rate of tone quality decreases if the noise energy is changed by the environmental variation in a signal frame. But the proposed method estimates a noise that is changed at each frame using ICA algorithm. The estimated noise is applied to perceptual filter. To show the performance of the proposed algorithm, several tests are performed to various input signals. With the proposed algorithm, we could confirm the enhancement of tone quality in terms of segmental SNR (SSNR), noise-to-mask ratio (NMR) and Degradation Category Rating (DCR) test.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.210-218
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• 2002

This paper proposes an efficient separation method of the composite images by using independent component analysis(ICA) based on neural networks of the approximate learning algorithm. The Proposed learning algorithm is the fixed point(FP) algorithm based on Secant method which can be approximately computed by only the values of function for estimating the root of objective function for optimizing entropy. The secant method is an alternative of the Newton method which is essential to differentiate the function for estimating the root. It can achieve a superior property of the FP algorithm for ICA due to simplify the composite computation of differential process. The proposed algorithm has been applied to the composite signals and image generated by random mixing matrix in the 4 signal of 500-sample and the 10 images of $512{\times}512-pixel$, respectively The simulation results show that the proposed algorithm has better performance of the learning speed and the separation than those using the conventional algorithm based method. It also solved the training performances depending on initial points setting and the nonrealistic learning time for separating the large size image by using the conventional algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.2
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• pp.10-20
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• 2002

Various blind source separation (BSS) and independent component analysis (ICA) algorithms have been developed. However, comparison study for BSS/ICA algorithms has not been extensively carried out yet. The main objective of this paper is to compare various promising BSS/ICA algorithms in terms of several factors such as robustness to sensor noise, computational complexity, the conditioning of the mixing matrix, the number of sensors, and the number of training patterns. We propose several benchmarks which are useful for the evaluation of the algorithm. This comparison study will be useful for real-world applications, especially EEG/MEG analysis and separation of miked speech signals.

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.447-454
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• 2007

TFT-LCD(Thin Film transistor liquid crystal display) has become actively used front panel display technology with increasing market. Intrinsically there is region of non uniformity with low contrast that to human eye is perceived as defect. As the gray level difference between the defect and the background is hardly distinguishable, conventional thresholding and edge detection techniques cannot be applied to detect the defect. Between the patterned and un-patterned LCD defects, this paper deals with un-patterned LCD defects by using independent component analysis, adaptive thresholding and skewness. Our method showed strong results even on noised LCD images and worked successfully on the manufacturing line.