• Journal of the Optical Society of Korea
• /
• 제12권3호
• /
• pp.121-125
• /
• 2008

We address the problem of Blind Source Separation(BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis(ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images(two-dimensional signals).

• 한국통계학회:학술대회논문집
• /
• 한국통계학회 2004년도 학술발표논문집
• /
• pp.79-84
• /
• 2004

We use principal component analysis (PCA) to identify exons of a gene and further analyze their internal structures. The PCA is conducted on the short-time Fourier transform (STFT) based on the 64 codon sequences and the 4 nucleotide sequences. By comparing to independent component analysis (ICA), we can differentiate between the exon and intron regions, and how they are correlated in terms of the square magnitudes of STFTs. The experiment is done on the gene F56F11.4 in the chromosome III of C. elegans. For this data, the nucleotide based PCA identifies the exon and intron regions clearly. The codon based PCA reveals a weak internal structure in some exon regions, but not the others. The result of ICA shows that the nucleotides thymine (T) and guanine (G) have almost all the information of the exon and intron regions for this data. We hypothesize the existence of complex exon structures that deserve more detailed analysis.

• 대한의용생체공학회:의공학회지
• /
• 제30권5호
• /
• pp.373-380
• /
• 2009

In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2001년도 하계학술대회 논문집 D
• /
• pp.2810-2812
• /
• 2001

본 논문에서는 독립성분해석을 이용한 영상분리에 있어서의 잡음에 대한 강인성에 대한 주성분해석과 비교 연구를 함으로써, 독립성분해석(Independent Component Analysis, ICA)기법의 효율성을 고찰하고 분석하고자 한다. 원래의 인식 시스템 모델에 잡음을 주었을 때, ICA를 이용한 영상 분리의 잡음에 대한 강인성은 주성분 해석(Principal Component Analysis, PCA)기법에서 보다 더 잡음에 강인한 성질을 내포하고 있는데, 이는 PCA 보다 ICA가 분리하려는 영상정보의 상호관계를 더 약화시키는 작용을 하기 때문이다. 이러한 특성은 모의실험을 통해 확인되었다.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2004년도 춘계학술발표대회
• /
• pp.809-812
• /
• 2004

본 논문에서는 다중해상도에서 기존의 그레이 블록 거리(grey block distance; GBD, 이하 GBD)알고리즘과 비교하여 이차원 영상간의 상대적 식별을 더 용이하게 하기 위한 새로운 GBD 알고리즘 방법을 제안한다. 이 제시된 방법은 다중해상도에서 기존의 GBD 알고리즘과 비교해서 영상이 급격히 변화하는 부분의 정보를 잃지 않게 개선할 수 있었다. 모의 실험 예로서 주성분분석(principal component analysis; 이하 PCA)기법과 독립성분분석(independent component analysis; 이하 ICA)기법을 적용하여 유용성과 제안된 방법이 이전의 연구보다 k가 감소할 때 편차는 줄어들어 좋은 영상 분류 특징을 보였으며, ICA가 PCA에 비하여 영상간의 상대적 식별을 용이하게 하여 빨리 수렴이 되는 것을 모의 실험을 통하여 확인하였다.

• 한국콘텐츠학회논문지
• /
• 제3권2호
• /
• pp.19-23
• /
• 2003

포유류 동물의 시각피질 세포에 나타나는 특징은 크게 단순특징을 추출하는 simple cell과 복잡한 특징에 반응하는 complex cell로 구분된다. 특히, 하위 계층의 세포들은 단순특징을 추출하며, 상위 계층으로 갈수록 복합특징을 추출하는 세포들이 존재한다. 이 연구에서는 입력영상에 독립성분분석을 적용하여 complex cell에 대응하는 복잡한한 특징을 추출하였다. 이 결과는 시각피질 세포의 정보처리에 대한 방식에 대한 이해를 기반으로 시각정보처리 알고리즘을 개발하는 데 기여할 것이다.

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
• /
• pp.305-309
• /
• 2000

In this paper, we presents a new methodology for face recognition after analysing conventional ICA(Independent Component Analysis) based approach. In the literature we found that ICA based methods have followed the same procedure without any exception, first PCA(Principal Component Analysis) has been used for feature extraction, next ICA learning method has been applied for feature enhancement in the reduced dimension. However, it is contradiction that features are extracted using higher order moments depend on variance, the second order statistics. It is not considered that a necessary component can be located in the discarded feature space. In the new methodology, features are extracted using the magnitude of kurtosis(4-th order central moment or cumulant). This corresponds to the PCA based feature extraction using eigenvalue(2nd order central moment or variance). The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. ICA methodology is analysed using SVD(Singular Value Decomposition). PCA does whitening and noise reduction. ICA performs the feature extraction. Simulation results show the effectiveness of the methodology compared to the conventional ICA approach.

• IEIE Transactions on Smart Processing and Computing
• /
• 제4권5호
• /
• pp.311-317
• /
• 2015

The aim of this study is to develop a 3D registration algorithm for positron emission tomography/computed tomography (PET/CT) and magnetic resonance (MR) images acquired from independent PET/CT and MR imaging systems. Combined PET/CT images provide anatomic and functional information, and MR images have high resolution for soft tissue. With the registration technique, the strengths of each modality image can be combined to achieve higher performance in diagnosis and radiotherapy planning. The proposed method consists of two stages: normalized mutual information (NMI)-based global matching and independent component analysis (ICA)-based refinement. In global matching, the field of view of the CT and MR images are adjusted to the same size in the preprocessing step. Then, the target image is geometrically transformed, and the similarities between the two images are measured with NMI. The optimization step updates the transformation parameters to efficiently find the best matched parameter set. In the refinement stage, ICA planes from the windowed image slices are extracted and the similarity between the images is measured to determine the transformation parameters of the control points. B-spline. based freeform deformation is performed for the geometric transformation. The results show good agreement between PET/CT and MR images.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제53권4호
• /
• pp.293-299
• /
• 2004

This paper is about the extraction of basis function for ECG signal processing. In the first step, it is assumed that ECG signal consists of linearly mixed independent source signals. 12 channel ECG signals, which were sampled at 600sps, were used and the basis function, which can separate and detect source signals - QRS complex, P and T waves, - was found by applying the fast fixed point algorithm, which is one of learning algorithms in independent component analysis(ICA). The possibilities of significant point detection and classification of normal and abnormal ECG, using the basis function, were suggested. Finally, the proposed method showed that it could overcome the difficulty in separating specific frequency in ECG signal processing by wavelet transform. And, it was found that independent component analysis(ICA) could be applied to ECG signal processing for detection of significant points and classification of abnormal beats.

• ETRI Journal
• /
• 제35권2호
• /
• pp.193-199
• /
• 2013

This paper addresses nonnegative independent component analysis (NICA), with the aim to realize the blind separation of nonnegative well-grounded independent source signals, which arises in many practical applications but is hardly ever explored. Recently, Bertrand and Moonen presented a multiplicative NICA (M-NICA) algorithm using multiplicative update and subspace projection. Based on the principle of the mutual correlation minimization, we propose another novel cost function to evaluate the diagonalization level of the correlation matrix, and apply the multiplicative exponentiated gradient (EG) descent update to it to maintain nonnegativity. An efficient approach referred to as the EG-NICA algorithm is derived and its validity is confirmed by numerous simulations conducted on different types of source signals. Results show that the separation performance of the proposed EG-NICA algorithm is superior to that of the previous M-NICA algorithm, with a better unmixing accuracy. In addition, its convergence speed is adjustable by an appropriate user-defined learning rate.