• Journal of Korea Multimedia Society
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• v.12 no.12
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• pp.1755-1760
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• 2009

In this research, for the first time, we tried to analyse Raman hyperspectral dentin data using Independent Component Analysis (ICA) to see its possibility of adoption for the dental analysis software. We captured hyperspectral dentin data on 569 spots on a molar with dental lesion by HR800 Micro Raman Spectrometer at UMKC-CRISP (University of Missouri at Kansas City-Center for Research on Interfacial Structure and Properties). Each spot has 1,005 hyperspectral data. We applied ICA to the captured hyperspectral data of dentin for evaluating ICA approach, and compared it with the well known multivariate analysis method, PCA. As a result of the experiment, ICA approach shows better local characteristic of dentin than the result of PCA. We confirmed that ICA also could be a good method along with PCA in the dental analysis software.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.873-876
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• 2000

The information-theoretic approach to face recognition is based on the compact coding where face images are decomposed into a small set of basis images. Most popular method for the compact coding may be the principal component analysis (PCA) which eigenface methods are based on. PCA based methods exploit only second-order statistical structure of the data, so higher- order statistical dependencies among pixels are not considered. Independent component analysis (ICA) is a signal processing technique whose goal is to express a set of random variables as linear combinations of statistically independent component variables. ICA exploits high-order statistical structure of the data that contains important information. In this paper we employ the ICA for the efficient feature extraction from face images and show that ICA outperforms the PCA in the task of face recognition. Experimental results using a simple nearest classifier and multi layer perceptron (MLP) are presented to illustrate the performance of the proposed method.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.499-503
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• 2008

In this paper, a novel human activity recognition method is proposed which utilizes independent components of activity shape information from image sequences and Hidden Markov Model (HMM) for recognition. Activities are represented by feature vectors from Independent Component Analysis (ICA) on video images, and based on these features; recognition is achieved by trained HMMs of activities. Our recognition performance has been compared to the conventional method where Principle Component Analysis (PCA) is typically used to derive activity shape features. Our results show that superior recognition is achieved with our proposed method especially for activities (e.g., skipping) that cannot be easily recognized by the conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2435-2453
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• 2015

Independent component analysis (ICA) is a signal processing technique used for un-mixing of the mixed recorded signals. In wireless communication, ICA is mainly used in multiple input multiple output (MIMO) systems. Most of the existing work regarding the ICA applications in MIMO systems assumed static or quasi static wireless channels. Performance of the ICA algorithms degrades in case of time varying wireless channels and is further degraded if the data block lengths are reduced to get the quasi stationarity. In this paper, we propose an ICA based MIMO transceiver that performs well in time varying wireless channels, even for smaller data blocks. Simulation is performed over quadrature amplitude modulated (QAM) signals. Results show that the proposed transceiver system outperforms the existing MIMO system utilizing the FastICA and the OBAICA algorithms in both the transceiver systems for time varying wireless channels. Performance improvement is observed for different data blocks lengths and signal to noise ratios (SNRs).

• The Journal of the Acoustical Society of Korea
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• v.38 no.2
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• pp.177-183
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• 2019

In this paper, we proposed an application method of ICA (Independent Component Analysis) to passive line array sonar to separate interferences from target signals in low frequency band and compared performance of three conventional ICA algorithms. Since the low frequency signals are received through larger bearing angles than other frequency bands, neighboring beam signals can be used to perform ICA as measurement signals of the ICA. We use three ICA algorithms such as Fast ICA, NNMF (Non-negative Matrix Factorization) and JADE (Joint Approximation Diagonalization of Eigen-matrices). Through experiments on real data obtained from passive line array sonar, it is verified that the interference can be separable from target signals by the suggested method and the JADE algorithm shows the best separation performance among the three algorithms.

• Journal of the Korea Computer Industry Society
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• v.5 no.2
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• pp.269-274
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• 2004

Noisy speech recognition is one of most important problems in speech recognition. In this paper, a method which efficiently removes the mixed noise with speech, is proposed. The proposed method is based on the ICA to separate the mixed noise. ICA(Independent component analysis) is a signal processing technique, whose goal is to express a set of random variables as linear combinations of components that are statistically as independent from each other as possible.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.96.2-96
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• 2002

In this paper, a new method for the mixed image separation is presented using the independent component analysis, the innovation process, and the expectation-maximization. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing scheme that represents the information from observations as a set of random variables in the form of linear combinations of another statistically independent component variables. In various useful applications, ICA provides a more meaningful representation of the data than the principal component analysis through the transformation of the data to be quasi-orthogonal to each other, which can be utilized in linear p...

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.470-472
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• 1999

Sparse coding은 최소한의 active한 (non-orthogonal) basis vector를 이용하여 데이터를 표시하는 하나의 방법이다. Sparse coding에서 basis coefficient들이 statistically independent 하다는 constraint를 주기에 sparse coding은 independent component analysis(ICA)와 밀접한 관계를 가지고 있다. 본 논문에서는 sparse representation을 위하여 super-Gaussian prior를 이용한 ICA, 즉 sparse ICA 방법을 제시한다. Sparse ICA 방법을 이용하여 natural scenes의 basis vector를 찾고 이와 sparse coding과의 관계를 고찰한다. 여러 가지 super-Gaussian prior들을 고려하지 않고 이들이 ICA에 미치는 영향에 대해 살펴본다.

• Journal of the Korean Society of Industry Convergence
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• v.8 no.3
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• pp.143-148
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• 2005

This paper presents a hybrid method for recognizing the faces by using principal component analysis(PCA) and fixed-point independent component analysis(FP-ICA). PCA is used to whiten the data, which reduces the effects of second-order statistics to the nonlinearities. FP-ICA is applied to extract the statistically independent features of face image. The proposed method has been applied to the problems for recognizing the 20 face images(10 persons * 2 scenes) of 324*243 pixels from Yale face database. 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 method has a superior recognition performances(speed, rate). The negative angle has been relatively achieved more an accurate similarity than city-block or Euclidean.

• Journal of KIEE
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• v.11 no.1
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• pp.1-7
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• 2001

Various methods for blind source separation (BSS) are based on independent component analysis (ICA) which can be viewed as a nonlinear extension of principal component analysis (PCA). Most existing ICA methods require certain nonlinear functions (which leads to higher-order statistics) depending on the probability distributions of sources, whereas PCA is a linear learning method based on second-order statistics. In this paper we show that the PCA can be applied to the task of BBS, provided that source are spatially uncorrelated but temporally correlated. Since the resulting method is based on only second-order statistics, it avoids the nonlinear function and is able to separate mixtures of several colored Gaussian sources, in contrast to the conventional ICA methods.