• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.79-86
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• 2005

In this paper, we propose a new LINF(Linear Independent Non-negative Factorization) algorithm for real-time face recognition systea This system greatly consists of the two parts: 1) face extraction part; 2) face recognition part. In the face extraction Part we applied subtraction image, the detection of eye and mouth region , and normalization method, and then in the face recognition Part we used LINF in extracted face candidate region images. The existing recognition system using only PCA(Principal Component Analysis) showed low recognition rates, and it was hard in the recognition system using only LDA(Linear Discriminants Analysis) to apply LDA directly when the training set is small. To overcome these shortcomings, we reduced dimension as the matrix that had non-negative value to be different from former eigenfaces and then applied LDA to the matrix in the proposed system We have experimented using self-organized DAIJFace database and ORL database offered by AT(')T laboratory in Cambridge, U.K. to evaluate the performance of the proposed system. The experimental results showed that the proposed method outperformed PCA, LDA, ICA(Independent Component Analysis) and PLMA(PCA-based LDA mixture algorithm) method within the framework of recognition accuracy.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.285-288
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• 2001

In this paper, we proposed new method of speech feature extraction using ICA(Independent Component Analysis) which minimized the dependency and correlation among speech signals on purpose to separate each component in the speech signal. ICA removes the repeating of data after finding the axis direction which has the greatest variance in input dimension. We verified improvement of speech recognition ability with training and recognition experiments when ICA compared with conventional mel-cepstrum features using HMM. Also, we can see that ICA dealt with the situation of recognition ability decline that is caused by environmental noise.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.292-294
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• 2003

본 논문에서는 독립성분분석(Independent Component Analysis； 이하 ICA)에서의 제안된 그레이 블록 거리 알고리즘(Grey Block Algorithm, 이하 GBD)을 이용한 영상 분류 방법을 제안한다. 이 제안된 방법은 기존의 GBD 알고리듬을 이용한 경우보다 k가 감소할 때 그 편차는 적어 좋은 영상 분류 특징을 보임을 모의 실험을 통하여 확인할 수 있었다.

• Proceedings of the Korea Contents Association Conference
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• 2003.05a
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• pp.351-355
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• 2003

Neurons in the mammalian visual cortex can be classified into the two main categories of simple cells and complex cells based on their response properties. Here, we find the complex features corresponding to the response of complex cells by applying the unsupervised independent component analysis network to input images. This result will be helpful to elucidate the information processing mechanism of neurons in primary visual cortex.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.220-223
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• 2011

Respiration signal of the vital signs is an important parameter in clinical parts. To extract the respiration signal from PPG signal for mobile healthcare system is difficult because the bands of the motion artifacts and respiration in the frequency domain are overlapped. This study to improve this problem suggested a respiration extraction method using the independent component analysis and evaluated its performances. In results of evaluation, the ICA method showed better performance than LPF suggested recently.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.505-507
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• 2002

본 논문에서는 그레이 블록 거리알고리즘(grey block algorithms, 이하 GBD)을 이용하여 독립성분분석(independent component analysis; 이하 ICA) 및 첨도(Kurtosis)에서의 영상간의 거리를 측정하여, 어느 정도 영상간의 상대적 식별을 용이하게 하여 영상 분류가 되는지 모의 실험을 통하여 확인하고자 한다. 모의 실험 결과로부터, ICA에서는 k는 8까지 상대적 식별이 되어 영상 분류가 되었고, 첨도에서는 영상간의 상대적 식별을 k가 4까지만 블록을 분할 할 수 있었다.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2674-2676
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• 2002

본 논문에서는 독립성분분석(independent component analysis; 이하 ICA)에서의 새로운 그레이 블록 거리(grey block distance; GBD, 이하 GBD)알고리즘을 이용한 영상 분류 방법을 제안한다. 이 제시된 방법은 다중해상도에서 기존의 GBD 알고리즘과 비교하여 이차원 영상간의 상대적 식별을 더 용이하게 하여 영상이 급격히 변화하는 부분의 정보를 잃지 않게 개선할 수 있었다. 모의 실험 결과로부터 기존의 GBD 알고리즘에 비하여 영상간의 상대적 식별이 더 용이하여 빨리 수렴이 되는 것을 모의 실험을 통하여 확인하였다.

• 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 KSRS Conference
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• 2003.11a
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• pp.485-487
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• 2003

Independent Component Analysis (ICA) is used to generate ICA filter for computing feature vector for image window. Filters that have high discrimination power are selected to classify image from these ICA filters. Proposed classification algorithm is based on probability distribution of feature vector.

• Progress in Medical Physics
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• v.18 no.1
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• pp.20-26
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• 2007

It is expected that synaptic construction and electrical characteristics In degenerate retina might be different from those In normal retina. Therefore, we analyzed the retinal waveform recorded with multielectrode array in normal and degenerate retina using principal component analysis (PCA) and Independent component analysis (ICA) and compared the results. PCA Is a well established method for retinal waveform while ICA has not tried for retinal waveform analysis. We programmed ICA toolbox for spatiotemporal analysis of retinal waveform. In normal mouse, the MEA spatial map shows a single hot spot perfectly matched with PCA-derived ON or OFF ganglion cell response. However In rd/rd mouse, the MEA spatial map shows numerous hot and cold spots whose underlying interactions and mechanisms need further Investigation for better understanding.