• Journal of the Korea Computer Graphics Society
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• v.12 no.2
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• pp.1-7
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• 2006

블렌드 쉐입 기반 얼굴 애니메이션을 위해 기저 모델(Expression basis)을 생성하는 방법을 크게 두 가지로 구분하면, 애니메이터가 직접 모델링을 하여 생성하는 방법과 통계적 방법에 기초하여 모델링하는 방법이 있다. 그 중 애니메이터에 의한 수동 모델링 방법으로 생성된 기저 모델은 직관적으로 표정을 인식할 수 있다는 장점으로 인해 전통적인 키프레임 제어가 가능하다. 하지만, 표정 공간(Expression Space)의 일부분만을 커버하기 때문에 모션데이터로부터의 재복원 과정에서 많은 오차를 가지게 된다. 반면, 통계적 방법을 기반으로 한 기저모델 생성 방법은 거의 모든 표정공간을 커버하는 고유 얼굴 모델(Eigen Faces)을 생성하므로 재복원 과정에서 최소의 오차를 가지지만, 시각적으로 직관적이지 않은 표정 모델을 만들어 낸다. 따라서 본 논문에서는 수동으로 생성한 기저모델을 유사 고유 얼굴 모델(Quasi-Eigen Faces)로 변형하는 방법을 제시하고자 한다. 결과로 생성되는 기저 모델은 시각적으로 직관적인 얼굴 표정을 유지하면서도 통계적 방법에 의한 얼굴표정 공간의 커버 영역과 유사하도록 확장할 수 있다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1211-1222
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• 2011

It is necessary to use prestressed modal analysis to calculate the modal frequencies and mode shapes of a prestressed structure such as a spinning blade, a preloaded structure, or a thermally deformed pipe, because the prestress effect sometimes causes significant changes in the frequencies and mode shapes. When the finite element model under consideration has a very large number of degrees of freedom, repeated prestressed modal analyses for investigating the prestress effects might become too computationally expensive to finish within a reasonable design-process time. To alleviate these computational difficulties, a Krylov subspace-based model order reduction, which reduces the number of degrees of freedom of the original finite element model and speeds up the necessary prestressed modal analysis with the reduced order models (ROMs), is presented. The numerical process for the moment-matching model reduction is performed directly on the full order models (FOMs) (modeled in ANSYS) by the Arnoldi process. To demonstrate the advantages of this approach for performing prestressed modal analysis, the prestressed wheel and the compressor impeller under their high-speed rotation are considered as examples.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4
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• pp.210-216
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• 2005

We Propose a new feature normalization scheme based on eigenspace for achieving robust speech recognition. In general, mean and variance normalization (MVN) is Performed in cepstral domain. However, another MVN approach using eigenspace was recently introduced. in that the eigenspace normalization Procedure Performs normalization in a single eigenspace. This Procedure consists of linear PCA matrix feature transformation followed by mean and variance normalization of the transformed cepstral feature. In this method. 39 dimensional feature distribution is represented using only a single eigenspace. However it is observed to be insufficient to represent all data distribution using only a sin91e eigenvector. For more specific representation. we apply unique na independent eigenspaces to cepstra, delta and delta-delta cepstra respectively in this Paper. We also normalize training data in eigenspace and get the model from the normalized training data. Finally. a feature space rotation procedure is introduced to reduce the mismatch of training and test data distribution in noisy condition. As a result, we obtained a substantial recognition improvement over the basic eigenspace normalization.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1475-1479
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• 2004

High dimensional data requires efficient feature extraction techliques. Though PCA(Principal Component Analysis) is a famous feature extraction method it requires huge memory space and computational cost is high. In this paper we use incremental PCA for feature extraction on high dimensional data. Through experiment we show that proposed method is superior to APEX model.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.67-76
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• 2008

Reliable face recognition under various illumination environments is essential for successful commercialization. Feature-based face recognition relies on a good choice of feature vectors. Gabor feature vectors are known to be more robust to variations of pose and illumination than any other feature vectors so that they are popularly adopted for face recognition. However, they are not completely independent of illuminations. In this paper, we propose an illumination-robust face recognition method based on the Gabor feature vector illumination PCA model. We first construct the Gabor feature vector illumination PCA model where Gator feature vector space is rendered to be decomposed into two orthogonal illumination subspace and face identity subspace. Since the Gabor feature vectors obtained by projection into the face identity subspace are separated from illumination, the face recognition utilizing them becomes more robust to illumination. Through experiments, it is shown that the proposed face recognition based on Gabor feature vector illumination PCA model performs more reliably under various illumination and Pose environments.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.18-25
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• 2011

In a manufacturing process of metal coins, surface defects of coins are manually detected. This paper describes an new method for detecting surface defects of metal coins on a moving conveyor belt using image processing. This method consists of multiple procedures: segmentation of a coin from the background, alignment of the coin to the model, projection of the aligned coin to the best eigen image space, and detection of defects by comparison of the projection error with an adaptive threshold. In these procedures, the alignement and the projection are newly developed in this paper for the detection of coin surface defects. For alignment, we use the histogram of the segmented coin, which converts two-dimensional image alignment to one-dimensional alignment. The projection reduces the intensity variation of the coin image caused by illumination and coin rotation change. For projection, we build multiple eigen image spaces and choose the best eigen space using estimated coin direction. Since each eigen space consists of a small number of eigen image vectors, we can implement the projection in real- time.

• The Journal of the Korea Contents Association
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• v.9 no.2
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• pp.115-124
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• 2009

The popular eigenfaces-based face recognition among proposed face recognition methods utilizes the eigenfaces obtained from applying PCA to a training face image set. Thus, it may not achieve a reliable performance under illumination environments different from that of training face images. In this paper, we propose an illumination-separate eigenfaces-based face recognition method, which excludes the effects of illumination as much as possible. The proposed method utilizes the illumination-separate eigenfaces which is obtained by orthogonal decomposition of the eigenface space of face model image set with respect to the constructed face illumination subspace. Through experiments, it is shown that the proposed face recognition method based on the illumination-separate eigenfaces performs more robustly under various illumination environments than the conventional eigenfaces-based face recognition method.

• Journal of Korean Society of Steel Construction
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• v.24 no.3
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• pp.337-348
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• 2012

This study investigated the characteristics of bifurcation and the instability due to the initial imperfection of the space truss, which is sensitive to the initial conditions, and the calculated buckling load by the analysis of Eigen-values and the determinant of tangential stiffness. A two-free nodes model, a star dome, and a three-ring dome model were selected as case studies in order to examine the unstable phenomenon due to the sensitivity to Eigen mode, and the influence of the rise-span ratio and the load parameter on the buckling load were analyzed. The sensitivity to the imperfection of the two-free nodes model changed the critical path after reaching the limit point through the bifurcation mode, and the buckling load level was reduced by the increase in the amount of imperfection. The two sensitive buckling patterns for the model can be explained by investigating the displaced position of the free node, and the asymmetric Eigen mode was a major influence on the unstable behavior due to the initial imperfection. The sensitive mode was similar to the in-extensional mechanism basis of the simplified model. Since the rise-span ratio was higher, the effect of local buckling is more prominent than the global buckling in the star dome, and bifurcation on the equilibrium path occurring as the value of the load parameter was higher. Additionally, the buckling load levels of the star dome and the three-ring model were about 50-70% and 80-90% of the limit point, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.35-43
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• 2010

1D PCA utilized in the face appearance-based face recognition methods such as eigenface-based face recognition method may lead to less face representative power and more computational cost due to the resulting 1D face appearance data vector of high dimensionality. To resolve such problems of 1D PCA, 2D PCA-based face recognition methods had been developed. However, the face representation model obtained by direct application of 2D PCA to a face image set includes both face common features and face distinctive identity features. Face common features not only prevent face recognizability but also cause more computational cost. In this paper, we first develope a model of a face distinctive identity feature subspace separated from the effects of face common features in the face feature space obtained by application of 2D PCA analysis. Then, a novel robust face recognition based on the face distinctive identity feature subspace model is proposed. The proposed face recognition method based on the face distinctive identity feature subspace shows better performance than the conventional PCA-based methods (1D PCA-based one and 2D PCA-based one) with respect to recognition rate and processing time since it depends only on the face distinctive identity features. This is verified through various experiments using Yale A and IMM face database consisting of face images with various face poses under various illumination conditions.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.243-248
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• 2003

An incremental kernel principal component analysis (IKPCA) is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis (KPCA) is that the computation becomes prohibitive when the data set is large. Another problem is that, in order to update the eigenvectors with another data, the whole eigenspace should be recomputed. IKPCA overcomes these problems by incrementally computing eigenspace model and empirical kernel map The IKPCA is more efficient in memory requirement than a batch KPCA and can be easily improved by re-learning the data. In our experiments we show that IKPCA is comparable in performance to a batch KPCA for the feature extraction and classification problem on nonlinear data set.