• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.3
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• pp.183-190
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• 2007

To make use of surveying data obtained from different sensors and different techniques, it is a pre-requite step that register them in a common coordinate system. For this purpose, we developed methodologies to register IKONOS-2 Satellite Imagery using LiDAR(Light Detection And Ranging) data. To achieve this, conjugate features from these data should be extracted in advance. In this study, linear features are chosen as conjugate features. Then, to register them, observation equations are established from similarity measurements of the extracted features and the results was evaluated statistically. The results clearly demonstrate that the proposed algorithms are appropriate to register these data.

• Korean Institute of Interior Design Journal
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• v.22 no.1
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• pp.183-193
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• 2013

The study aimed to find the fundamental differences of aesthetics in Korea, China, and Japan by analyzing expressive features and structural meanings of the storage furniture from $17^{th}$ to $19^{th}$ century. The study was performed in four steps; analysis of expressive features, isotopic analysis, semantic structure analysis, and comprehensive interpretation. The results showed that three countries had linear shapes with curvilinear elements, closed forms with open spaces, natural material hues with change of tone or color, and symmetrical forms with asymmetrical patterns and structures in common. Korea comparatively accented on the natural material colors and wood grains. China stressed on the big and wide faces using three-dimensional carving. Japan accented on the linear elements with strong color contrast and decorative metal fixtures. These features were caused by the traditional thoughts and according aesthetic principles. Korea and China were affected by the Confucianism focused on establishing the order of rank. Meanwhile, Japan was more influenced by the Buddhism emphasized on the individuality and communication. Therefore, the differences of the expressive features in furniture among the three countries were inevitable consequences of the different ideologies.

• Communications for Statistical Applications and Methods
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• v.6 no.3
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• pp.831-842
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• 1999

We propose a wavelet decomposition feature extraction method for the hand-written character recognition. Comparing the recognition rates of which methods with original image features and with selected features by the wavelet decomposition we study the characteristics of the proposed method. LDA(Linear Discriminant Analysis) QDA(Quadratic Discriminant Analysis) RDA(Regularized Discriminant Analysis) and NN(Neural network) are used for the calculation of recognition rates. 6000 hand-written numerals from CENPARMI at Concordia University are used for the experiment. We found that the set of significantly selected wavelet decomposed features generates higher recognition rate than the original image features.

• International Journal of Contents
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• v.10 no.1
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• pp.47-53
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• 2014

The paper considers the challenges involved in measuring the similarities between time series, such as time shifts and the mixture of frequencies. To improve recognition accuracy, we investigate an improved linear dynamical system for discovering prominent features by exploiting the evolving dynamics and correlations in a time series, as the quality of unsupervised pattern recognition relies strongly on the extracted features. The proposed approach yields a set of compact extracted features that boosts the accuracy and reliability of clustering for time series data. Experimental evaluations are carried out on time series applications from the scientific, socio-economic, and business domains. The results show that our method exhibits improved clustering performance compared to conventional methods. In addition, the computation time of the proposed approach increases linearly with the length of the time series.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.6_1
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• pp.529-536
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• 2007

Registration between image and object space is a fundamental step in photogrammetry and computer vision. Along with rapid development of sensors - multi/hyper spectral sensor, laser scanning sensor, radar sensor etc., the needs for registration between different sensors are ever increasing. There are two important considerations on different sensor registration. They are sensor invariant feature extraction and correspondence between them. Since point to point correspondence does not exist in image and laser scanning data, it is necessary to have higher entities for extraction and correspondence. This leads to modify first, existing mathematical and geometrical model which was suitable for point measurement to line measurements, second, matching scheme. In this research, linear feature is selected for sensor invariant features and matching entity. Linear features are incorporated into mathematical equation in the form of extended collinearity equation for registration problem known as photo resection which calculates exterior orientation parameters. The other emphasis is on the scheme of finding matched entities in the aide of RANSAC (RANdom SAmple Consensus) in the absence of correspondences. To relieve computational load which is a common problem in sampling theorem, deterministic sampling technique and selecting 4 line features from 4 sectors are applied.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.9-16
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• 2015

We propose a hybrid pattern recognition method that effectively combines two different features for improving data classification. We first extract the PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis) features, both of which are widely used in pattern recognition, to construct a set of basic features, and then evaluate the separability of each basic feature. According to the results of evaluation, we select only the basic features that contain a large amount of discriminative information for construction of the combined features. The experimental results for the various data sets in the UCI machine learning repository show that using the proposed combined features give better recognition rates than when solely using the PCA or LDA features.

• MALSORI
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• no.66
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• pp.61-72
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• 2008

This paper proposes a method to improve pathological and normal voice classification performance by combining multiple features such as auditory-based and higher-order features. Their performances are measured by Gaussian mixture models (GMMs) and linear discriminant analysis (LDA). The combination of multiple features proposed by the frame-based LDA method is shown to be an effective method for pathological and normal voice classification, with a 87.0% classification rate. This is a noticeable improvement of 17.72% compared to the MFCC-based GMM algorithm in terms of error reduction.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1469-1474
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• 2007

In recent years, research to machine large-surface micro-features has become important because of the light guide panel of a large-scale liquid crystal display and the bipolar plate of a high-capacity proton exchange membrane fuel cell. In this study, in order to realize the systematic design technology and performance improvements of an ultra-precision machine for machining the large-surface micro-features, a structural characteristic analysis was performed using its virtual prototype. The prototype consisted of gantry-type frame, hydrostatic feed mechanisms, linear motors, brushless DC servo motor, counterbalance mechanism, and so on. The loop stiffness was estimated from the relative displacement between the tool post and C-axis table, which was caused by a cutting force. Especially, the causes of structural stiffness deterioration were identified through the structural deformation analysis of sub-models.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.410-414
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• 1996

A visual servoing algorithm is proposed for a robot with a camera in hand. Specifically, novel image features are suggested by employing a viewing model of perspective projection to estimate relative pitching and yawing angles between the object and the camera. To compensate dynamic characteristics of the robot, desired feature trajectories for the learning of visually guided line-of-sight robot motion are obtained by measuring features by the camera in hand not in the entire workspace, but on a single linear path along which the robot moves under the control of a, commercially provided function of linear motion. And then, control actions of the camera are approximately found by fuzzy-neural networks to follow such desired feature trajectories. To show the validity of proposed algorithm, some experimental results are illustrated, where a four axis SCARA robot with a B/W CCD camera is used.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.842-847
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• 2005

This paper proposes a novel real-time EMG pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction virtual hand. From experimental results, we show that all processes, including virtual hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.