• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2844-2851
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• 2015

How to recover from the multiple 2D images into 3D object has been widely studied in the field of computer vision. In order to improve the accuracy of the recovered 3D shape, it is more important that noise must be minimized and the number of image frames must be guaranteed. However, potential noise is implied when tracking feature points. And the number of image frames which is consisted of an observation matrix usually decrease because of tracking failure, occlusions, or low image resolution, and so on. Therefore, it is obviously essential that the number of image frames must be secured by recovering the missing feature points under noise. Thus, we propose the analytic approach which can control directly the error distance and orientation of missing feature point by the geometrical properties under noise distribution. The superiority of proposed method is demonstrated through experimental results for synthetic and real object.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.714-718
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• 2009

3D reconstruction of a human face from an image sequence remains an important problem in computer vision. We propose a method, based on a factorization algorithm, that reconstructs a 3D face model from short image sequences exhibiting rotational motion. Factorization algorithms can recover structure and motion simultaneously from one image sequence, but they usually require that all feature points be well tracked. Under rotational motion, however, feature tracking often fails due to occlusion and frame out of features. Additionally, the paucity of images may make feature tracking more difficult or decrease reconstruction accuracy. The proposed 3D reconstruction approach can handle short image sequences exhibiting rotational motion wherein feature points are likely to be missing. We implement the proposal as a reconstruction method; it employs image sequence division and a feature tracking method that uses Active Appearance Models to avoid the failure of feature tracking. Experiments conducted on an image sequence of a human face demonstrate the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.383-388
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• 2005

Depth recovery in robot vision is an essential problem to infer the three dimensional geometry of scenes from a sequence of the two dimensional images. In the past, many studies have been proposed for the depth estimation such as stereopsis, motion parallax and blurring phenomena. Among cues for depth estimation, depth from lens translation is based on shape from motion by using feature points. This approach is derived from the correspondence of feature points detected in images and performs the depth estimation that uses information on the motion of feature points. The approaches using motion vectors suffer from the occlusion or missing part problem, and the image blur is ignored in the feature point detection. This paper presents a novel approach to the defocus technique based depth from lens translation using sequential SVD factorization. Solving such the problems requires modeling of mutual relationship between the light and optics until reaching the image plane. For this mutuality, we first discuss the optical properties of a camera system, because the image blur varies according to camera parameter settings. The camera system accounts for the camera model integrating a thin lens based camera model to explain the light and optical properties and a perspective projection camera model to explain the depth from lens translation. Then, depth from lens translation is proposed to use the feature points detected in edges of the image blur. The feature points contain the depth information derived from an amount of blur of width. The shape and motion can be estimated from the motion of feature points. This method uses the sequential SVD factorization to represent the orthogonal matrices that are singular value decomposition. Some experiments have been performed with a sequence of real and synthetic images comparing the presented method with the depth from lens translation. Experimental results have demonstrated the validity and shown the applicability of the proposed method to the depth estimation.

• Journal of Korean society of Dental Hygiene
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• v.17 no.3
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• pp.331-342
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• 2017

Objectives: This study aimed at helping oral health prevention of the blind and related management plan, which is defined as the influence factors between missing and filled permanent teeth index and general feature and oral health behavior of the blind in Korea (estimates 229,678 persons) using data of the 6th Korea National Health and Nutrition Examination Survey from 2014 Korea Centers For Disease Control and Prevention. Methods: The blind over the age of 30 were selected as study subjects who have conducted health survey and dental inspections in KNHANES VI-2. Estimates of the subjects were 229,67 persons. For analyzing data, general linear models: GLM and covariance analysis were conducted to identify the relation between general feature and oral health behavior and missing and filled permanent teeth index. SPSS 21 statistical program was used, which is possible to conduct complex sampling design, and the significance level was 0.05. Results: The missing and filled permanent teeth index was 8.58 points. Regarding the results of the analysis, R-squared of the missing and filled permanent teeth index depending on general features of the blind was 0.839 points, which shows gender, age, residence, education level, individual income, disability rating, kinds of health insurance, marital status and recipient of basic living had an effect on the missing and filled permanent teeth index. R2 of the missing and filled permanent teeth index depending on oral health form of the blind was 0.728 points, which shows oral examination, dental treatment, smoking and toothbrushing after lunch had an effect on the missing and filled permanent teeth index. Conclusions: With the result of this study, we found the oral health actual condition of the blind in Korea. Therefore, it is considered that the government needs to introduce the personalized oral health education program to maintain oral health of the blind and to develop a program that uses braille and voice device which enables to access and utilize to improve oral health behavior that the government could use it as a reference to establish the policy plan.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.600-619
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• 2015

We propose a cost-efficient hybrid algorithm for online location updates that efficiently combines feature point detection with the online trajectory-based sampling algorithm. Our algorithm is designed to minimize the average trajectory error with the minimal number of sample points. The algorithm is composed of 3 steps. First, we choose corner points from the map as sample points because they will most likely cause fewer trajectory errors. By employing the online trajectory sampling algorithm as the second step, our algorithm detects several missing and important sample points to prevent unwanted trajectory errors. The final step improves cost efficiency by eliminating redundant sample points on straight paths. We evaluate the proposed algorithm with real GPS trajectory data for various bus routes and compare our algorithm with the existing one. Simulation results show that our algorithm decreases the average trajectory error 28% compared to the existing one. In terms of cost efficiency, simulation results show that our algorithm is 29% more cost efficient than the existing one with real GPS trajectory data.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.737-741
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• 2009

This paper addresses the factorization method to estimate the projective structure of a scene from feature (points) correspondences over images with occlusions. We propose both a column and a row space approaches to estimate the depth parameter using the subspace constraints. The projective depth parameters are estimated by maximizing projection onto the subspace based either on the Joint Projection matrix (JPM) or on the the Joint Structure matrix (JSM). We perform the maximization over significant observation and employ Tardif's Camera Basis Constraints (CBC) method for the matrix factorization, thus the missing data problem can be overcome. The depth estimation and the matrix factorization alternate until convergence is reached. Result of Experiments on both real and synthetic image sequences has confirmed the effectiveness of our proposed method.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1577-1584
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• 2007

Fingerprint verification is being adopted widely to provide positive identification with a high degree of confidence in all practical areas. This popular usage requires reliable methods for matching of these patterns. To meet the latest expectations, the paper presents a pair wise distance histogram method for fingerprint matching. Here, we introduced a randomized algorithm which exploits pair wise distances between the pairs of minutiae, as a basic feature for match. The method undergoes two steps for completion i.e. first it performs the matching locally then global matching parameters are calculated in second step. The proposed method is robust to common problems that fingerprint matching faces, such as scaling, rotation, translational changes and missing points etc. The paper includes the test of algorithm on various randomly generated minutiae and real fingerprints as well. The results of the tests resemble qualities and utility of method in related field.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.1-7
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• 2007

In this paper, we analyze geometrical relations of 3D shape reconstruction from 2D images taken under anne projection. The purpose of this research is to contribute to more accurate 3-D reconstruction under noise distribution by analyzing geometrically the 2D to 3D relationship. In situation for no missing feature points (FPs) or no noise in 2D image plane, the accurate solution of 3D shape reconstruction is blown to be provided by Singular Yalue Decomposition (SVD) factorization. However, if several FPs not been observed because of object occlusion and image low resolution, and so on, there is no simple solution. Moreover, the 3D shape reconstructed from noise-distributed FPs is peturbed because of the influence of the noise. This paper focuses on analysis of geometrical properties which can interpret the missing FPs even though the noise is distributed on other FPs.

• Journal of the Korean School Mathematics Society
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• v.12 no.4
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• pp.453-472
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• 2009

This article describes the interview data with two preservice teachers where they dealt with five water-filling problems for the investigation of their covariational thinking. The study's results revealed that two students developed their covariation levels from Direction level to Instantaneous Rate with an aid of the pre-constructed GSP simulations for the problem situations. However, this study also points out that there is a missing important feature for a function notion, 'causality' in the covariation framework and suggests that future research should combine students' conception of causality with their covariational thinking for the investigation of their development of a function concept.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.32-43
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• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).