• International Journal of Contents
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• v.11 no.3
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• pp.47-53
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• 2015

Visual object tracking is a fundamental problem in the field of computer vision, as it needs a proper model to account for drastic appearance changes that are caused by shape, textural, and illumination variations. In this paper, we propose a feature-based visual-object-tracking method with a sparse representation. Generally, most appearance-based models use the gray-scale pixel values of the input image, but this might be insufficient for a description of the target object under a variety of conditions. To obtain the proper information regarding the target object, the following combination of features has been exploited as a corresponding representation: First, the features of the target templates are extracted by using the HOG (histogram of gradient) and LBPs (local binary patterns); secondly, a feature-based sparsity is attained by solving the minimization problems, whereby the target object is represented by the selection of the minimum reconstruction error. The strengths of both features are exploited to enhance the overall performance of the tracker; furthermore, the proposed method is integrated with the particle-filter framework and achieves a promising result in terms of challenging tracking videos.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.148-154
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• 2016

In recent year, the interests of PQ is increase due to the increasing of non-linear load and distributed power sources in power system. For the parameters detection and feature extraction of PQ, and the PQ improvement method, continuous power signal monitering is needed. In this paper, the power signal compression and reconstruction method is suggested for power signal monitering. The power signal is compressed using DCT that has good compression performance, and the compressed signal is reconstructed through IDCT. And for the higher compression rate, DCT coefficients are arranged by magnitude in compression process, and in recouction process DCT coefficients are rearranged to original frequency position. The synthesized signal according to the IEC standard is used used in compression and reconstruction simulations. The performances of the proposed method are verified by comparing the error between synthesized signal and reconstructed signal.

• Journal of Korea Multimedia Society
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• v.7 no.3
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• pp.308-318
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• 2004

A subject to recognize the damaged facial image is becoming an important issue in commercialization of automatic face recognition. The method to recognize a face on a damaged image is divided into two types. The one is to recognize remainders after removing the damaged information and the other is to recognize a total face after recovering the damaged information. On this paper, we present the reconstruction method by analyzing the main materials after extracting the damaged region through Kohonen network. The suggested algorithm in this paper estimates feature vectors of the damaged region using eigen-faces in PCA and then reconstructs the damaged image. This allows also the reconstruction under the untrained images. Through testing the artificial images where the eye and the mouth which have many effects to face recognition are damaged, the recognition rate of the proposed results showed similar results with the method which used Kohonen network, and improved about 11.8% more than symmetrical property method. Also, in case of the untrained image, our results improved about 14% more than that of the Kohonen method and about 7% more than that of the symmetrical property method.

• Journal of Korean Society of Transportation
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• v.18 no.4
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• pp.107-115
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• 2000

We suggest a method which solves the planar, two vehicle collision reconstruction problem. The method based on the Principle of impulse and momentum determines the pre-impact velocity components from Post-impact velocity components, vehicle Physical data and collision geometry. A novel feature is that although the impact coefficients such as the restitution coefficient and the impulse ratio are unknown, the method can estimate automatically the coefficients and calculate the pre-impact velocity components. This reverse calculation is important for vehicle accident reconstruction, since the pre-impact velocities are unknown and Post-impact Phase is the starting Point in a usual collision analysis. However. an inverse solution is not always Possible with the analytical rigid-body impact model. Mathematically, one does not exist under the common velocity condition. On the other hand, our method has a capability of reverse calculation under the condition if the absorbed energy during the collision process can be estimated using the crush profile. To validate the developed collision reconstruction a1gorithm, we use car-to-car collision test results. The analysis and experimental results agree well in the impact coefficients and the Pre-impact velocity components.

• Wind and Structures
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• v.18 no.6
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• pp.693-715
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• 2014

Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.

• 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 Institute of Convergence Signal Processing
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• v.10 no.1
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• pp.13-22
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• 2009

In this paper, we propose the 3D shape reconstruction method that combine the mosaic method and the active stereo matching using the laser beam. The active stereo matching method detects the position information of the irradiated laser beam on object by analyzing the color and brightness variation of left and right image, and acquires the depth information in epipolar line. The mosaic method extracts feature point of image by using harris comer detection and matches the same keypoint between the sequence of images using the keypoint descriptor index method and infers correlation between the sequence of images. The depth information of the sequence image was calculated by the active stereo matching and the mosaic method. The merged depth information was reconstructed to the 3D shape information by wrapping and blending with image color and texture. The proposed reconstruction method could acquire strong the 3D distance information, and overcome constraint of place and distance etc, by using laser slit beam and stereo camera.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.2
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• pp.75-80
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• 2017

Recently, there has been growing interest in spatial data that combines information and communication technology with smart cities. The high-precision LiDAR (Light Dectection and Ranging) equipment is mainly used to collect three-dimensional spatial data, and the acquired data is also used to model geographic features and to manage plant construction and cultural heritages which require precision. The LiDAR equipment can collect precise data, but also has limitations because they are expensive and take long time to collect data. On the other hand, in the field of computer vision, research is being conducted on the methods of acquiring image data and performing 3D reconstruction based on image data without expensive equipment. Thus, precise 3D spatial data can be constructed efficiently by collecting and processing image data using CCTVs which are installed as infrastructure facilities in smart cities. However, this method can have an accuracy problem compared to the existing equipment. In this study, experiments were conducted and the results were analyzed to increase the number of extracted matching points by applying the feature-based method and the area-based method in order to improve the precision of 3D spatial data built with image data acquired from stereo CCTVs. For techniques to extract matching points, SIFT algorithm and PATCH algorithm were used. If precise 3D reconstruction is possible using the image data from stereo CCTVs, it will be possible to collect 3D spatial data with low-cost equipment and to collect and build data in real time because image data can be easily acquired through the Web from smart-phones and drones.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.13 no.2E
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• pp.26-31
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• 1994

A new feature extraction technique utilizing strange attractor and artificial neural network for speaker recognition is presented. Since many signals change their characteristics over long periods of time, simple time-domain processing techniques should e capable of providing useful information of signal features. In many cases, normal time series can be viewed as a dynamical system with a low-dimensional attractor that can be reconstructed from the time series using time delay. The reconstruction of strange attractor is described. In the technique, the raw signal will be reproduced into a geometric three dimensional attractor. Classification decision for speaker recognition is based upon the processing or sets of feature vectors that are derived from the attractor. Three different methods for feature extraction will be discussed. The methods include box-counting dimension, natural measure with regular hexahedron and plank-type box. An artificial neural network is designed for training the feature data generated by the method. The recognition rates are about 82%-96% depending on the extraction method.