• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.552-555
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• 2002

In order to achieve higher performance and low cost a-Si TFT-LCD panel, new material architecture and its process integration for a-Si TFT array manufacturing method were developed. Material combination of low resistant dry-etchable metal and new pixel electrode under currently adopted 4 mask process made it possible to get more-simplified manufacturing method and better device performance for the a-Si TFT-LCD application. Proposed 4 mask process architecture with optimized wet etchants and dry etching process was applicable to various devices such as notebook, monitor and TV.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.8
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• pp.720-725
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• 2007

This study investigated the pressing conditions in the molding of aspheric glass lenses for the mega pixel phone camera module using the DOE method. Tungsten carbide (WC; Japan, Everloy Co., 002K),which contained 0.5 w% cobalt (Co), was used to build the mold. The mold surface was ultra-precision ground and polished, and its form accuracy (PV) was 0.85um in aspheric surface. We selected four factors, pressing temperature, force and time of first step, and force of second step, respectively, as the parameters of the pressing process. in order to reduce the number of experiments, we applied fractional factorial design considering the main effects and two-way interactions. The analysis results indicate that the only two main effects, the pressing temperature and the time of pressing step 1, are available for the form accuracy (PV) of the molded lens. The analysis results indicated that the best combination of the factors for lowering the form accuracy(PV) value of molded lens was to have them at their low levels.

• 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 Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.64.1-64.1
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• 2013

The point spread function (PSF) of an optical system is in general defined as a two-dimensional intensity distribution which results from a single point source at infinity. It is an important key for the evaluation of the optical performance of an astronomical telescope. The PSFs of the soft X-ray telescope (SXT) aboard Yohkoh were measured in a wide range of the field-of-view under the in-flight configuration at White Sands Missile Range prior to launching the satellite. It has been known that the SXT PSF has a sharp peak at the core and the intensity drops very fast as it goes distant from the center. Due to the combination of this sharp peak at the PSF core and the effect of undersampling by a large pixel size, a carefully designed method is requested in the examination of the PSF data. The pattern of the SXT PSF is determined by the fitting of a mathematical functional form to the pre-launch experimental data. The elliptical Moffat function has been adopted for the evaluation of the SXT PSF. It is revealed from our study that the SXT PSF shows a peculiar characteristics, and thus a careful consideration on the undersampling effect and also a proper choice of statistics are necessary for the determination of the best fit function of the PSF. Details on the on- and off-axis SXT PSF in the field-of-view will be introduced and discussed in our presentation.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.6
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• pp.94-101
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• 2004

From the Beckmann's reflection model of wave incident, reflected light from a surface is known to have not only specular but also diffuse components. The specular component dominant a surface for a mirror-like surface is distributed on the almost the same area as the spot on the surface, but the diffuse component region dominant f3r a rough surface spreads scattered on the larger areas than the spot. Therefore, statistic parameters from the scattered light distribution are more meaningful in the diffuse region, while the magnitude of rather meaning in the specular region. In usual, there need two sensors to acquire two kinds of information: Photo-detector for light intensity magnitude and image sensor for light intensity distribution. But dual sensor scheme requires a beam splitter usually to feed light to each sensor, and moreover there is not a combination rule to relieve the different sensor characteristics. In this study a new method is proposed for acquisition of the dual information using only an image sensor. Specular region is established on an image area being distinguished from a diffuse component, and laser power is adjusted so that no pixel of the image sensor in the specular region is saturated. Simulation based on the light reflection theory and the experimental results are quite well matched, and thus the proposed method was proved to be very useful for mirror-like surface measurement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.3
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• pp.173-179
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• 2015

In this paper, object-based classification of urban areas based on a combination of information from lidar and aerial images is introduced. High resolution images are frequently used in automatic classification, making use of the spectral characteristics of the features under study. However, in urban areas, pixel-based classification can be difficult since building colors differ and the shadows of buildings can obscure building segmentation. Therefore, if the boundaries of buildings can be extracted from lidar, this information could improve the accuracy of urban area classifications. In the data processing stage, lidar data and the aerial image are co-registered into the same coordinate system, and a local maxima filter is used for the building segmentation of lidar data, which are then converted into an image containing only building information. Then, multiresolution segmentation is achieved using a scale parameter, and a color and shape factor; a compactness factor and a layer weight are implemented for the classification using a class hierarchy. Results indicate that lidar can provide useful additional data when combined with high resolution images in the object-oriented hierarchical classification of urban areas.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.403-406
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands. This algorithm is a hybrid algorithm that has used two methods combined. The first method used the differential absorption in brightness temperature difference between $11{\mu}m\;and\;12{\mu}m\;(BTD1)$. The radiation at $11{\mu}m$ is absorbed more than at $12{\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m\;and\;11{\mu}m(BTD2)$. This technique is sensitive to dust loading, which the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. First the Principle Component Analysis (PCA), a form of eigenvector statistical analysis from the two methods, is performed and the aerosol pixel with the lowest 10% of the eigenvalue is eliminated. Then the aerosol index (AI) from the combination of BTD 1 and 2 is derived. We applied this method to Multi-functional Transport Satellite-l Replacement (MTSAT-1R) data and obtained that the derived AI showed remarkably good agreements with Ozone Mapping Instrument (OMI) AI and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.34.1-34.1
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• 2018

We investigate the properties of bright galaxies with various morphological types in Abell 1139 and Abell 2589, using the pixel color-magnitude diagram (pCMD) analysis. The 32 bright member galaxies ($Mr{\leq}-21.3mag$) are deeply imaged in the g and r bands in our CFHT/MegaCam observations, as a part of the KASI-Yonsei Deep Imaging Survey of Clusters (KYDISC). We examine how the features of their pCMDs depend on galaxy morphology and infrared color. We find that the g - r color dispersion as a function of surface brightness (${\mu}r$) shows better performance in distinguishing galaxy morphology, than the mean g - r color does. The best set of parameters for galaxy classification appears to be a combination of the minimum color dispersion at ${\mu}r{\leq}21.2mag\;arcsec-2$ and the maximum color dispersion at $20.0{\leq}{\mu}r{\leq}21.0mag\;arcsec-2$: the latter reflects the complexity of stellar populations at the disk component in a typical spiral galaxy. Moreover, the color dispersion of an elliptical galaxy appears to be correlated with its WISE infrared color ([4.6]-[12]). This indicates that the complexity of stellar populations in an elliptical galaxy is related to its recent star formation activities. From this observational evidence, we infer that gas-rich minor mergers or gas interactions may have usually occurred during the recent growth of massive elliptical galaxies.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.6
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• pp.309-317
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• 2022

In this paper, we propose a machine learning-based copy-move forgery detection network with dual branches. Because the rotation or scaling operation is frequently involved in copy-move forger, the conventional convolutional neural network is not effectively applied in detecting copy-move tampering. Therefore, we divide the input into rotation-invariant and scaling-invariant features based on the wavelet coefficients. Each of the features is input to different branches having the same structure, and is fused in the combination module. Each branch comprises feature extraction, correlation, and mask decoder modules. In the proposed network, VGG16 is used for the feature extraction module. To check similarity of features generated by the feature extraction module, the conventional correlation module used. Finally, the mask decoder model is applied to develop a pixel-level localization map. We perform experiments on test dataset and compare the proposed method with state-of-the-art tampering localization methods. The results demonstrate that the proposed scheme outperforms the existing approaches.