• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.563-569
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• 2021

With the launch of Compact Advanced Satellite 500 series of various characteristics and the operation of KOMPSAT-3/3A, uses of high-resolution satellite images have been continuously increased. Especially, in order to provide satellite images in the form of ARD (Analysis Ready Data), various pre-processing such as geometric correction and radiometric correction have been developed. For pre-processing of high spatial satellite imagery, auxiliary information, such as solar zenith, solar azimuth and offnadir angle, should be required. However, most of the high-resolution satellite images provide the solar zenith and nadir angle for the entire image as a single variable. In this paper, the solar zenith and offnadir angle corresponding to each pixel of the image were calculated using RFM (Rational Function Model) and auxiliary information of the image, and the quality of extracted information were evaluated. In particular, for the utilization of pixel-based solar zenith and offnadir angle, pixel-based auxiliary data were applied in calculating the top of atmospheric reflectance, and comparative evaluation with a single constant-based top of atmospheric reflectance was performed. In the experiments using various satellite imagery, the pixel-based solar zenith and offnadir angle information showed a similar tendency to the auxiliary information of satellite sensor, and it was confirmed that the distortion was reduced in the calculated reflectance in the top of atmospheric reflectance.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.269-282
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• 2023

The purpose of this study was to construct land cover maps using a deep learning model and to select the optimal deep learning model for land cover classification by adjusting the dataset such as input image size and Stride application. Two types of deep learning models, the U-net model and the DeeplabV3+ model with an Encoder-Decoder network, were utilized. Also, the combination of the two deep learning models, which is an Ensemble model, was used in this study. The dataset utilized RapidEye satellite images as input images and the label images used Raster images based on the six categories of the land use of Intergovernmental Panel on Climate Change as true value. This study focused on the problem of the quality improvement of the dataset to enhance the accuracy of deep learning model and constructed twelve land cover maps using the combination of three deep learning models (U-net, DeeplabV3+, and Ensemble), two input image sizes (64 × 64 pixel and 256 × 256 pixel), and two Stride application rates (50% and 100%). The evaluation of the accuracy of the label images and the deep learning-based land cover maps showed that the U-net and DeeplabV3+ models had high accuracy, with overall accuracy values of approximately 87.9% and 89.8%, and kappa coefficients of over 72%. In addition, applying the Ensemble and Stride to the deep learning models resulted in a maximum increase of approximately 3% in accuracy and an improvement in the issue of boundary inconsistency, which is a problem associated with Semantic Segmentation based deep learning models.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.321-329
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• 2017

This paper presents a bone metastasis Detection algorithm on abdominal computed tomography images for early detection using fully convolutional neural networks. The images were taken from patients with various cancers (such as lung cancer, breast cancer, colorectal cancer, etc), and thus the locations of those lesions were varied. To overcome the lack of data, we augmented the data by adjusting the brightness of the images or flipping the images. Before the augmentation, when 70% of the whole data were used in the pre-test, we could obtain the pixel-wise sensitivity of 18.75%, the specificity of 99.97% on the average of test dataset. With the augmentation, we could obtain the sensitivity of 30.65%, the specificity of 99.96%. The increase in sensitivity shows that the augmentation was effective. In the result obtained by using the whole data, the sensitivity of 38.62%, the specificity of 99.94% and the accuracy of 99.81% in the pixel-wise. lesion-wise sensitivity is 88.89% while the false alarm per case is 0.5. The results of this study did not reach the level that could substitute for the clinician. However, it may be helpful for radiologists when it can be used as a screening tool.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.55-68
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• 2012

In this paper, we propose a novel method to solve the problem of excessive segmentation out of the method of segmenting regions from an image using Homogeneity Threshold($H_T$). The algorithm of the previous image segmentation based on $H_T$ was carried out region growth by using only the center pixel of selected window. Therefore it was caused resulting in excessive segmented regions. However, before carrying region growth, the proposed method first of all finds out whether the selected window is homogeneity or not. Subsequently, if the selected window is homogeneity it carries out region growth using the total pixels of selected window. But if the selected window is not homogeneity, it carries out region growth using only the center pixel of selected window. So, the method can reduce remarkably the number of excessive segmented regions of image segmentation based on $H_T$. In order to show the validity of the proposed method, we carried out multiple experiments to compare the proposed method with previous method in same environment and conditions. As the results, the proposed method can reduce the number of segmented regions above 40% and doesn't make any difference in the quality of visual image when we compare with previous method. Especially, when we compare the image united with regions of descending order by size of segmented regions in experimentation with the previous method, even though the united image has regions more than 1,000, we can't recognize what the image means. However, in the proposed method, even though image is united by segmented regions less than 10, we can recognize what the image is. For these reason, we expect that the proposed method will be utilized in various fields, such as the extraction of objects, the retrieval of informations from the image, research for anatomy, biology, image visualization, and animation and so on.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.153-155
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• 2004

This paper proposes a real-time approach on the rotational motion estimation and correction for the roll stabilization of the sight system. This method first estimates a rotation center by the least-mean square algorithm based on the motion vectors of some feature points. And, then, a rotation angle is searched for a best matching block between a reference block image and seccessive input images using MPC(maximum pixel count) matching criterion. Finally, motion correction is performed by the bilinear interpolation technique. Various computer simulations show that the estimation performance is good and the proposed algorithm is a real-time implementable one to the TMS320C6415(500MHz) DSP.

• Journal of the Semiconductor & Display Technology
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• v.9 no.1
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• pp.11-16
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• 2010

Wafer Pre-Alignment is to find the center and the orientation of a wafer and to move the wafer to the desired position and orientation. In this paper, an area camera based pre-aligning method is presented that captures 8 wafer images regularly during 360 degrees rotation. From the images, wafer edge positions are extracted and used to estimate the wafer's center and orientation using least squares circle fitting. These data are utilized for the proper alignment of the wafer. For accurate alignments, camera calibration methods using high order polynomials are used for converting pixel coordinates into real-world coordinates. A complete pre-alignment system was constructed using mechanical and optical components and tested. Experimental results show that alignment of wafer center and orientation can be done with the standard deviation of 0.002 mm and 0.028 degree, respectively.

• Journal of Information Display
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• v.13 no.3
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• pp.125-130
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• 2012

In this paper, a new precharging method without any side effects is proposed to overcome image degradation caused by insufficient charging times. This work explains why the precharging method can compensate for short charging times. However, side effects of the precharging method in the form of horizontal line artifacts are addressed, wherein line artifact-compensating precharging (LCP) is presented to mitigate the side effects. Behavioral modeling is employed to investigate the side effects by estimating transient responses of a liquid crystal display. The LCP proves that it can dramatically reduce line artifacts caused by precharging because the brightness difference of adjacent pixels does not exceed 1.0.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.570-572
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• 2003

In this paper, a method of vegetation spectrum measurement using multi spectrum camera was studied. Each pixel in taken images using multi spectrum camera have spectrum data, the relationship between spectrum data and distribution, structure, etc. are directly turned out. In other words, detailed spectrum data information of object including spatial distribution can be obtained from those images. However, the camera has some problems for applying field measurement and data analysis. In this study, those problems are solved.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1474-1476
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• 2015

무안경방식 초다시점 3D 영상을 재현하기 위해서는 초다시점 콘텐츠 및 3D 디스플레이 설계가 필수적이다. 본 논문은 무안경방식 초다시점 3D 디스플레이를 설계하기 위한 주요 인자들을 정의하고 이들 상호적인 관계를 이용하여 2D 디스플레이에서 초다시점 3D콘텐츠 정보를 재현하는 시스템을 개발함으로써 디바이스 설계자에게는 능동적으로 디스플레이를 설계할 수 있도록 하고, 콘텐츠 개발자는 실제 초다시점 디스플레이를 설계하지 않고도 시뮬레이션을 통하여 콘텐츠의 입체감을 가이드 할 수 있는 시스템을 제안한다.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.58.1-58.1
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• 2019

We have developed Ground Software (GSW) of BITSE. The ground software includes mission operation software, data visualization software and data processing software. Mission operation software is implemented using COSMOS. COSMOS is a command and control system providing commanding, scripting and data visualization capabilities for embedded systems. Mission operation software send commands to flight software and control coronagraph. It displays every telemetry packets and provides realtime graphing of telemetry data. Data visualization software is used to display and analyze science image data in real time. It is graphical user interface (GUI) and has various functions such as directory listing, image display, and intensity profile. The data visualization software shows also image information which is FITS header, pixel resolution, and histogram. It helps users to confirm alignment and exposure time during the mission. Data processing software creates 4-channel polarization data from raw data.