• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.47-52
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• 2016

The used digital images in the smart device and small displayer has been a downscaled image. In this paper, the detection of the downscaling image forgery is proposed using the feature vector according to the pixel value's gradients. In the proposed algorithm, AR (Autoregressive) coefficients are computed from pixel value's gradients of the image. These coefficients as the feature vectors are used in the learning of a SVM (Support Vector Machine) classification for the downscaling image forgery detector. On the performance of the proposed algorithm, it is excellent at the downscaling 90% image forgery compare to MFR (Median Filter Residual) scheme that had the same 10-Dim. feature vectors and 686-Dim. SPAM (Subtractive Pixel Adjacency Matrix) scheme. In averaging filtering ($3{\times}3$) and median filtering ($3{\times}3$) images, it has a higher detection ratio. Especially, the measured performances of all items in averaging and median filtering ($3{\times}3$), AUC (Area Under Curve) by the sensitivity and 1-specificity is approached to 1. Thus, it is confirmed that the grade evaluation of the proposed algorithm is 'Excellent (A)'.

• ETRI Journal
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• v.29 no.6
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• pp.826-828
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• 2007

Efficient downscaling in a transcoder is important when the output should be converted to a lower resolution video. In this letter, we suggest an efficient algorithm for transcoding from MPEG-4 SP (with simple profile) to H.264/AVC with spatial downscaling. First, target image blocks are classified into monotonous, complex, and very complex regions for fast mode decision. Second, adaptive search ranges are applied to these image classes for fast motion estimation in an H.264/AVC encoder with predicted motion vectors. Simulation results show that our transcoder considerably reduces transcoding time while video quality is kept almost optimal.

• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.185-191
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• 2005

In this paper, we proposed a downscaled 3D object technique using medical images for telediagnostic use. The proposed system consisted of downscaling/thresholding processes for building a downscaled 3D object and a process for obtaining 2D images at specific angles for diagnosis support. We used 80 slices of Digital Imaging and Communication in Medicine(DICOM) CT images as sample images and the platform-independent Java language for the experiment. We confirmed that the total image set size and transmission time of the original DICOM image set using a down-scaled 3D object decreased approximately $99\%\;and\;98.41\%,$ respectively. With additional studies, the proposed technique obtained from these results will become useful in supporting diagnosis for home and hospital care.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.20-25
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• 2019

Most multimedia signals contain image data, so the problem of efficient processing and transmitting the image data is an important task of the information society. This paper proposes a compression algorithm that reduces the color bits according to importance using YUV color space among the various methods of compressing image data. 4: 2: 2 subsampling is the standard in the field of video. Using the color information and the characteristics of the human retina, YUV color data was reduced by 4: 2: 2 subsampling. The YUV images and RGB images can be interconverted using the transformation matrix. The image data was converted into color space by YUV, and the relatively low U and V bits were subjected to a downscaling operation. The data was then compressed through 4: 2: 2 subsampling. The performance of the proposed algorithm was compared and analyzed by a comparison with existing methods. As a result of the analysis, it was possible to compress the image without reducing the information of the low importance color element and without significant deterioration in the quality compared to the original.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_2
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• pp.559-565
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• 2013

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.31-31
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• 2015

토양수분은 일반적으로 시료를 채취하거나 현장에 설치된 다양한 센서를 통해 추정하지만 이는 시간과 비용이 많이 소모되기 ?문에 유역내의 공간적인 토양수분 분포를 추정하는데 상당한 어려움이 따른다. 토양수분뿐만 아니라 공간적인 대기현상, 토양수분, 식생현황 등을 관측하는데 대중적으로 사용되는 것이 위성 관측이며, 기본적으로는 위성에 탑재된 센서가 각 주파수대역에 따라 영상을 생성하면 이를 특정 알고리듬을 적용하여 원하는 값을 도출하게 된다. 토양수분 산정에 사용되는 대표적인 위성영상으로는 SMOS (Soil Moisture and Ocean Salinity), ARMS-E(Advanced Microwave Scanning Radiometer - Earth Observing System), ARMS2 (ARMS ver.2) 영상 등이 있으며, 이러한 위성은 해상도가 약 10 km ~ 40 km로 상당이 낮기 때문에 우리나라와 같이 면적이 좁고 지형이 복잡하며 다양한 토지피복이 밀집되어있는 곳에서는 기존 수문 연구에 응용할 수 있는 토양수분 공간지도 산정을 위해 상세화(Downscaling)과정이 필요하다고 판단된다. 따라서 본 연구에서는 ARMS2 토양수분 영상을 MODIS 영상의 식생지수(NDVI, Normalized Difference Vegetation Index), 알베도 및 온도를 활용하여 공간적으로 상세화된 토양 수분 지도를 작성하였고, 유역 내에서 실제 측정되고 있는 토양수분 관측값을 활용하여 상세화기법의 적용성을 검토하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.5
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• pp.494-503
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• 2002

A motion vector re-estimation algorithm for image downscaling in discrete consine transform domain is presented. Kernel functions are difined using SAD (Aum of Absolute Difference) and edge information of a macroblock. The proposed method uses these kernel functions to re-estimate a new motion vector of the downscaled image. The motion vectors from the incoming bitstream of transcoder are reused to reduce computation burden of the block-matching motion estimation, and we also reuse the given motion vectors. Several experiments in this paper show that the computation efficiency and the PSNR (Peak Signal to Noise Ratio) and better than the previous methods.

• Journal of the Korea Computer Graphics Society
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• v.17 no.1
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• pp.39-44
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• 2011

In this paper, we introduce a novel method to reduce image to a small size, such that the quality of the image is improved when it is up-scaled. Recent hardwares including cameras and display devices allow us to capture and display high-resolution images. However, it is not always realistic to store and transmit those high-resolution images due to limitation of storage and network bandwidth. Therefore, high-resolution images are often down-scaled to be stored and transmitted, and then up-scaled back for display. To improve final image quality in this scenario, we first formulate selected up-scale methods as linear transformations. The optimal reduction methods are obtained as its inverse transformation. Based on this basic idea, we develop down-scale kernel that is optimized for each up-scale method. In our experiment, the proposed method could improve the quality of the up-scaled image noticeable.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.749-762
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• 2020

Precipitation is closely related to various hydrometeorological phenomena, such as runoff and evapotranspiration. In Korean Peninsula, observing rainfall intensity using weather radar and rain gauge network is dominating due to their accurate, intuitive and precise detecting power. However,since these methods are not suitable at ungauged regions, rainfall detection using satellite is required. Satellite-based rainfall data has coarse spatial resolution (10 km, 25 km), and has a limited range of usage due to its reliability of data. The aim of this study is to obtain finer scale precipitation. Especially, to make the applicability of satellite higher at ungauged regions, 10 km satellite-based rainfall data was downscaled to 1 km data using MODerate Resolution Imaging Spectroradiometer (MODIS) based cloud property. Downscaled precipitation was verified in urban region, which has complex topographical and environmental characteristics. Correlation coefficient was similar in summer (+0), decreased in spring (-0.08) and autumn (-0.01), and increased in winter (+0.04) season compared to Global Precipitation Measurement (GPM) based precipitation. Downscaling without calibration using in situ data could be useful in areas where rain gauge system is not sufficient or ground observations are rarely available.

• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.