• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.265-278
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• 2019

Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) data help to enable rapid emergency responses through detection of the artificial and natural disasters occurring at night. The DNB data without correction of lunar irradiance effect distributed by Korea Ocean Science Center (KOSC) has advantage for rapid change detection because of direct receiving. In this study, radiance differences according to the phase of the moon was analyzed for urban and mountain areas in Korean Peninsula using the DNB data directly receiving to KOSC. Lunar irradiance correction algorithm was proposed for the change detection. Relative correction was performed by regression analysis between the selected pixels considering the land cover classification in the reference DNB image during the new moon and the input DNB image. As a result of daily difference image analysis, the brightness value change in urban area and mountain area was ${\pm}30$ radiance and below ${\pm}1$ radiance respectively. The object based change detection was performed after the extraction of the main object of interest based on the average image of time series data in order to reduce the matching and geometric error between DNB images. The changes in brightness occurring in mountainous areas were effectively detected after the calibration of lunar irradiance effect, and it showed that the developed technology could be used for real time change detection.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1393-1405
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• 2020

UAV (Unmanned Aerial Vehicle) can acquire high-resolution images due to low-altitude flight, and it can be photographed at any time. Therefore, the UAV images can be updated at any time in map production. Due to these advantages, studies on the possibility of producing large-scale digital maps using UAV images are actively being conducted. Precise digital maps can be used as base data for digital twins or smart cites. For producing a precise digital map, precise sensor modeling using GCPs (Ground Control Points) must be preceded. In this study, geometric models of UAV images were established through a precision sensor modeling algorithm developed in house. Then, a digital map by stereo plotting was produced to evaluate the possibility of large-scale digital map. For this study, images and GCPs were acquired for Ganseok-dong, Incheon and Yeouido, Seoul. As a result of precision sensor modeling accuracy analysis, high accuracy was confirmed within 3 pixels of the average error of the checkpoints and 4 pixels of the RMSE was confirmed for the two study regions. As a result of the mapping accuracy analysis, it satisfied the 1:1,000 mapping accuracy announced by the NGII (National Geographic information Institute). Therefore, the precision sensor modeling technology suggested the possibility of producing a 1:1,000 large-scale digital map by UAV images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.3
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• pp.58-65
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• 2002

In order to reduce blocking artifacts in inter-frame coded images, we propose a new image restoration algorithm, which directly processes differential images before reconstruction. We note that blocking artifact in inter-frame coded images is caused by both 8$\times$8 DCT and 16$\times$16 macroblock based motion compensation, while that of intra-coded images is caused by 8$\times$8 DCT only. According to the observation, we Propose a new degradation model for differential images and the corresponding restoration algorithm that utilizes additional constraints and convex sets for discontinuity inside blocks. The proposed restoration algorithm is a modified version of standard regularization that incorporate!; spatially adaptive lowpass filtering with consideration of edge directions by utilizing a part of DCT coefficients. Most of video coding standard adopt a hybrid structure of block-based motion compensation and block discrete cosine transform (BDCT). By this reason, blocking artifacts are occurred on both block boundary and block interior For more complete removal of both kinds of blocking artifacts, the restored differential image must satisfy two constraints, such as, directional discontinuities on block boundary and block interior Those constraints have been used for defining convex sets for restoring differential images.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1D
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• pp.81-88
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• 2012

This present research was carried out by dividing two parts; field surveying and data processing, in order to analyze changed patterns of a shoreline. Firstly, the shoreline information measured by the precise GPS positioning during long duration was collected. Secondly, the algorithm for detecting an auto boundary with regards to the changed shoreline with multi-image data was developed. Then, a comparative research was conducted. Haeundae beach which is one of the most famous ones in Korea was selected as a test site. RTK-GPS surveying had been performed overall eight times from September 2005 to September 2009. The filed test by aerial Lidar was conducted twice on December 2006 and March 2009 respectively. As a result estimated from both sensors, there is a slight difference. The average length of shoreline analyzed by RTK-GPS is approximately 1,364.6 m, while one from aerial Lidar is about 1,402.5 m. In this investigation, the specific algorithm for detecting the shoreline detection was developed by Visual C++ MFC (Microsoft Foundation Class). The analysis result estimated by aerial photo and satellite image was 1,391.0 m. The level of reliability was 98.1% for auto boundary detection when it compared with real surveying data.

• Journal of the Korean Society of Radiology
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• v.12 no.5
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• pp.637-643
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• 2018

A depth of interaction(DOI) detector module using a block scintillator and a pixellated scintillator was designed, and layer discrimination ability was calculated using DETECT2000. The block scintillator was used to improve the sensitivity and the spatial resolution was improved by measuring the DOI. The DOI was measured by analyzing the signal characteristics of each channel of the changed distribution of light. The detector module was composed to the block scintillator in the top layer and the pixellated scintillator in the bottom layer, which changes the distribution of light generated from a scintillator interacting with a gamma ray. In the flood image, the top layer was able to acquire the image at the position similar to the position of the bottom layer because the bottom layer consist of the pixellated scintillator. By using the Anger algorithm, the 16 channel signal was reduced to 4 channels to facilitate the analysis of the signal characteristics. The layer discrimination was measured using a simple algorithm and the accuracy was about 84% for each layer. When this detector module is used in preclinical PET, the spatial resolution at the outside of the field of view can be improved by measuring the DOI.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.117-130
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• 2008

This paper proposes a stable and robust method to distinct the color similarity for clothes using the LBG algorithm under various light sources, Since the conventional methods, such as the histogram intersection and the accumulated histogram, are profoundly sensitive to the changing of light environments, the distinction of color similarity for the same cloth can be different due to the complicated light sources. To reduce the effects of the light sources, the properties of hue and saturation which consistently sustain the characteristic of the color under the various changes of light sources are analyzed to define the characteristic of the color distribution. In a two-dimensional space determined by the properties of hue and saturation, the LBG algorithm, a non-parametric clustering approach, is applied to examine the color distribution of images for each clothes. The color similarity of images is defined by the average of Euclidean distance between the mapping clusters which are calculated from the result of clustering of both images. To prove the stability of the proposed method, the results of the color similarity between our method and the traditional histogram analysis based methods are compared using a dozen of cloth examples that obtained under different light environments. Our method successively provides the classification between the same cloth image pair and the different cloth image pair and this classification of color similarity for clothe images obtains the 91.6% of success rate.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.841-849
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• 2005

Two-phase flows, e.g. sediment-laden flow and bubbly flow, have two different flow profiles; flow velocity and sediment velocity. To measure velocity distributions of two-phase flows, it is necessary to use sophisticated instruments which can separate velocity profiles of two-phases. For bubbly flows, PIV (Particle Image Velocimetry) or PTV (Particle Tracking Velocimetry) has given fairly good velocity profiles of two-phases. However, for sediment-laden flows, the applications of PIV or PTV has not been so successful, because the sediment particles introduced to the flow kept the images from being analyzed. A new algorithm, which consists of several image analysis methods, is proposed to analyze sediment-laden flows. For detection algorithm, threshold method, edge detection method, and thinning method are adapted, and for finding matching pair PIV and PTV routines are combined. The proposed method can (1) detect sediment particles with irregular boundaries, (2) remove reflected images and scattered images, and (3) discriminate tracer particles from reflected images of sediment particles.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.181-192
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• 2017

Recently, many researches are carried out for Advanced Driver Assistant Systems(ADAS). Especially, many studies are carried out to analyze the road situation using road images. In order to improve the performance of the road situation analysis, it is necessary to acquire images with appropriate exposure time. In this paper, we design and implement multi exposure smart vehicular camera which provides road traffic information to driver. Proposed device can acquire road traffic information by on-board camera and various sensors. And we propose an auto exposure control algorithm for the road environment to increase accuracy of image recognition. In addition, we also propose the switching ROI method that apply existing ROI techniques to overcome a limited computation power of embedded devices. We developed prototype of multi exposure smart vehicular camera and performed experiments to evaluate proposed auto exposure control algorithm and switching ROI method. The results show that the average accuracy of image recognition increased by 13.45%.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.37-44
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• 2020

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.1
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• pp.33-39
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• 2017

In this paper, an effective feature which is capable of classifying targets among the detections obtained from 2D range-bearing maps generated in active sonar environments is proposed. Most conventional approaches for target classification with the 2D maps have considered magnitude of peak and statistical features of the area surrounding the peak. To improve the classification performance, HOG(Histogram of Gradient) feature, which is popular for their robustness in the image textures analysis is applied. In order to classify the target signal, SVM(Support Vector Machine) method with reduced HOG feature by the PCA(Principal Component Analysis) algorithm is incorporated. The various simulations are conducted with the real clutter signal data and the synthesized target signal data. According to the simulated results, the proposed method considering HOG feature is claimed to be effective when classifying the active sonar target compared to the conventional methods.