• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.184-188
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• 2006

Lightscape is a visual rendering software enabling higher dimensional 3D image production using rendering as well. However, direct light simulation showed that more realistic feature of material-specific texture or color could be achieved by adjusting options. Accordingly, this study is to generate optimal values of options and achieve more realistic images by varying such values according to individual materials in order to create better quality simulation images using Lightscape.

• Korean Journal of Digital Imaging in Medicine
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• v.12 no.1
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• pp.27-31
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• 2010

The purpose of this study was to investigated the usefulness of MR perfusion image comparing with SPECT image. A total of pediatric 30 patients(average age : 7.8) with Moyamoya disease were performed MR Perfusion with 32 channel body coil at 3T from March 01, 2010 to June 10, 2010. The MRI sequences and parameters were as followed : gradient Echo-planar imaging(EPI), TR/TE : 2000ms/50ms, FA : $90^{\circ}$, FOV : $240{\times}240$, Matrix : $128{\times}128$, Thickness : 5mm, Gap : 1.5mm. Images were obtained contrast agent administrated at a rate of 1mL/sec after scan start 10s with a total of slice 1000 images(50 phase/1 slice). It was measured with visual color image and digitize data using MRDx software(IDL version 6.2) and also, it was compared of measurement with values of normal and abnormal ratio to analyze hemodynamic change, and a comparison between perfusion MR with technique using Warm Color at SPECT examination. On MR perfusion examination, the color images from abnormal region to the red collar with rCBV(relative cerebral blood volume) and rCBF(relative cerebral blood flow) caused by increase cerebral blood flow with brain vascular occlusion in surrounding collateral circulation advancement, the blood speed relatively was depicted slowly with blue in MTT(Mean Transit Time) and TTP(Time to Peak) images. The region which was visible abnormally from MR perfusion examination visually were detected as comparison with the same SPECT examination region, would be able to confirm the identical results in MMD(Moyamoya disease)judgments. Hymo-dynamic change in MR perfusion examination produced by increase and delay cerebral blood flow. This change with digitize data and being color imaging makes enable to distinguish between normal and abnormal area. Relatively, MR perfusion examination compared with SPECT examination could bring an excellent image with spatial resolution without radiation expose.

• The KIPS Transactions:PartB
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• v.18B no.2
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• pp.51-56
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• 2011

Algorithms for intrinsic images reduce color differences in RGB images caused by the temperature of black-body radiators. Based on the reference light and detecting single invariant direction, these algorithms are weak in real images which can have multiple invariant directions when the scene illuminant is a colored illuminant. To solve these problems, this paper proposes a method of acquiring an intrinsic image by omnidirectional projection of an ROI and a translation of white patch in the ${\chi}$-chromaticity space. Because it is not easy to analyze an image in the three-dimensional RGB space, the ${\chi}$-chromaticity is also employed without the brightness factor in this paper. After the effect of the colored illuminant is decreased by a translation of white patch, an invariant direction is detected by omnidirectional projection of an ROI in this chromaticity space. In case the RGB image has multiple invariant directions, only one ROI is selected with the bin, which has the highest frequency in 3D histogram. And then the two operations, projection and inverse transformation, make intrinsic image acquired. In the experiments, test images were four datasets presented by Ebner and evaluation methods was the follows: standard deviation of the invariant direction, the constancy measure, the color space measure and the color constancy measure. The experimental results showed that the proposed method had lower standard deviation than the entropy, that its performance was two times higher than the compared algorithm.

• Korean Journal of Remote Sensing
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• v.27 no.2
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• pp.75-87
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• 2011

This paper presents a method for region-based building extraction from high resolution satellite images(HRSI) using integrated information of spectral and color invariant features without user intervention such as selecting training data sets. The purpose of this study is also to evaluate the effectiveness of the proposed method by applying to IKONOS and QuickBird images. Firstly, the image is segmented by the MSRG method. The vegetation and shadow regions are automatically detected and masked to facilitate the building extraction. Secondly, the region merging is performed for the masked image, which the integrated information of the spectral and color invariant features is used. Finally, the building regions are extracted using the shape feature for the merged regions. The boundaries of the extracted buildings are simplified using the generalization techniques to improve the completeness of the building extraction. The experimental results showed more than 80% accuracy for two study areas and the visually satisfactory results obtained. In conclusion, the proposed method has shown great potential for the building extraction from HRSI.

• Journal of IKEEE
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• v.23 no.1
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• pp.112-119
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• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• Journal of Biomedical Engineering Research
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• v.30 no.1
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• pp.34-40
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• 2009

Transforms including translation and rotation are required for registering two or more images. In medical applications, different registration methods have been applied depending on the structures: for rigid bodies such as bone structures, affine transformation was widely used. In most previous research, a single transform was used for registering the whole images, which resulted in low registration accuracy especially when the degree of deformation was high between two images. In this paper, a novel registration method is introduced which is based image sub-division and bilinear interpolation of transformations. The proposed method enhanced the registration accuracy by 40% comparing with Trimmed ICP for registering color and MRI images.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.407-410
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• 2005

We used a series of Landsat images acquired from 1984 to 2001 to observe decadal changes of the research forest of Kangwon National University. Tree NDVI images of November in 1984, 1986 and 2001 were displayed in RGB color composite. This image enabled us to identify historical change of conifer types and their approximate ages. Conifers were classified into 'old conifer aged more than 25 years', 'young conifer aged 20-25 years' 'very young conifer aged less than 20 years', and recently deforested areas. The results coincide with in situ data very well. Archives of higher resolution images should be used to monitor the change of area for various tree types.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.870-874
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• 2009

Genetic algorithms can be effectively used when generating abstract images in an automatic way. However, managing huge number of automatically generated images has been problematic without sufficient managing mechanisms. This paper presents effective classification scheme for the abstract Affine images using form, emotion and color facets, and implements image databases.

• KSBB Journal
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• v.26 no.1
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• pp.87-91
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• 2011

Automatic counting of yeast cells in baker's yeast culture was tried using a conventional light microscope equipped with a pc camera. Relatively good binary image was obtained by using white LED as microscope light source, but uneven brightness distribution in original image hindered counting accuracy. A block binarization method using local thresholds proportional to local brightnesses was used to get improved binary images. The brightnesses of the blocks were expressed as the value component in HSV color model. Good quality binary images were obtained by binarization on $8{\times}6$ blocks of original images and connected-component labelling of the binarized images produced reliable counting results in the concentration range $1.4{\times}10^5/mL{\sim}1.4{\times}10^7\;cells/mL$.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.971-972
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• 2008

In this paper, we present a novel method for classifying pornography images using adaptive skin detection. From an input image, we detect initial skin regions and construct an adaptive skin probability density model using color information for the detected skin regions. From the skin probability density model, we extract feature vectors and train the images using Support Vector Machine to classify pornography images.