• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.375-378
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• 1997

This research deals with a problem of reconstructing 3D surface structures from their 2D projections, which is an important research topic in computer vision. In order to provide robust reconstruction algorithm, that is reliable even in the presence of uncertainty in the range images, we first present a detailed model and analysis of several error sources and their effects on measuring three-dimensional surface properties using the space encoded range imaging technique. Our approach has two key elements. The first is the error modeling for the space encoding range sensor and its propagation to the 3D surface reconstruction problem. The second key element in our approach is the algorithm for removing outliers in the range image. Such analyses, to our knowledge, have never attempted before. Experimental results show that our approach is significantly reliable.

• Proceedings of the KSRS Conference
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• v.1
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• pp.504-507
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• 2006

Airborn Lidar technology has been applied to diverse applications with the advantages of accurate 3D information. Further, Lidar intensity, backscattered signal power, can provid us additional information regarding target's characteristics. Lidar intensity varies by the target reflectance, moisture condition, range, and viewing geometry. This study purposes to generate normalized airborne LiDAR intensity image considering those influential factors such as reflectance, range and geometric/topographic factors (scan angle, ground height, aspect, slope, local incidence angle: LIA). Laser points from one flight line were extracted to simplify the geometric conditions. Laser intensities of sample plots, selected by using a set of reference data and ground survey, werethen statistically analyzed with independent variables. Target reflectance, range between sensor and target, and surface slope were main factors to influence the laser intensity. Intensity of laser points was initially normalized by removing range effect only. However, microsite topographic factor, such as slope angle, was not normalized due to difficulty of automatic calculation.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.1
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• pp.129-137
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• 2017

When the micro-Doppler (MD) image of a ballistic missile is derived, the translational motion compensation (TMC) method is usually applied to the inverse synthetic aperture radar (ISAR) image, but yields poor results because of the micro-motion of the ballistic missile. This paper proposes an efficient TMC method to obtain a focused MD image of a ballistic missile engaged in complicated micro-motion. During range alignment, range profiles (RPs) are coarsely aligned by using the 1D entropy cost function of RPs as a mark, then the coarsely-aligned RPs are fine-aligned by using the minimum 2D entropy of the MD image. During phase adjustment, the gradient of the phase error is appropriately weighted and added to the previous phase error to further fine-tune the aligned RPs. In simulations using the point scatterer model and the measured data from the real missile model, the proposed method provided better image focus than the existing method.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.82-86
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• 2017

High dynamic range (HDR) imaging offers a radically approach of representing colors in digital images. Instead of using the range of colors produced by given devices, HDR imaging method manipulates and stores all colors and brightness levels visible to the human eye. To faithfully represent, store and then reproduce all these effects, the original scene must be stored and treated using high fidelity HDR techniques. Then, tone mapping is required to accommodate HDR image to low dynamic range (LDR) devices, and tone mapping operation of HDR image for realistic display is commonly researched. However, color visualization for analyzing scene luminance in HDR imaging has less attention from researches. This paper presents and implements a method for reproduction and visualization of the false color in HDR images. We produce a color visualization framework with several mapping functions, and evaluate their effectiveness by using RMAE and SNR with commonly used HDR image data. Experiment reveals that the sigmodal mapping function shows better performance in the false color visualization, compared to other methods.

• Asian Oncology Nursing
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• v.8 no.2
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• pp.86-92
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• 2008

Purpose: This study was designed to investigate the body-image and quality of life in breast cancer patients with mastectomy. Methods: Data were obtained by self-reported questionaries from 110 patients undergone mastectomy from August 1 to 31, 2007. And data were analyzed using SPSS/PC WIN 12.0 program and frequency, percentage, mean, minimum, maximum, t-test, ANOVA, Scheffe test, Pearson's Correlation Coefficient were used. The results of the study were as follows; 1) Mean of Body image of the subjects was $52.54{\pm}6.67$ (range of scale; 17 to 85). Mean of quality of life was $118.01{\pm}34.37$ (range of scale; 0 to 10). 2) There was no significant difference with the score of body image by demographic and disease-related characteristics of subjects. 3) There was significant difference with the score of quality of life by the work type and economic status of subjects. 4) Relationship between body image and quality of life showed moderately strong positive correlation (r=0.408, p<0.0001). Conclusion: Mean of body image and of quality of life in mastectomy patients were moderate. Relationship between body image and quality of life showed moderately strong positive correlation. Therefore, for improving the quality of life, it is needed to improve body image in breast cancer patients with mastectomy.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.1 s.343
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• pp.65-70
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• 2006

An image sensor has limited dynamic range while the human eye has logarithmic response over wide range of light intensity. Although the sensor gain can be set high to identify details in darker area on the image, this results in saturation in brighter area. The gamma correction is essential to fit the human eye response. However, the digital gamma correction degrades image quality especially for darker area on the image due to the limited ADC resolution and the dynamic range. This Paper proposes a CMOS image sensor (CIS) with a nonlinear analog-to-digital converter (AU) which performs analog gamma correction. The CIS with the proposed nonlinear analog-to-digital conversion scheme was fabricated with a $0.35{\mu}m$ CMOS process. The analog gamma correction using the proposed nonlinear ADC CIS provides the 2.2dB peak-signal-to-noise-ratio(PSM) improved image qualify than conventional digital gamma correction. The PSNR of the image obtain from the digital gamma correction is 25.6dB while it is 27.8dB for analog gamma correction. The PSNR improvement over digital gamma correction is about $28.8\%$.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2086-2094
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• 2008

Finding a planar surface on 3D space is very important for efficient and safe operation of a mobile robot. In this paper, we propose a method using a plane detection cell (PDC) and iterative randomized Hough transform (IRHT) for finding the planar region from a 3D range image. First, the local planar region is detected by a PDC from the target area of the range image. Each plane is then segmented by analyzing the accumulated peaks from voting the local direction and position information of the local PDC in Hough space to reduce effect of noises and outliers and improve the efficiency of the HT. When segmenting each plane region, the IRHT repeatedly decreases the size of the planar region used for voting in the Hough parameter space in order to reduce the effect of noise and solve the local maxima problem in the parameter space. In general, range images have many planes of different normal directions. Hence, we first detected the largest plane region and then the remained region is again processed. Through this procedure, we can segment all planar regions of interest in the range image.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1395-1403
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• 2022

Lately, over 4K high definition and high dynamic range (HDR) display devices are popularized, various interpolation and HDR methods have been researched to expand the size and the dynamic range. Since most of the legacy low resolution (LR) images require both an interpolation and a HDR tone mapping methods, the two processes should be subsequently applied. Therefore, the proposed algorithm presents a HDR up-scaling algorithm using undecimated wavelet transform and Retinex method, which transfers a LR image of low dynamic range (LDR) into the high resolution (HR) with HDR. The proposed algorithm consists of an up-scaling scheme increasing the image size and a tone mapping scheme expanding the dynamic range. The up-scaling scheme uses the undecimated version of the simplest Haar wavelet analysis for the 8-directional interpolation and the change region is extracted during the analysis. This region information is utilized in controlling the surround functions' size of the proposed tone mapping using MSRCR, to enhance the pixels of around the edges that are dominant feature of the subjective image quality. As the results, the proposed algorithm can apply an up-scaling and tone mapping processes in accordance with the type of pixel.

• Proceedings of the Optical Society of Korea Conference
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• 1999.08a
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• pp.52-53
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• 1999

• Transactions of the Society of Information Storage Systems
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• v.5 no.2
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• pp.89-95
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• 2009

We setup the system to measure the noise characteristics of the 5M complementary metal-oxide semiconductor (CMOS) image sensor by generic measurement indicator of Standard mobile imaging architecture (SMIA) which is one of internal standard of mobile imaging architecture. To evaluate the effect of environment and setting parameters, such as temperature and integration time, we measure the variation of the dark signal, dynamic range and fixed pattern noise of image sensor. We also detect the number of defective pixels and cluster defects defined as adjacent single defect pixels at 5M CMOS image sensor. Then, we find the existence of some cluster defects in experiment, which are not expected in calculation.