• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2538-2543
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• 2012

The Percutaneous Coronary intervention is a typical way of testing which could be performance to treat a stenosed region by inserting a stent using catheter. In this case, choosing the best stent amongst various kinds of stent for performing an intervention is the most difficult process. For the reason, a width of the blood vessel which is stenosed must be correctly measured to help an operator choose right size of stent. So based on pixel, a width of the blood vessel measured by using the way of Euclidean distance after designing a center-line of vessel from a certain point assigned by operator is shown as a profile in this study. This study would be used as a goof reference for operators when choosing right size of stent.

• Journal of Korean Society on Water Environment
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• v.32 no.1
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• pp.60-69
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• 2016

In this study, we estimated root zone soil moisture dynamics using remotely sensed (RS) data. A soil moisture data assimilation scheme was used to derive the soil and root parameters from MODerate resolution Imaging Spectroradiometer (MODIS) data. Based on the estimated soil/root parameters and weather forcings, soil moisture dynamics were simulated at spatio-temporal scales based on a hydrological model. For calibration/validation, the Little Washita (LW13) in Oklahoma and Chungmi-cheon/Seolma-cheon sites were selected. The derived water retention curves matched the observations at LW 13. Also, the simulated soil moisture dynamics at these sites was in agreement with the Time Domain Reflectrometry (TDR)-based measurements. To test the applicability of this approach at ungauged regions, the soil/root parameters at the pixel where the Seolma-cheon site is located were derived from the calibrated MODIS-based (Chungmi-cheon) soil moisture data. Then, the simulated soil moisture was validated using the measurements at the Seolma-cheon site. The results were slightly overestimated compared to the measurements, but these findings support the applicability of this proposed approach in ungauged regions with predictable uncertainties. These findings showed the potential of this approach in Korea. Thus, this proposed approach can be used to assess root zone soil moisture dynamics at spatio-temporal scales across Korea, which comprises mountainous regions with dense forest.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.12
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• pp.48-54
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• 2010

This paper presents a high-performance architecture of integer-pel motion estimation circuit for H.264 video CODEC. Full search algorithm guarantees the best results by examining all candidate blocks. However, the full search algorithm requires a huge amount of computation and data. Many fast search algorithms have been proposed to reduce the computational efforts. The disadvantage of these algorithms is that data access from or to memory is very irregular and data reuse is difficult. In this paper, we propose an efficient integer-pixel motion estimation algorithm and the circuit architecture to improve the processing speed and reduce the external memory bandwidth. The proposed circuit supports seven kinds of variable block sizes and generates 41 motion vectors. We described the proposed high-performance motion estimation circuit at R1L and verified its operation on FPGA board. The circuit synthesized by using l30nm CMOS standard cell library processes 139.8 1080HD ($1,920{\times}1,088$) image frames per second and supports up to H.264 level 5.1.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.175-182
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• 2017

Noises are usually generated by various external causes and low quality devices in image data acquisition and recording as well as by channel interference in image transmission. Since these noise signals result in the loss of information, subsequent image processing is subject to the corruption of the original image. In general, image processing is performed in the mixed noise environment where common types of noise, known to be Gaussian and impulse, are present. This study proposes an iterative weighted mean filter for reducing mixed type of noise. Impulse noise pixels are first turned off in the input image, then $3{\times}3$ sliding window regions are processed by replacing center pixel with the result of weighted mean mask operation. This filtering processes are iterated until all the impulse noise pixels are replaced. Applied to images corrupted by Gaussian noise with ${\sigma}=10$ and different levels of impulse noise, the proposed filtering method improved the PSNR by up to 12.98 dB, 1.97 dB, 1.97 dB respectively, compared to SAWF, AWMF, MMF when impulse noise desities are less than 60%.

• Progress in Medical Physics
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• v.30 no.4
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• pp.128-138
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• 2019

Purpose: Segmental analysis of volumetric modulated arc therapy (VMAT) is not clinically used for compositional error source evaluation. Instead, dose verification is routinely used for plan-specific quality assurance (QA). While this approach identifies the resultant error, it does not specify which machine parameter was responsible for the error. In this research study, we adopted an approach for the segmental analysis of VMAT as a part of machine QA of linear accelerator (LINAC). Methods: Two portal dose QA plans were generated for VMAT QA: a) for full arc and b) for the arc, which was segmented in 12 subsegments. We investigated the multileaf collimator (MLC) position and dosimetric accuracy in the full and segmented arc delivery schemes. A MATLAB program was used to calculate the MLC position error from the data in the dynalog file. The Gamma passing rate (GPR) and the measured to planned dose difference (DD) in each pixel of the electronic portal imaging device was the measurement for dosimetric accuracy. The eclipse treatment planning system and a MATLAB program were used to calculate the dosimetric accuracy. Results: The maximum root-mean-square error of the MLC positions were <1 mm. The GPR was within the range of 98%-99.7% and was similar in both types of VMAT delivery. In general, the DD was <5 calibration units in both full arcs. A similar DD distribution was found for continuous arc and segmented arcs sums. Exceedingly high DD were not observed in any of the arc segment delivery schemes. The LINAC performance was acceptable regarding the execution of the VMAT QA plan. Conclusions: The segmental analysis proposed in this study is expected to be useful for the prediction of the delivery of the VMAT in relation to the gantry angle. We thus recommend the use of segmental analysis of VMAT as part of the regular QA.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.418-428
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• 2002

In this paper, we propose a motion-adaptive de-interlacing method using motion compensated interpolation. In a conventional motion compensated method, a simple pre-filter such as line averaging is applied to interpolate missing lines before the motion estimation. However, this method causes interpolation error because of inaccurate motion estimation and compensation. In the proposed method, EBMF(Edge Based Median Filter) as a pre-filter is applied, and new matching method, which uses two same-parity fields and opposite-parity field as references, is proposed. For further improvement, motion correction filter is proposed to reduce the interpolation error caused by incorrect motion. Simulation results show that the proposed method provides better performance than existing methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.553-559
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• 2004

In todays security industry, personal identification is also based on biometric. Biometric identification is performed basing on the measurement and comparison of physiological and behavioral characteristics, Biometric for recognition includes voice dynamics, signature dynamics, hand geometry, fingerprint, iris, etc. Iris can serve as a kind of living passport or living password. Iris recognition system is the one of the most reliable biometrics recognition system. This is applied to client/server system such as the electronic commerce and electronic banking from stand-alone system or networks, ATMs, etc. A new algorithm using nonlinear function in recognition process is proposed in this paper. An algorithm is proposed to determine the localized iris from the iris image received from iris input camera in client. For the first step, the algorithm determines the center of pupil. For the second step, the algorithm determines the outer boundary of the iris and the pupillary boundary. The localized iris area is transform into polar coordinates. After performing three times Wavelet transformation, normalization was done using sigmoid function. The converting binary process performs normalized value of pixel from 0 to 255 to be binary value, and then the converting binary process is compare pairs of two adjacent pixels. The binary code of the iris is transmitted to the by server. the network. In the server, the comparing process compares the binary value of presented iris to the reference value in the University database. Process of recognition or rejection is dependent on the value of Hamming Distance. After matching the binary value of presented iris with the database stored in the server, the result is transmitted to the client.

• Investigative Magnetic Resonance Imaging
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• v.21 no.4
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• pp.210-222
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• 2017

Purpose: To develop a novel combination of controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) with integrated SSFP (CAIPI-iSSFP) for accelerated SSFP imaging without banding artifacts at 3T. Materials and Methods: CAIPI-iSSFP was developed by adding a dephasing gradient to the balanced SSFP (bSSFP) pulse sequence with a gradient area that results in $2{\pi}$ dephasing across a single pixel. Extended phase graph (EPG) simulations were performed to show the signal behaviors of iSSFP, bSSFP, and RF-spoiled gradient echo (SPGR) sequences. In vivo experiments were performed for brain and abdominal imaging at 3T with simultaneous multi-slice (SMS) acceleration factors of 2, 3 and 4 with CAIPI-iSSFP and CAIPI-bSSFP. The image quality was evaluated by measuring the relative contrast-to-noise ratio (CNR) and by qualitatively assessing banding artifact removal in the brain. Results: Banding artifacts were removed using CAIPI-iSSFP compared to CAIPI-bSSFP up to an SMS factor of 4 and 3 on brain and liver imaging, respectively. The relative CNRs between gray and white matter were on average 18% lower in CAIPI-iSSFP compared to that of CAIPI-bSSFP. Conclusion: This study demonstrated that CAIPI-iSSFP provides up to a factor of four acceleration, while minimizing the banding artifacts with up to a 20% decrease in the relative CNR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2156-2170
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• 2020

This paper presents a fire detection algorithm with a minimal false detection rate, intended for a thermal imaging surveillance environment, whose properties vary depending on temporal conditions of day or night and environmental changes. This algorithm was designed to minimize the false detection alarm rate while ensuring a high detection rate, as required in fire detection applications. It was necessary to reduce false fire detections due to non-flame elements occurring when existing fixed threshold-based fire detection methods were applied. To this end, adaptive flame thresholds that varied depending on the characteristics of input images, as well as the center of gravity of the heat-source and hot-source regions, were analyzed in an attempt to minimize such non-flame elements in the phase of selecting flame candidate blocks. Also, to remove any false detection elements caused by camera shaking, one of the most frequently raised issues at outdoor sites, preliminary decision thresholds were adaptively set to the motion pixel ratio of input images to maximize the accuracy of the preliminary decision. Finally, in addition to the preliminary decision results, the texture correlation and intensity of the flame candidate blocks were averaged for a specific period of time and tested for their conformity with the fire decision conditions before making the final decision. To verify the fire detection performance of the proposed algorithm, a total of ten test videos were subjected to computer simulation. As a result, the fire detection accuracy of the proposed algorithm was determined to be 94.24%, with minimum false detection, demonstrating its improved performance and practicality compared to previous fixed threshold-based algorithms.