• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.269-270
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• 1998

Image-guided surgery (IGS) system has become well known in the field of neurosurgery and spine surgery. A patient's anatomy is first registered to preoperatively acquired CT/ MRI data using the point matching algorithm. A magnetic field digitizer was used to measure the physical space data and the system was based on Workstation of Unix system. To evaluate the spatial accuracy of interactive IGS system, the phantom consisting of rods varied height and known location was used. The RMS error value between CT/MR images and real location was 3-4mm. For the more convenience of the surgery, we provide various image display modules.

• Korean Journal of Remote Sensing
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• v.9 no.2
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• pp.7-19
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• 1993

In stereo vision, when we use two parallel axis images, small portion of object is contained and B/H(Base-line to Height) ratio is limited due to the size of object and depth information is inaccurate. To overcome these difficulities we take a non-parallel axis image which is rotated $\theta$ about y-axis and match other parallel-axis image. Epipolar lines of non-parallel axis image are not same as those of parallel-axis image and we can't match these two images directly. In this paper, we transform the non-parallel axis image geometrically with camera parameters, whose epipolar lines are alingned parallel. NCC(Normalized Cross Correlation) is used as match measure, area-based matching technique is used find correspondence and 9$\times$9 window size is used, which is chosen experimentally. Focal length which is necessary to get depth information of given object is calculated with least-squares method by CCD camera characteristics and lenz property. Finally, we select 30 test points from given object whose elevation is varied to 150 mm, calculate heights and know that height RMS error is 7.9 mm.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.275-285
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• 2016

This study analyzes the accuracy of ortho imagery based on whether camera calibration performed or not, using an unmanned aerial vehicle which equipped smart camera. Photgrammetric UAV system application was developed and smart camera performed image triangulation, and then created image as ortho imagery. Image triangulation was performed depending on whether interior orientation (IO) parameters were considered or not, which determined at the camera calibration phase. As a result of the camera calibration, RMS error appeared 0.57 pixel, which is more accurate compared to the result of the previous study using non-metric camera. When IO parameters were considered in static experiment, the triangulation resulted in 2 pixel or less (RMSE), which is at least 200 % higher than when IO parameters were not considered. After generate ortho imagery, the accuracy is 89% higher when camera calibration are considered than when they are not considered. Therefore, smart camera has high potential to use as a payload for UAV system and is expected to be equipped on the current UAV system to function directly or indirectly.

• Journal of dental hygiene science
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• v.19 no.4
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• pp.238-244
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• 2019

Background: Recently, three-dimensional (3D) printing has been hailed as a disruptive technology in dentistry. Among 3D printers, a digital light processing (DLP) 3D printer has certain advantages, such as high precision and relatively low cost. Therefore, the latest trend in resin crown manufacturing is the use of DLP 3D printers. However, studies on the internal fitness of such resin crowns are insufficient. The recently introduced 3D evaluation method makes it possible to visually evaluate the error of the desired area. The purpose of this study is to evaluate the internal fitness of resin crowns fabricated a by DLP 3D printer using the 3D evaluation method. Methods: The working model was chosen as the maxillary molar implant model. A total of 20 resin crowns were manufactured by dividing these into two groups. One group was manufactured by subtractive manufacturing system (PMMA), while the other group was manufactured by additive manufacturing system, which uses a DLP 3D printer. Resin crowns data were measured using a 3D evaluation program. Internal fitness was calculated by root mean square (RMS). The RMS was calculated using the Geomagic Verify software, and the mean and standard deviation (SD) were measured. For statistical analysis, IBM SPSS Statistics for Windows ver. 22.0 (IBM Corp., USA) was used. Then, independent t-test was performed between the two groups. Results: The mean±SD of the RMS were 41.51±1.51 and 43.09±2.32 for PMMA and DLP, respectively. There was no statistically significant difference between PMMA and DLP. Conclusion: Evaluation of internal fitness of the resin crown made using a DLP 3D printer and subtractive manufacturing system showed no statistically significant differences, and clinically acceptable results were obtained.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.319-324
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• 2011

In computing the optical flow. Horn and Schunck's method which is a representative algorithm is based on differentiation. But it is difficult to estimate the velocity for a large displacement by this algorithm. To cope with this problem multigrid method has been proposed. In this paper, we have proposed a scaled multigrid algorithm which the initial flow for a level is calculated by the summation of the optimally scaled flow and error flow. The optimally scaled flow is the scaled expanded flow of the previous level, which can generate an estimated second image having the least RMS error with respect to the original second image, and the error flow is the flow between the estimated second image (generated by the optimally scaled flow) and the original second image. The flow for this level is then estimated using the original first and second images and the initial flow for that level. From among the various coarsest starting levels of the multigrid algorithm, we select the one that finally gives the best estimated flow. Better results were achieved using our proposed method compared with Horn and Schunck's method and a conventional multigrid algorithm.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.30 no.11 s.254
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• pp.1051-1056
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• 2006

The mean and RMS velocity field of the respiratory gas flow in the human airway was studied experimentally by particle image velocimetry (PIV). Some researchers investigated the airflow for the mouth breathing case both experimentally and numerically. But it is very rare to investigate the airflow of nose breathing in a whole airway due to its geometric complexity. We established the procedure to create a transparent rectangular box containing a model of the human airway for PIV measurement by combination of the RP and the curing of clear silicone. We extend this to make a whole airway including nasal cavities, larynx, trachea, and 2 generations of bronchi. The CBC algorithm with window offset (64 $\times$ 64 to 32 $\times$ 32) is used for vector searching in PIV analysis. The phase averaged mean and RMS velocity distributions in Sagittal and coronal planes are obtained for 7 phases in a respiratory period. Some physiologic conjectures are obtained. The main stream went through the backside of larynx and trachea in inspiration and the frontal side in expiration. There exist vortical motions in inspiration, but no prominent one in expiration.

• Journal of Magnetics
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• v.18 no.3
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• pp.240-244
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• 2013

The influences of Gallium doping on the magnetic microstructures and corrosion behaviors of Nd-Fe-B-Ti-C alloys are investigated. The cooling rate for obtaining fully amorphous structure is raised, and the glassforming ability is improved by the Ga addition. The High Resolution Transmission Electron Microscopy image shows that the ${\alpha}$-Fe and $Fe_3B$ soft magnetic phases become granular surrounded by the $Nd_2Fe_{14}B$ hard magnetic phase. The rms and $({\Delta}{\varphi})_{rms}$ value of Nd-Fe-B-Ti-C nanocomposite alloy thick ribbons in the typical topographic and magnetic force images detected by Magnetic Force Microscopy(MFM) decreases with 0.5 at% Ga addition. The corrosion resistances of $Nd_9Fe_{73}B_{12.6}C_{1.4}Ti_{4-x}Ga_x$ (x = 0, 0.5, 1) alloys are enhanced by the Ga addition. It can be attributed to the formation of more amorphous phases in the Ga doped samples.

• The Journal of Engineering Geology
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• v.14 no.1
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• pp.21-28
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• 2004

Three-dimensional (3-D) resistivity inversion including a topographic effect can be considered theoretically to be the technique of acquiring the most accurate image in the interpretation of resistivity data, because it includes characteristic image that the actual subsurface structure is 3-D. In this study, a finite-element method was used as the numerical method in modeling, and the efficiency of Jacobian calculation has been maximized with sensitivity analysis for the destination block in inversion process. Also, during the iterative inversion, the resolution of inversion can be improved with the method of selecting the optimal value of Lagrange multiplier yielding minimum RMS(root mean square) error in the parabolic equation. In this paper, we present synthetic examples to compare the difference between the case which has the toprographic effect and the other case which has not the effect in the inversion process.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.