• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1279-1292
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• 2022

This study proposes a non-contact input method based on face recognition and Pyautogui mouse control as a system that can help users who have difficulty using input devices such as conventional mouse due to physical discomfort. This study includes features that help web surfing more conveniently, especially screen zoom, scroll function, and also solves the problem of eye fatigue, which has been suggested as a limitation in existing non-contact input systems. In addition, various set values can be adjusted in consideration of individual physical differences and Internet usage habits. Furthermore, no high-performance CPU or GPU environment is required, and no separate tracker devices or high-performance cameras are required. Through these studies, we intended to contribute to the realization of barrier-free access by increasing the web accessibility of the disabled and the elderly who find it difficult to use web content.

• Imaging Science in Dentistry
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• v.54 no.3
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• pp.240-250
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• 2024

Purpose: This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared. Materials and Methods: A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which were determined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method. Results: In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The time required to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually, compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz). Conclusion: Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculate these measurements, the efficiency of diagnosis and treatment may be improved.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.638-642
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• 2005

For gas hydrate exploration, long offset multichannel seismic data acquired using by the 4km streamer length in Ulleung basin of the East Sea. The dataset was processed to define the BSRs (Bottom Simulating Reflectors) and to estimate the amount of gas hydrates. Confirmation of the presence of Bottom Simulating reflectors (BSR) and investigation of its physical properties from seismic section are important for gas hydrate detection. Specially, faster interval velocity overlying slower interval velocity indicates the likely presences of gas hydrate above BSR and free gas underneath BSR. In consequence, estimation of correct interval velocities and analysis of their spatial variations are critical processes for gas hydrate detection using seismic reflection data. Using Dix's equation, Root Mean Square (RMS) velocities can be converted into interval velocities. However, it is not a proper way to investigate interval velocities above and below BSR considering the fact that RMS velocities have poor resolution and correctness and the assumption that interval velocities increase along the depth. Therefore, we incorporated Migration Velocity Analysis (MVA) software produced by Landmark CO. to estimate correct interval velocities in detail. MVA is a process to yield velocities of sediments between layers using Common Mid Point (CMP) gathered seismic data. The CMP gathered data for MVA should be produced after basic processing steps to enhance the signal to noise ratio of the first reflections. Prestack depth migrated section is produced using interval velocities and interval velocities are key parameters governing qualities of prestack depth migration section. Correctness of interval velocities can be examined by the presence of Residual Move Out (RMO) on CMP gathered data. If there is no RMO, peaks of primary reflection events are flat in horizontal direction for all offsets of Common Reflection Point (CRP) gathers and it proves that prestack depth migration is done with correct velocity field. Used method in this study, Tomographic inversion needs two initial input data. One is the dataset obtained from the results of preprocessing by removing multiples and noise and stacked partially. The other is the depth domain velocity model build by smoothing and editing the interval velocity converted from RMS velocity. After the three times iteration of tomography inversion, Optimum interval velocity field can be fixed. The conclusion of this study as follow, the final Interval velocity around the BSR decreased to 1400 m/s from 2500 m/s abruptly. BSR is showed about 200m depth under the seabottom

• The Korean Journal of Nuclear Medicine
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• v.24 no.1
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• pp.29-36
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• 1990

Most of the diagnostic methods currently used for the detection of neoplastic masses provide indirect evidence. To obtain greater specificity in the interpretation of neoplasias by in vivo methods, the immunological approach appears to be most promising. Two problems that interfered with progress in this field were the lack of tumor specific antigen and the lack of well-defined and reproducible antibodies. To improve the sensitivity and specificity of radioimmunoscintigraphy as a technique for tumor localization, the use of monoclonal antibodies, fragments of antibodies and single photon emission computerized tomography (SPECT) are reasonable. The obvious advantages of monoclonal antibodies are their homogeneity, their specificity for the immunizing antigen and the reaction with a single determinant-thus no large immunecomplexes with antigen are formed. Monoclonal antibody technique has recently provided an opportunity to reevaluate the role of nuclear medicine for the diagnosis of malignant diseases by using the immunological approach. Out first results by means of radioimmunoscintigraphy of CEA and CA 19-9 producing tumors using a cocktail of fragments F $(ab')_2$, of mocolonal antibodies to CA 19-9 and CEA labeled with $^{131}I$ (IMACIS-1) are reported. The aims of this investigation was to evaluate the role of immunoscintigraphy in patients with colorectal and other cancers for diagnosis of local recurrences and metastasis. This report contains results of the first 8 colorectal and pancreas cancer patients with the elevation of the level of serum CEA and/or CA 19-9. IMACIS-1 was injected intravenously during 30 minutes in 100 ml saline solution after skin test. Planar scintigrams were recorded 3, 5 and 7 days after the injection of the IMACIS-1. Anterior, lateral and posterior views of the liver as well as anterior and posterior views of the pelvis were obtained in each patients as an $^{131}I-antibody$ image. We were able to localize exactly the malignant process with the double-nuclide double-compound $^{99m}Tc\;^{131}I$ (Tc+l) scintigrams. In Tc & I double-nuclide scintigraphy, computer subtraction display provided more clear localization of the tumor. We compared the results of radioimmunoscintigraphy with CT, ultrasonograms, conventional scintigrams. The results were as follows: 1) The sensitivity and specificity of radioimmunoscintigraphy using the fragments $F(ab')_2$ of the cocktails of CEA and CA 19-9 monoclonal antibodies were 80% and 100% respectively. 2) Tumor detection rate was not proportionated to the level of serum tumor markets. 3) Second tracer technique was essential for tumor localization as an anatomic landmark using double-nuclide scintigraphy. 4) A slow infusion of the antibodies was necessary to prevent the formation of large immune complexes. 5) Tumor/non-tumor radioactivity was most elevated at 7 days delayed imaging. 6) Using planar scintigraphic technique of $^{131}I$ labeled monoclonal antibodies are possible for imaging most of the tumors.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.171-187
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• 2023

When a person's face is recognized through a recording device such as a low-pixel surveillance camera, it is difficult to capture the face due to low image quality. In situations where it is difficult to recognize a person's face, problems such as not being able to identify a criminal suspect or a missing person may occur. Existing studies on face recognition used refined datasets, so the performance could not be measured in various environments. Therefore, to solve the problem of poor face recognition performance in low-quality images, this paper proposes a method to generate high-quality images by performing image quality improvement on low-quality facial images considering various environments, and then improve the performance of facial feature point detection. To confirm the practical applicability of the proposed architecture, an experiment was conducted by selecting a data set in which people appear relatively small in the entire image. In addition, by choosing a facial image dataset considering the mask-wearing situation, the possibility of expanding to real problems was explored. As a result of measuring the performance of the feature point detection model by improving the image quality of the face image, it was confirmed that the face detection after improvement was enhanced by an average of 3.47 times in the case of images without a mask and 9.92 times in the case of wearing a mask. It was confirmed that the RMSE for facial feature points decreased by an average of 8.49 times when wearing a mask and by an average of 2.02 times when not wearing a mask. Therefore, it was possible to verify the applicability of the proposed method by increasing the recognition rate for facial images captured in low quality through image quality improvement.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.101-108
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• 2020

Since the performance of deep learning techniques has recently been proven in the field of image processing, there are many attempts to perform classification, analysis, and detection of images using such techniques in various fields. Among them, the expectation of medical image analysis software, which can serve as a medical diagnostic assistant, is increasing. In this study, we are attention to the capsule endoscope image, which has a large data set and takes a long time to judge. The purpose of this paper is to distinguish the gastrointestinal landmarks and to estimate the gastrointestinal transition location that are common to all patients in the judging of capsule endoscopy and take a lot of time. To do this, we designed CNN-based Classifier that can identify gastrointestinal landmarks, and used it to estimate the gastrointestinal transition location by filtering the results. Then, we estimate gastrointestinal transition location about seven of eight patients entered the suspected gastrointestinal transition area. In the case of change from the stomach to the small intestine(pylorus), and change from the small intestine to the large intestine(ileocecal valve), we can check all eight patients were found to be in the suspected gastrointestinal transition area. we can found suspected gastrointestinal transition area in the range of 100 frames, and if the reader plays images at 10 frames per second, the gastrointestinal transition could be found in 10 seconds.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1235-1243
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• 2021

Geostationary Ocean Color Imager 2 (GOCI-II) on Geo-KOMPSAT-2 (GK2B)satellite was developed as a mission successor of GOCI on COMS which had been operated for around 10 years since launch in 2010 to observe and monitor ocean color around Korean peninsula. GOCI-II on GK2B was successfully launched in February of 2020 to continue for detection, monitoring, quantification, and prediction of short/long term changes of coastal ocean environment for marine science research and application purpose. GOCI-II had already finished IAC and IOT including early in-orbit calibration and had been handed over to NOSC (National Ocean Satellite Center) in KHOA (Korea Hydrographic and Oceanographic Agency). Radiometric calibration was periodically conducted using on-board solar calibration system in GOCI-II. The final calibrated gain and offset were applied and validated during IOT. And three video parameter sets for one day and 12 video parameter sets for a year was selected and transferred to NOSC for normal operation. Star measurement-based INR (Image Navigation and Registration) navigation filtering and landmark measurement-based image geometric correction were applied to meet the all INR requirements. The GOCI2 INR software was validated through INR IOT. In this paper, status and results of IOT, radiometric calibration and INR of GOCI-II are analysed and described.

• Radiation Oncology Journal
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• v.18 no.2
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• pp.114-119
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• 2000

Purpose : To evaluate the e지ent and frequency of the inter- and intra-treatment isocenter deviations of the whole pelvis radiation field in using small bowel displacement system (SBDS). Methods and Materials : Using electronic portal imaging device (EPID), 302 postero-anterior 232 lateral portal images were prospectively collected from 11 patients who received pelvic radiation therapy (7 with cervix cancer and 4 with rectal cancer). All patients were treated in prone position with SBDS under the lower abdomen. Five metallic fiducial markers were placed on the image detection unit for the recognition of the isocenter and magnification. After aligning the bony landmarks of the EPID images on those of the reference image, the deviations of the isocenter were measured in right-left (RL), cranio-caudal (CC), and PA directions. Results : The mean inter-treatment deviation of the isocenter in each RL, CC, and PA direction was 1.2 mm ($\pm$ 1.6 mm), 1.0 mm ($\pm$3.0 mm), and 0.9 mm ($\pm$4.4 mm), respectively. Inter-treatment isocenter deviations over 5 mm and 10 mm in RL, CC, and PA direction were 2, 12, 24$\%$, and 0, 0, 5$\%$, respectively. Maximal deviation was detected in PA direction, and was 11.5 mm. The mean intratreatment deviation of the isocenter in RL, CC, and PA direction was 0 mm ($\pm$0.9 mm), 0.1 mm ($\pm$ 1.9mm), and 0 mm ($\pm$1.6 mm), respectively. All intra-treatment isocenter deviations over 5 mm in each direction were 0, 1, 1$\pm$, respectively. Conclusions : As the greatest and the most frequent inter-treatment deviation of the isocenter was along the PA direction, it is recommended to put more generous safety margin toward the PA direction on the lateral fields if clinically acceptable in pelvic radiotherapy with SBDD.