• Korean Journal of Radiology
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• v.25 no.1
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• pp.24-32
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• 2024

Despite improvements in operative techniques and perioperative care, post-hepatectomy liver failure (PHLF) remains the most serious cause of morbidity and mortality after surgery, and several risk factors have been identified to predict PHLF. Although volumetric assessment using imaging contributes to surgical simulation by estimating the function of future liver remnants in predicting PHLF, liver function is assumed to be homogeneous throughout the liver. The combination of volumetric and functional analyses may be more useful for an accurate evaluation of liver function and prediction of PHLF than only volumetric analysis. Gadoxetic acid is a hepatocyte-specific magnetic resonance (MR) contrast agent that is taken up by hepatocytes via the OATP1 transporter after intravenous administration. Gadoxetic acid-enhanced MR imaging (MRI) offers information regarding both global and regional functions, leading to a more precise evaluation even in cases with heterogeneous liver function. Various indices, including signal intensity-based methods and MR relaxometry, have been proposed for the estimation of liver function and prediction of PHLF using gadoxetic acid-enhanced MRI. Recent developments in MR techniques, including high-resolution hepatobiliary phase images using deep learning image reconstruction and whole-liver T1 map acquisition, have enabled a more detailed and accurate estimation of liver function in gadoxetic acid-enhanced MRI.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.6
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• pp.70-81
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• 2001

This paper presents a 3D face modeling method using a CCD camera and a projector (LCD projector or Slide projector). The camera faces the human face and the projector casts white stripe patterns on the human face. The 3D shape of the face is extracted from spatial and temporal locations of the white stripe patterns on a series of image frames. The proposed method employs region segmentation and multi-beam techniques for efficient 3D modeling of hair region and faster 3D scanning respectively. In the proposed method, each image is segmented into face, hair, and shadow regions, which are independently processed to obtain the optimum results for each region. The multi-beam method， which uses a number of equally spaced stripe patterns, reduces the total number of image frames and consequently the overall data acquisition time. Light beam calibration is adopted for efficient light plane measurement, which is not influenced by the direction (vertical or horizontal) of the stripe patterns. Experimental results show that the proposed method provides a favorable 3D face modeling results, including the hair region.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.11 no.2
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• pp.7-15
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• 1993

In order to generate database, we need to obtain speedy and corret topographic information according to requisite purpose. Generally methods to an acquisition of topographic information are available by the use of maps, satellite images, stereo models of aerophoto and so forth. But we must choose a optimal method in consideration of area of object region, spatial solution of image, required accuracy and economic. Therefore, this study aims at providing the establish method of efficient topographic data base of small object region by means of spatial layer techniques of geo-spatial information system and using acquisition of geo-information and production method of base map with photoballoon system to obtain topographic information for reasonable plan and design of object region which select a zone preparation of a collective village with small region. As a result of this study, we decided an f-stop and a shutter speed of camera to obtain accurate stereo model and were able to obtain stereo photography and topography for small region by using of photoballoon system through accuracy analysis according to change flight height and air base speedly and economically. We can establish the data base useable to efficient plan and design as existence map with overlay plan drawing.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1131-1135
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• 2005

Since human is vulnerable to emotional, physical and environmental distractions, most human inspectors cannot sustain a consistent 8-hour inspection in a day specifically for small components like door locking levers. As an alternative for human inspection, presented in this study is the development of a machine vision inspection system (MVIS) purposely for door locking levers. Comprises the development is the structure of the MVIS components, designed to meet the demands, features and specifications of door locking lever manufacturing companies in increasing their production throughput upon keeping the quality assured. This computer-based MVIS is designed to perform quality measures of detecting missing portions and defects like burr on every door locking lever. NI Vision Builder software for Automatic Inspection (AI) is found to be the optimum solution in configuring the needed quality measures. The proposed software has measurement techniques such as edge detecting and pattern-matching which are capable of gauging, detecting missing portion and checking alignment. Furthermore, this study exemplifies the incorporation of the optimized NI Builder inspection environment to the pre-inspection and post-inspection subsystems.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.305-308
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• 2002

More complex radiotherapy techniques using multi leaf collimation(MLC) such as intensity-modulated radiation therapy(IMRT) has been increasing the significance of verification of leaf position and motion. Due to the reliability and robustness, quality assurance(QA) of MLC is usually performed with portal films. However, the advantage of ease of use and capability of providing digital data of electronic portal imaging devices(EPIDs) have attracted many attentions as alternatives of films for routine quality assurance in spite of the concerns about their clinical feasibility, efficacy, and the cost to benefit ratio. In our work, the method of routine QA of MLC using electronic portal imaging(EPI) was developed. The verification of availability of EPI images for routine QA was performed by comparison with those of the portal films which were simultaneously obtained when radiation was delivered and known prescription input to MLC controller. Specially designed test patterns of dynamic MLC were applied to image acquisition. Quantitative off-line analysis using edge detection algorithm enhanced the verification procedure in addition to on-line qualitative visual assessment. In conclusion, the EPI is available enough for routine QA with the accuracy of portal films.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.250-255
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• 2012

Face recognition has wide applications in security and surveillance systems as well as in robot vision and machine interfaces. Conventional challenges in face recognition include pose, illumination, and expression, and face recognition at a distance involves additional challenges because long-distance images are often degraded due to poor focusing and motion blurring. This study investigates the effectiveness of applying photon-counting linear discriminant analysis (Pc-LDA) to face recognition in harsh environments. A related technique, Fisher linear discriminant analysis, has been found to be optimal, but it often suffers from the singularity problem because the number of available training images is generally much smaller than the number of pixels. Pc-LDA, on the other hand, realizes the Fisher criterion in high-dimensional space without any dimensionality reduction. Therefore, it provides more invariant solutions to image recognition under distortion and degradation. Two decision rules are employed: one is based on Euclidean distance; the other, on normalized correlation. In the experiments, the asymptotic equivalence of the photon-counting method to the Fisher method is verified with simulated data. Degraded facial images are employed to demonstrate the robustness of the photon-counting classifier in harsh environments. Four types of blurring point spread functions are applied to the test images in order to simulate long-distance acquisition. The results are compared with those of conventional Eigen face and Fisher face methods. The results indicate that Pc-LDA is better than conventional facial recognition techniques.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.49-60
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• 2019

Plants are very crucial for life on Earth. There is a wide variety of plant species available, and the number is increasing every year. Species knowledge is a necessity of various groups of society like foresters, farmers, environmentalists, educators for different work areas. This makes species identification an interdisciplinary interest. This, however, requires expert knowledge and becomes a tedious and challenging task for the non-experts who have very little or no knowledge of the typical botanical terms. However, the advancements in the fields of machine learning and computer vision can help make this task comparatively easier. There is still not a system so developed that can identify all the plant species, but some efforts have been made. In this study, we also have made such an attempt. Plant identification usually involves four steps, i.e. image acquisition, pre-processing, feature extraction, and classification. In this study, images from Swedish leaf dataset have been used, which contains 1,125 images of 15 different species. This is followed by pre-processing using Gaussian filtering mechanism and then texture and color features have been extracted. Finally, classification has been done using Multiclass-support vector machine, which achieved accuracy of nearly 93.26%, which we aim to enhance further.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.43 no.4
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• pp.256-261
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• 2017

Objectives: The concept of natural head position (NHP) was first introduced by Broca in 1862, and was described as a person's stable physiologic position "when a man is standing and his visual axis is horizontal." NHP has been used routinely for clinical examination; however, a patient's head position is random during cone-beam computed tomography (CBCT) acquisition. To solve this problem, we developed an accelerometer to record patients' NHP and reproduce them for CBCT images. In this study, we also tested the accuracy and reproducibility of our accelerometer. Materials and Methods: A total of 15 subjects participated in this study. We invented an accelerometer that measured acceleration on three axes and that could record roll and pitch calculations. Recorded roll and pitch data for each NHP were applied to a reoriented virtual image using three-dimensional (3D) imaging software. The data between the 3D models and the clinical photos were statistically analyzed side by side. Paired t-tests were used to statistically analyze the measurements. Results: The average difference in the angles between the clinical photograph and the 3D model was $0.04^{\circ}$ for roll and $0.29^{\circ}$ for pitch. The paired t-tests for the roll data (P=0.781) and the pitch data (P=0.169) showed no significant difference between the clinical photographs and the 3D model (P>0.05). Conclusion: By overcoming the limitations of previous NHP-recording techniques, our new method can accurately record patient NHP in a time-efficient manner. Our method can also accurately transfer the NHP to a 3D virtual model.