• Maxillofacial Plastic and Reconstructive Surgery
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• v.42
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• pp.27.1-27.9
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• 2020

Background: Mandibular setback surgery can change the position of the mandible which improves occlusion and facial profile. Surgical movement of the mandible affects the base of the tongue, hyoid bone, and associated tissues, resulting in changes in the pharyngeal airway space. The aim of this study was to analyze the 3-dimensional (3D) changes in the hyoid bone and tongue positions and oropharyngeal airway space after mandibular setback surgery. Methods: A total of 30 pairs of cone-beam computed tomography (CBCT) images taken before and 1 month after surgery were analyzed by measuring changes in the hyoid bone and tongue positions and oropharyngeal airway space. The CBCT images were reoriented using InVivo 5.3 software (Anatomage, San Jose, USA) and landmarks were assigned to establish coordinates in a three-dimensional plane. The mean age of the patients was 21.7 years and the mean amount of mandibular setback was 5.94 mm measured from the B-point. Results: The hyoid bone showed significant posterior and inferior displacement (P < 0.001, P < 0.001, respectively). Significant superior and posterior movements of the tongue were observed (P < 0.05, P < 0.05, respectively). Regarding the velopharyngeal and glossopharyngeal spaces, there were significant reductions in the volume and minimal cross-sectional area (P < 0.001). The anteroposterior and transverse widths of the minimal cross-sectional area were decreased (P < 0.001, P < 0.001, respectively). In addition, the amount of mandibular setback positively correlated with the amount of posterior and inferior movement of the hyoid bone (P < 0.05, P < 0.05, respectively). Conclusion: There were significant changes in the hyoid bone, tongue, and airway space after mandibular setback surgery.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.78.1-78.1
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• 2019

There are a huge number of faint objects that have not been observed due to the lack of large and deep surveys. In this study, we demonstrate that a deep learning approach can produce a better quality deep image from a single pass imaging so that could be an alternative of conventional image stacking technique or the expensive large and deep surveys. Using data from the Sloan Digital Sky Survey (SDSS) stripe 82 which provide repeatedly scanned imaging data, a training data set is constructed: g-, r-, and i-band images of single pass data as an input and r-band co-added image as a target. Out of 151 SDSS fields that have been repeatedly scanned 34 times, 120 fields were used for training and 31 fields for validation. The size of a frame selected for the training is 1k by 1k pixel scale. To avoid possible problems caused by the small number of training sets, frames are randomly selected within that field each iteration of training. Every 5000 iterations of training, the performance were evaluated with RMSE, peak signal-to-noise ratio which is given on logarithmic scale, structural symmetry index (SSIM) and difference in SSIM. We continued the training until a GAN model with the best performance is found. We apply the best GAN-model to NGC0941 located in SDSS stripe 82. By comparing the radial surface brightness and photometry error of images, we found the possibility that this technique could generate a deep image with statistics close to the stacked image from a single-pass image.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.403-406
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• 2005

The purpose of this paper is to propose ways to remove false color noise (FCN) generated during de-mosaicing on RGB Bayer pattern images. In case of images sensors adapting Bayer pattern color filters array (CFA), de-mosaicing is conducted to recover the RGB color data in single pixels. Here, FCN phenomena would occur where there is clearer silhouette or contrast of colors. The FCN phenomena found during de-mosaicking process appears locally in the edges inside the image and the proposed method of eliminating this is to convert RGB color space to YCbCr space to conduct smoothing process. Moreover, for edges where different colors come together, censoring based smoothing technique is proposed as a way to minimize color blurring effect.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.476-479
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• 2003

The Rough transform (HT) is often used for extracting global features in binary images, for example curve and line segments, from local features such as single pixels. The HT is useful due to its insensitivity to missing edge points and occlusions, and robustness in noisy images. However, it possesses some disadvantages, such as time and memory consumption due to the number of input data and the selection of an optimal and efficient resolution of the accumulator space can be difficult. Another problem of the HT is in the difficulty of peak detection due to the discrete nature of the image space and the round off in estimation. In order to resolve the problem mentioned above, a possibilistic C-means approach to clustering [1] is used to cluster neighboring peaks. Several experimental results are given.

• Journal of the Korea Fashion and Costume Design Association
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• v.1 no.1
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• pp.35-51
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• 1999

This study was aimed to analyze how images of information society were embodied in contemporary fashion design. To find out images of information society expressed in contemporary fashion design, this paper reviewed the characteristics of information society, and, found out six categories of contemporary fashion design in which the information society was expressed. Cyber environment causes techno style clothing which is characterized by the metallic or lustrous material and mechanical message. And interest in new materials increased significantly. Time-space compression makes the meaning of time and space change, therefore the past, present and future are coexist in the contemporary fashion design. The boundaries has loosed, so, heterogeneous materials and colors are used in fashion design all together. Variety and plurality affect expression of individuality and self-concept. Flexibility increased, so, soft and pliable clothing increased, too. Finally, quality of life is considered as more important value than quantity, so, luxurious look and zen style appear. Also, minimalism which is characterized as simplicity and purity affects the contemporary fashion design.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.299-302
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of$.$10 persons show that the proposed method yields high recognition rates.

• Proceedings of the KIEE Conference
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• 2007.10c
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• pp.229-231
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• 2007

The CCTV, closed circuit television, system is the most popular method to monitor some specific area. The CCTV-based monitoring system is composed of a lot of cameras installed the areas, and monitors to display the vision through the cameras. However, these systems have limitations to prevent some problems or to cope with the problems promptly, because they can carry out only the function that shows us the analogue images of the cameras. Especially, urban transit service area is the space where many people crowd in all at the same time and the space is not only wide but also distributed sporadically. This paper presents the efficient plan for video-based monitoring system to monitor urban transit service area. To build the efficient monitoring system, it is necessary to devide the monitoring area to appropriate sectors that should be composed to be displayed at a time. If the proposed method is used to construct the video-based monitoring system, the operating officers in the urban transit have the more direct and real images.

• Journal of Biomedical Engineering Research
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• v.3 no.1
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• pp.35-42
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• 1982

The objective of this phantom study is to develop a digital method for improving the lateral resolution of B-scan ultrasonographic images irs medical application of ultrasound. By utilizing a discrete state-space modeling approach and Kalman-Buch method for analysis of the transducer's beam profile and the measurement and sampling noise, a stable recursive restoration of the object image was obtained for improved lateral resolution. The point spread function (PSF) was measured for the reflective signals after scanning the small pins located along the depth of interest. One major advantage of the present recursive scheme over the transform method is in its applicability for the space-variant imaging, such as in the case of the rotational movement of transducer.

• Proceedings of the KSRS Conference
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• v.2
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• pp.946-949
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• 2006

With repeated coverage, spaceborne SAR (Synthetic Aperture Radar) instruments provide the most efficient means to monitor and study the changes in important elements of the marine environment. Due to highresolution of SAR data, the coverage of SAR sensor is always limited, especially for a repeat cycle. With more SAR sensors from various satellites, new data products such as ocean surface drift can be derived when two SARs' tracks overlap in a short time over coastal areas. Currently, there are two SAR sensors on different satellites with almost the exactly same path. That is, ERS-2 is following ENVISAT with a 30-minutes delay, which will be a good timing for ocean mesosclae feature tracking. For another application, a mystery ship near a big eddy with strong ship wake has been tracked between ERS-2 and ENVISAT SAR images to estimate its ship speed.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.421-424
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• 1999

Automatic human face detection in a complex background is one of the difficult problems In this paper. we propose an effective automatic face detection system that can locate the face region in natural scene images when the system is used as a pre-processor of a face recog- nition system. We use two natural and powerful visual cues, the color and the human head shape. The outline of the human head can be generally described as being roughly elliptic in nature. In the first step of the proposed system, we have tried the approach of fitting the best Possible ellipse to the outline of the head In the next step, the method based on the human skin color space by selecting flesh tone regions in color images and histogramming their r(＝R/(R＋G＋B)) and g(＝G/R＋G＋B)) values. According to our experiment. the proposed system shows robust location results