• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.525-528
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• 2019

Atmospheric particle removal is a challenging task and attacks wide interests in computer vision filed. In this paper, we proposed a single image snow removal framework based on deep residual networks. According to the fact that there are various snow sizes in a snow image, the inception module which consists of different filter kernels was adopted to extract multiple resolution features of the input snow image. Except the traditional mean square error loss, the perceptual loss and total variation loss were employed to generate more clean images. Experimental results on synthetic and realistic snow images indicated that the proposed method achieves superior performance in respect of visual perception and objective evaluation.

• Journal of Yeungnam Medical Science
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• v.24 no.1
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• pp.79-84
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• 2007

Sphenoid sinus aspergillosis is notorious for its serious complications, such as permanent cranial nerve deficits and possible death. The most common associated symptoms are headache, followed by visual changes, and cranial nerve palsies. Because of an insidious onset, frequently resulting in missed and delayed diagnosis, sphenoid sinus aspergillosis is a potentially lethal medical condition. We report a case of visual loss secondary to isolated sphenoid sinus aspergillosis. A 69-year-old man presented to our hospital with the complaint of headache. The headache started one year previously and was described as severe dull pain localized bilaterally to the temporo-orbital region. The patient took daily NSAIDs for the pain. The neurological examination was normal. The MRI of the brain showed a left sphenoid sinusitis. A transnasal endoscopic superior meatal sphenoidotomy was performed. Aspergillosis was confirmed after a surgical biopsy was obtained. The patient was discharged from hospital without antifungal therapy. One month later, the patient complained of headache and loss of vision bilaterally. The orbital MRI showed a left cavernous sinus and bilateral optic nerve invasion. The loss of visions was permanent. In our case, the diagnosis was delayed; antifungal agents were not administered after surgery and the patient lost his vision as a result. Therefore, early diagnosis and proper treatment are important. Although the treatment of an invasive type of aspergillus has not been established, surgical removal of a nidus and aggressive antifungal therapy are recommended.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.6
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• pp.989-996
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• 2008

Recently, a few types of fish-cage in open sea are researched. This fish-cage has to operate monitoring system for keeping an optimum living condition for fish. The most efficient monitoring system is WVMS(Wireless Vision Monitoring System) for fish-cage in open sea. WVMS should be able to transmit video signal and communicate with each controller. So. it needs to be based on WLAN(Wireless LAN) which has characteristic of higher transfer-rate, In this paper, we propose a structure of WVMS using WLAN equipments for maritime environment and prove its effectiveness. We present the propagation loss model of WVMS's communication channel. measured by field test, and discuss its validity compared with the predictive value based on the Friss propagation model and Plane earth reflection model. We present the number of frames that is received from WLAN modem connecting with underwater-camera in field test spots. As a result, we confirmed that proposed WVMS is suitable for maritime environment and it is possible to be applied to fish-cage in open sea on 'seogwipo'.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.511-513
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• 1998

This paper describes a full color stereoscopic video display system using a LCD shutter. Human apprehends the world with a natural stereo vision. The left eye sees through a slightly different perspective from the right eye; proposed vision system combines two images into a single image that has stereo depth. That is, when the left image is on the screen, the left shutter opens and the right shutter closes - and vice versa. The LCD shutter channels the left image to the left eye, and the right image to the right eye. The brain then fuses the stereo pair into a single high-resolution, flicker-free 3D image. The designed vision system is a real-time system that shows stereoscopic images without the loss of image information from video cameras.

• Clinical and Experimental Emergency Medicine
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• v.5 no.4
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• pp.278-281
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• 2018

Central retinal artery occlusion (CRAO) is considered an ophthalmologic emergency. The prognosis of this disease is very poor. Currently, there is no generally effective therapy available to treat CRAO. Hyperbaric oxygen therapy (HBOT) can increase the volume of oxygen delivered to the ischemic retinal tissue until spontaneous or assisted reperfusion occurs. We report the case of a patient who experienced sudden visual loss due to CRAO that was treated with HBOT. The patient was an 81-year-old woman who presented with CRAO in her right eye (OD). She exhibited "hand motion" visual acuity before treatment. She underwent three sessions of HBOT at a pressure of 2.8 atmospheres absolute, performed over 3 days. After 4 days in hospital, her visual acuity improved to 0.4 (OD) for far vision and 0.5 (OD) for near vision. Her vision was stable without the supply of oxygen; therefore, she was discharged.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.67-72
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• 2023

In the past decade, Autonomous Vehicle Systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. The fusion of light detection and ranging (LiDAR) and camera data in real-time is known to be a crucial process in many applications, such as in autonomous driving, industrial automation and robotics. Especially in the case of autonomous vehicles, the efficient fusion of data from these two types of sensors is important to enabling the depth of objects as well as the classification of objects at short and long distances. This paper presents classification of objects using CNN based vision and Light Detection and Ranging (LIDAR) fusion in autonomous vehicles in the environment. This method is based on convolutional neural network (CNN) and image up sampling theory. By creating a point cloud of LIDAR data up sampling and converting into pixel-level depth information, depth information is connected with Red Green Blue data and fed into a deep CNN. The proposed method can obtain informative feature representation for object classification in autonomous vehicle environment using the integrated vision and LIDAR data. This method is adopted to guarantee both object classification accuracy and minimal loss. Experimental results show the effectiveness and efficiency of presented approach for objects classification.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.22 no.1
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• pp.145-151
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• 2014

Not only in Korea, but also in other countries, most helicopter accidents occur at low altitude. Analysis on accidents data collected both in Korea and other countries has brought two conclusions. Firstly, helicopter operations during the night hours carries a high risk. Secondly, the most common cause of night hour operations is loss of control due to the flight illusion. As an operation relying on a night vision in particular has a very high accident hazard, accompanying instruments such as NVG are ought to be provided. Hence, a thorough preparation and inspection on missions for night forest fire extinguishing should be required and perfect guidelines/road maps and enough training programs for the operation should be provided before the engagement in missions.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1142-1149
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• 2018

Recently, computer vision application is increasing by using CNN which is one of the deep learning algorithms. However, CNN does not provide perfect classification performance due to gradient vanishing problem. Most of CNN algorithms use an activation function called ReLU to mitigate the gradient vanishing problem. In this study, four activation functions that can replace ReLU were applied to four different structural networks. Experimental results show that ReLU has the lowest performance in accuracy, loss rate, and speed of initial learning convergence from 20 experiments. It is concluded that the optimal activation function varied from network to network but the four activation functions were higher than ReLU.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2006.11b
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• pp.155-166
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• 2006

Application of industry roadmap is to propose the vision on the future through correction and supplement of various environment change(nation competition power uplift, origin technology development, long and short-term vision establishment, R&D investment improvement, co-work increase etc). Now, in Jeju region, Roles of traditional medicine is needed by environmental needs(competition loss of Jeju industry, limitation of tourism industry etc). Thus, the purpose of this study is to contribute to region economy activity by building the TRM of foods field in traditional oriental medicine industry through the analysis of earlier TRM paper and materials.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.632-633
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• 2023

Diabetic retinopathy (DR) is a prevalent complication of diabetes that can lead to vision impairment if not diagnosed and treated promptly. This study presents a novel approach for the automated grading of diabetic retinopathy in ultra-widefield fundus images (UFI) using deep learning techniques. We propose a method that involves preprocessing UFIs by cropping the central region to focus on the most relevant information. Subsequently, we employ state-of-the-art deep learning models, including ResNet50, EfficientNetB3, and Xception, to perform DR grade classification. Our extensive experiments reveal that Xception outperforms the other models in terms of classification accuracy, sensitivity, and specificity. his research contributes to the development of automated tools that can assist healthcare professionals in early DR detection and management, thereby reducing the risk of vision loss among diabetic patients.