• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.38-47
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• 2006

Conventional classification method uses a single classifier based on shape or motion feature. However this method exhibits a weakness if naively used since the classification performance is highly sensitive to the accuracy of moving region to be detected. The detection accuracy, in turn, depends on the condition of the image background. In this paper, we propose to resolve the drawback and thus strengthen the classification reliability by employing a Bayesian decision fusion and by optimally combining the decisions of three classifiers. The first classifier is based on shape information obtained from Fourier descriptors while the second is based on the shape information obtained from image gradients. The third classifier uses motion information. Our experimental results on the classification Performance of human and vehicle with a static camera in various directions confirm a significant improvement and indicate the superiority of the proposed decision fusion method compared to the conventional Majority Voting and Weight Average Score approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.4
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• pp.161-168
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• 2018

High blood pressure is main today's adult disease and existing blood pressure gauge is not possible for real-time blood pressure measurement and remote monitoring. But real-time blood pressure monitoring u-healthcare system makes effect health management. In my paper, for monitoring real-time blood pressure, an architecture of real-time blood pressure monitoring system which consisted of wrist type-blood pressure measurement, smart-phone and u-healthcare server is presented. And the analog circuit architecture which is major core function for pulse wave detection and digital hardware architecture for wrist type-blood pressure measurement is presented. Also for software development to operate this hardware system, UML analysis method and flowcharts and screen design for this software design are showed. Therefore such design method in my paper is expected to be useful for real-time blood pressure monitoring u-healthcare system implementation.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.349-356
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• 2020

This paper introduces the implementation of a driver's condition warning system using eye aspect ratio to prevent a car accident. The proposed driver's condition warning system using eye aspect ratio consists of a camera, that is required to detect eyes, the Raspberrypie that processes information on eyes from the camera, buzzer and vibrator, that are required to warn the driver. In order to detect and recognize driver's eyes, the histogram of oriented gradients and face landmark estimation based on deep-learning are used. Initially the system calculates the eye aspect ratio of the driver from 6 coordinates around the eye and then gets each eye aspect ratio values when the eyes are opened and closed. These two different eye aspect ratio values are used to calculate the threshold value that is necessary to determine the eye state. Because the threshold value is adaptively determined according to the driver's eye aspect ratio, the system can use the optimal threshold value to determine the driver's condition. In addition, the system synthesizes an input image from the gray-scaled and LAB model images to operate in low lighting conditions.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.54.3-55
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• 2016

DEEP-South Scheduling and Data reduction System (DS SDS) consists of two separate software subsystems: Headquarters (HQ) at Korea Astronomy and Space Science Institute (KASI), and SDS Data Reduction (DR) at Korea Institute of Science and Technology Information (KISTI). HQ runs the DS Scheduling System (DSS), DS database (DB), and Control and Monitoring (C&M) designed to monitor and manage overall SDS actions. DR hosts the Moving Object Detection Program (MODP), Asteroid Spin Analysis Package (ASAP) and Data Reduction Control & Monitor (DRCM). MODP and ASAP conduct data analysis while DRCM checks if they are working properly. The functions of SDS is three-fold: (1) DSS plans schedules for three KMTNet stations, (2) DR performs data analysis, and (3) C&M checks whether DSS and DR function properly. DSS prepares a list of targets, aids users in deciding observation priority, calculates exposure time, schedules nightly runs, and archives data using Database Management System (DBMS). MODP is designed to discover moving objects on CCD images, while ASAP performs photometry and reconstructs their lightcurves. Based on ASAP lightcurve analysis and/or MODP astrometry, DSS schedules follow-up runs to be conducted with a part of, or three KMTNet telescopes.

• Journal of IKEEE
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• v.24 no.1
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• pp.97-105
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• 2020

A thermal imaging sensor receives the radiating energy from the target and the background, so it has been widely used for detection, tracking, and classification of targets at night for military purpose. In recognizing the target automatically using thermal images, if the correct edges of object are used then it can generate the classification results with high accuracy. However since the thermal images have lower spatial resolution and more blurred edges than color images, the accuracy of the classification using thermal images can be decreased. In this paper, to overcome this problem, a new hierarchical classifier using both shape and local features based on the segmentation reliabilities, and the class/pose updating method for vehicle classification are proposed. The proposed classification method was validated using thermal video sequences of more than 20,000 images which include four types of military vehicles - main battle tank, armored personnel carrier, military truck, and estate car. The experiment results showed that the proposed method outperformed the state-of-the-arts methods in classification accuracy.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.3
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• pp.667-678
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• 2016

Due to a development of the cable/wireless network infra, the traffic as big as unable to compare with the past is being served through the internet, the traffic is increasing every year following the change of the network paradigm such as the object internet, especially the traffic of about 1.6 zettabyte is expected to be distributed through the network in 2018. As the network traffic increases, the performance of the security infra is developing together to deal with the bulk terabyte traffic in the security equipment, and is generating hundreds of thousands of security events every day such as hacking attempt and the malignant code. Efficiently analyzing and responding to an event on the attack attempt detected by various kinds of security equipment of company is one of very important assignments for providing a stable internet service. This study attempts to overcome the limit of study such as the detection of Tor network traffic using the existing low-latency by classifying the anonymous network by means of the suggested algorithm about the event detected in the security infra.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.58-61
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• 2018

In Korea, current medical technology is the highest level in the world. As a result, many doctors have specialized knowledge of various disorders or diseases, and are proceeding in a better way. With such high medical technology, it is possible to increase the probability of success of surgery to provide high reliability to patients. Rehabilitation is also a form of medical treatment that reduces the side effects that occur after surgery that is done for quick cure. However, the situation in this section is slightly different. There are moves to develop rehabilitation devices and operations, but most of them are now dependent on foreign technology. Rehabilitation therapy, which we commonly know, is dominated by behavior. However, it is also a kind of rehabilitation to find out how much the patient's symptoms are improved or recovered. In this paper, we have studied the development of a program by using the Deeplearning method in order to detect the problem of the food swallowing operation by the severity.

• Journal of Bio-Environment Control
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• v.20 no.4
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• pp.277-282
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• 2011

We carried out shape recognition. We found out cucumber's feature shape by means of neural network and back propagation algorithm. We developed an algorithm which finds object position and shape in real image and we gained following conclusion as a result. It was processed for feature shape extraction of cucumber to detect automatic. The output pattern rates of the miss-detected objects was 0.1~4.2% in the output pattern which was recognized as cucumber. We were gained output pattern according to image resolution $445{\times}363$, $501{\times}391$, $450{\times}271$, $297{\times}421$. It was appeared that no change was detected. When learning pattern was increased to 25, miss-detection ratio was 16.02%, and when learning pattern had 2 pattern, it didn't detect 8 cucumber in 40 images.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.7 no.4
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• pp.139-146
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• 1987

The 3-dimensional deformation measurement by the terrestrial photogrammetry is consist of 3-dimensional coordinates computation, displaced point detection and deformation estimation of object targets. In this study, at the first step of deformation analysis, the variation of the variance-covariance matrix for the exterior orientation elements was analyzed by the increment of the ground control points and the photos in the Bundle adjustment. And then, to give the constraints for improving accuracy of ground control points, the concept of Free-Network adjustment was applied to Bundle adjustment. As a result, we knew that it was desired in the accuracy and the economy, the observation time when the numbers of ground control point and photo were respectively 6 points and 3 photos. In addition, in the case of applying the concept of Free Network adjustment in Bundle adjutment, it was desirable that the space distance for the constraints is distributed outside.

• Journal of Convergence for Information Technology
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• v.7 no.6
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• pp.143-151
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• 2017

The generalized Hough transform(GHough) can be used effectively for detecting and extracting an arbitrary-shaped 2-D model in an input image. However, the main drawbacks of the GHough are both heavy computation and an excessive storage requirement. Thus, most of the researches so far have focused on reducing both the time and space requirement of the GHough. But it is still not clear how well their improved algorithms will perform under various noise in an input image. Thus, this paper proposes a new framework that can measure the performance of the GHough quantitatively. For this purpose, we view the GHough as a detector in signal detection theory and the ROC curve will be used to specify the performance of the GHough. Finally, we show that we can evaluate the GHough under various noise conditions in an input image.