• Smart Structures and Systems
• /
• v.15 no.6
• /
• pp.1583-1600
• /
• 2015

When natural disasters occur, including earthquakes, tsunamis, and debris flows, they are often accompanied by various types of damages such as the collapse of buildings, broken bridges and roads, and the destruction of natural scenery. Natural disaster detection and warning is an important issue which could help to reduce the incidence of serious damage to life and property as well as provide information for search and rescue afterwards. In this study, we propose a novel computer vision technique for debris flow detection which is feature-based that can be used to construct a debris flow event warning system. The landscape is composed of various elements, including trees, rocks, and buildings which are characterized by their features, shapes, positions, and colors. Unlike the traditional methods, our analysis relies on changes in the natural scenery which influence changes to the features. The "background module" and "monitoring module" procedures are designed and used to detect debris flows and construct an event warning system. The multi-criteria decision-making method used to construct an event warring system includes gradient information and the percentage of variation of the features. To prove the feasibility of the proposed method for detecting debris flows, some real cases of debris flows are analyzed. The natural environment is simulated and an event warning system is constructed to warn of debris flows. Debris flows are successfully detected using these two procedures, by analyzing the variation in the detected features and the matched feature. The feasibility of the event warning system is proven using the simulation method. Therefore, the feature based method is found to be useful for detecting debris flows and the event warning system is triggered when debris flows occur.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.6
• /
• pp.621-628
• /
• 2015

This paper describes an algorithm for detecting vehicles detection in real time. The proposed algorithm has the technique based on computer vision and image processing. In real, complex environment such as one with road traffic, many algorithms have great difficulty such as low detection rate and increasing computational time due to complex backgrounds and rapid changes. To overcome this problem in this paper, the proposed algorithm consists of the following methods. First, to effectively separate the candidate regions, we use vertical and horizontal edge information, and shadow values from input image sequences. Second, we extracts features by using HOG from the selected candidate regions. Finally, this paper uses the OS fuzzy-ELM based on SLFN to classify the extracted features. The experimental results show that the proposed method perform well for detecting vehicles and improves the accuracy and the computational time of detecting.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.4
• /
• pp.1-10
• /
• 2022

Attempts to interpret human perspectives using computer vision have been developed in various fields. In this paper, we propose a method for evaluating the walking environment through semantic segmentation results of images from road images. First, the Kakao Map API was used to collect road images, and four-way images were collected from about 50,000 points in JeonJu. 20% of the collected images build datasets through crowdsourcing-based paired comparisons, and train various regression models using paired comparison data. In order to derive the walkability score of the image data, the ranking score is calculated using the Trueskill algorithm, which is a ranking algorithm, and the walkability and analysis using various regression models are performed using the constructed data. Through this study, it is shown that the walkability of Jeonju can be evaluated and scores can be derived through the correlation between pixel distribution classification information rather than human vision.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.133-149
• /
• 2021

The purpose of this study is to explore the role of information systems in the implementation of knowledge management, at King Abdul-Aziz University (KAU) in Jeddah, by highlighting the importance of information systems and their implementation of the knowledge processes. The researcher used the case-study method to explore the importance of information systems in supporting the implementation of knowledge management at the university. Moreover, the study has used the questionnaire as a tool for collecting information and obtaining feedbacks from the administrators at the university, and a random sample was chosen to identify the study community. The study resulted that there is a statistical indication of the importance and degree of the use of electronic systems in the university by the administrators. The study sample members believe that the university is keen to provide information systems, where systems analyze data and convert them into knowledge information that benefits the senior management at the university. Members of the study sample emphasize the importance of electronic information systems at the university, which in turn saves time and effort in extracting information, reports, statistics and providing them easily to senior management. The study also concluded with some recommendations, such as emphasizing the importance of knowledge management as one of its top priorities, spreading the knowledge culture, instilling a vision of knowledge among individuals, and emphasizing the importance of information systems.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12
• /
• pp.93-100
• /
• 2021

Satellite communication for high altitude platform stations (HAPS) and geostationary orbit (GEO) systems suffers from sand and dust (SANDU) storms in desert and arid regions. The focus of this paper is to propose common relations between HAPS and GEO for the atmospheric impairments affecting the satellite communication networks operating above Ku-band crossing the propagation path. A double phase three-dimensional relationship for HAPS and GEO systems is then presented. The comparison model present the analysis of atmospheric attenuation with specific focus on sand and dust based on particular size, visibility, adding gas effects for different frequency, and propagation angle to provide systems' operations with a predicted vision of satellite parameters' values. Thus, the proposed system provides wide range of selecting applicable parameters, under different weather conditions, in order to achieve better SNR for satellite communication.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.41 no.4
• /
• pp.39-43
• /
• 2004

A typical theme of active vision systems is gaze-fixing of a camera. Here gaze-fixing of a camera means by steering orientation of a camera so that a given point on the object is always at the center of the image. For this we need to combine a function to analyze image data and a function to control orientation of a camera. This paper presents an algorithm for gaze-fixing of a camera where image analysis and orientation control are designed in a frame. At this time, for avoiding difficulties in implementing and aiming for real-time applications we design the algorithm to be a simple closed-form without using my information related to calibration of the camera or structure estimation.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.37 no.12
• /
• pp.1209-1216
• /
• 2009

This paper presents the pose estimation of a small UAV utilizing visual information from low cost cameras installed indoor. To overcome the limitation of the outside flight experiment, the indoor flight test environment based on multi-camera systems is proposed. Computer vision algorithms for the proposed system include camera calibration, color marker detection, and pose estimation. The well-known extended Kalman filter is used to obtain an accurate position and pose estimation for the small UAV. This paper finishes with several experiment results illustrating the performance and properties of the proposed vision-based indoor flight test environment.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.17 no.5
• /
• pp.173-187
• /
• 2018

This paper proposes a computer vision and deep learning-based technique for surveillance camera system for vehicle counting as one part of parking lot management system. We applied the You Only Look Once version 2 (YOLOv2) detector and come up with a deep convolutional neural network (CNN) based on YOLOv2 with a different architecture and two models. The effectiveness of the proposed architecture is illustrated using a publicly available Udacity's self-driving-car datasets. After training and testing, our proposed architecture with new models is able to obtain 64.30% mean average precision which is a better performance compare to the original architecture (YOLOv2) that achieved only 47.89% mean average precision on the detection of car, truck, and pedestrian.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.4
• /
• pp.47-57
• /
• 2009

Event detection and recognition is an active and challenging topic recent in Computer Vision. This paper describes a new method for recognizing events caused by human motion from video sequences in an office environment. The proposed approach analyzes human motions using Motion History Image (MHI) sequences, and is invariant to body shapes. types or colors of clothes and positions of target objects. The proposed method has two advantages; one is thant the proposed method is less sensitive to illumination changes comparing with the method using color information of objects of interest, and the other is scale invariance comparing with the method using a prior knowledge like appearances or shapes of objects of interest. Combined with edge detection, geometrical characteristics of the human shape in the MHI sequences are considered as the features. An advantage of the proposed method is that the event detection framework is easy to extend by inserting the descriptions of events. In addition, the proposed method is the core technology for event detection systems based on context-aware computing as well as surveillance systems based on computer vision techniques.

• Journal of Korea Multimedia Society
• /
• v.8 no.6
• /
• pp.768-775
• /
• 2005

For sign languages are main communication means among hearing-impaired people, there are communication difficulties between speaking-oriented people and sign-language-oriented people. Automated sign-language recognition may resolve these communication problems. In sign languages, finger spelling is used to spell names and words that are not listed in the dictionary. There have been research activities for gesture and posture recognition using glove-based devices. However, these devices are often expensive, cumbersome, and inadequate for recognizing elaborate finger spelling. Use of colored patches or gloves also cause uneasiness. In this paper, a vision-based finger spelling recognition system is introduced. In our method, captured hand region images were separated from the background using a skin detection algorithm assuming that there are no skin-colored objects in the background. Then, hand postures were recognized using a two-dimensional grid analysis method. Our recognition system is not sensitive to the size or the rotation of the input posture images. By optimizing the weights of the posture features using a genetic algorithm, our system achieved high accuracy that matches other systems using devices or colored gloves. We applied our posture recognition system for detecting Korean Sign Language, achieving better than $93\%$ accuracy.