• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.171-178
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• 2010

The surveillance system in general, has been sufficiently studied in the field of wireless semiconductor using basic sensors and its study of image surveillance system mainly using camera as a sensor has especially been fully implemented. In this paper, we propose 'Intelligent Image Detection System' used by image object identification technique based on the result analysis of various researches. This 'Intelligent Image Detection System' can easily trace and judge before and after a particular incident and ensure affirmative evidence and numerous relative information. Therefore, the 'Intelligent Image Detection System' proposed in this paper can be effectively used in the lived society such as traffic management, disaster alarm system and etc.

• International journal of advanced smart convergence
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• v.1 no.2
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• pp.39-42
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• 2012

This paper proposed a method that detects emergency situations in a video stream using MHI (Motion History Image) and template matching for a video-based intelligent security system. The proposed method creates a MHI of each human object through image processing technique such as background removing based on GMM (Gaussian Mixture Model), labeling and accumulating the foreground images, then the obtained MHI is compared with the existing MHI templates for detecting an emergency situation. To evaluate the proposed emergency detection method, a set of experiments on the dataset of video clips captured from a security camera has been conducted. And we successfully detected emergency situations using the proposed method. In addition, the implemented system also provides MMS (Multimedia Message Service) so that a security manager can deal with the emergency situation appropriately.

• Journal of the Institute of Convergence Signal Processing
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• v.17 no.1
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• pp.32-39
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• 2016

Intelligent Transport Systems (Intelligent Transportation System: ITS) is a system for inducing a flow of ideal car for using the most advanced technology, it is determined the status of the road, and take appropriate action. In order to be measured at various time points, and is managed, the information about the traffic situation is used image using a computer mainly. The image processing using a computer, it is an easy way to collect parameters of the various traffic in real time, technology has developed more and more. Vehicle detection of transport parameters of intelligent transportation system is a very important technology basically. Therefore, technology detection method using car background images and the contour line extraction method using an edge is used, however, problems have been raised on the accuracy of the detection rate.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.283-286
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• 2001

Based on a fuzzy system representation of gray scale images, we derive an edge detection algorithm whose convolution kernel is different from the known kernels such as those of Roberts', Prewitt's or Sobel's gradient. Our fuzzy system representation is an exact representation of the bicubic spline function which represents the gray scale image approximately. Hence the fuzzy system is a continuous function and it provides a natural way to define the gradient and the Laplacian operator. We show that the gradient at grid points can be evaluated by taking the convolution of the image with a 3 3 kernel. We also show that our gradient coupled with the approximate value of the continuous function generates an edge detection method which creates edge images clearer than those by other methods. A few examples of applying our methods are included.

• Journal of the Korea Safety Management & Science
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• v.17 no.2
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• pp.47-54
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• 2015

To prevent safety hazards in dangerous working area, we have proposed an intelligent image analysis system. Six common patterns of safety violations of workers' are defined and its motion detection algorithms are developed for alarm to CCTV monitoring system. Developed algorithms are implemented at 195 dangerous areas such as chemical and gas treated room. The results of violated motion detection ratio by developed system shows 94.95% of true positive cases, and 0.21% of false positive cases from all 587,645 event cases in one month implementation period. In the period, it is observed that the number of safety rule violations and the following accidents are decreased.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.27-31
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• 2005

Vehicle detector system based on image processing technology is a significant domain of ITS (Intelligent Transportation System) applications due to its advantages such as low installation cost and it does not obstruct traffic during the installation of vehicle detection systems on the road[1]. In this paper, we propose architecture for vehicle detection by using image processing. The architecture consists of two main parts such as an image processing part, using high speed FPGA, decision and calculation part using CPU. The CPU part takes care of total system control and synthetic decision of vehicle detection. The FPGA part assumes charge of input and output image using video encoder and decoder, image classification and image memory control.

• Journal of IKEEE
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• v.19 no.1
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• pp.58-63
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• 2015

This study suggested the mobile intelligent management system based on the cloud service. The mobile intelligent management system are composed of cloud server, middleware and sensor networks. Each modules are controlled on mobile environment and observed operating status of each apparatus for environment. In this pater, the image-based moving detection algorithm applied in order to detect an intruder and average 12.3% are measured in moving detection experiments. it was confirmed the validity of the security device.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.3
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• pp.68-74
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• 2001

A lane detection based on a road model or feature all needs correct acquirement of information on the lane in an image. It is inefficient to implement a lane detection algorithm through the full range of an image when it is applied to a real road in real time because of the calculating time. This paper defines two (other proper terms including"modes") for detecting lanes on a road. First is searching mode that is searching the lane without any prior information of a road. Second is recognition mode, which is able to reduce the size and change the position of a searching range by predicting the position of a lane through the acquired information in a previous frame. It allows to extract accurately and efficiently the edge candidate points of a lane without any unnecessary searching. By means of inverse perspective transform which removes the perspective effect on the edge candidate points, we transform the edge candidate information in the Image Coordinate System(ICS) into the plan-view image in the World Coordinate System(WCS). We define a linear approximation filter and remove faulty edge candidate points by using it. This paper aims at approximating more correctly the lane of an actual road by applying the least-mean square method with the fault-removed edge information for curve fitting.e fitting.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.429-431
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• 2008

생체 인식 기술이 사회 전반에 걸쳐 다양하게 사용되어짐에 따라 인식기술 중의 하나인 Face Recognition 은 하루가 다르게 발전하고 있다. 하지만, 그와 함께 해킹방법도 다양화되어지고 있다. 그럼에도 불구하고, 위, 변조 영상 판별(Liveness Detection) 분야에 관련된 연구들은 초기 단계를 벗어나지 못하고 있다. 본 논문에서는 적외선 영상을 이용하여 동공부분의 반사 정도를 이용하여 실제 이미지와 위, 변조 이미지를 판별하는 방법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1597-1610
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• 2022

At present, the main method of high-speed train chassis detection is using computer vision technology to extract keypoints from two related chassis images firstly, then matching these keypoints to find the pixel-level correspondence between these two images, finally, detection and other steps are performed. The quality and accuracy of image matching are very important for subsequent defect detection. Current traditional matching methods are difficult to meet the actual requirements for the generalization of complex scenes such as weather, illumination, and seasonal changes. Therefore, it is of great significance to study the high-speed train image matching method based on deep learning. This paper establishes a high-speed train chassis image matching dataset, including random perspective changes and optical distortion, to simulate the changes in the actual working environment of the high-speed rail system as much as possible. This work designs a convolutional neural network to intensively extract keypoints, so as to alleviate the problems of current methods. With multi-level features, on the one hand, the network restores low-level details, thereby improving the localization accuracy of keypoints, on the other hand, the network can generate robust keypoint descriptors. Detailed experiments show the huge improvement of the proposed network over traditional methods.