• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.797-804
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• 2003

Scene change detection algorithm, which is very important preprocessing technique for video indexing and retrieval and determines the performance of video database system, is being studied widely. In this paper, we propose a more effective abrupt scene change detection, which is robust to large motion, sudden change of light and successive abrupt shot transitions rapidly. And we also propose a new gradual scene change detection algorithm, which can detect dissolve, and fade in/out precisely. Furthermore, we also propose a representative frame extraction algorithm which performs content-based video summary by novel DCT DC image buffering technique and accumulative histogram intersection measure (AHIM).

• Journal of KIBIM
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• v.7 no.1
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• pp.18-27
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• 2017

Although Industry Foundation Classes (IFC) provide standards for exchanging Building Information Modeling (BIM) data, authoring tools still require manual mapping between BIM entities and IFC classes. This leads to errors and omissions, which results in corrupted data exchanges that are unreliable and thus compromise the validity of IFC. This research explored precedent work by Krijnen and Tamke, who suggested ways to automate the mapping of IFC classes using a machine learning technique, namely anomaly detection. The technique incorporates geometric features of individual components to find outliers among entities in identical IFC classes. This research primarily focused on applying this approach on two architectural BIM models and determining its feasibility as well as limitations. Results indicated that the approach, while effective, misclassified outliers when an IFC class had several dissimilar entities. Another issue was the lack of entities for some specific IFC classes that prohibited the anomaly detection from comparing differences. Future research to improve these issues include the addition of geometric features, using novelty detection and the inclusion of a probabilistic graph model, to improve classification accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.615-617
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• 1998

This paper proposes a new segmentation method of human races from grey scale images with clutter using a racial template and elliptical structure of the human head. Face detection technique can be applied in many areas of image processing such as face recognition, composition and computer graphics. Until now, many researches about face detection have been conducted, and applications in more complicated conditions are increasing. The general case is more in a complicated background than in a simple one, and a image with not only one face. Research and development of face detection in such a general case are growing rapidly, and the necessity for that is increasing continuously. Sirohey proposed a face detection method using linearized elliptical equation. The method designed in this paper is improved to be applicable even in the more general cases like where the face is much smaller than the image size and with many faces in one image using template matching and elliptic fitting technique.

• Smart Structures and Systems
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• v.4 no.6
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• pp.779-794
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• 2008

Observer-based fault detection and isolation (FDI) filter design method is a model-based method. By carefully choosing the observer gain, the residual outputs can be projected onto different independent subspaces. Each subspace corresponds to the monitored structural element so that the projected residual will be nonzero when the associated structural element is damaged and zero when there is no damage. The key point of detection filter design is how to find an appropriate observer gain. This problem can be interpreted in a geometric framework and is found to be equivalent to the problem of finding a decentralized static output feedback gain. But, it is still a challenging task to find the decentralized controller by either analytical or numerical methods because its solution set is, generally, non-convex. In this paper, the concept of detection filter and iterative LMI technique for decentralized controller design are combined to develop an algorithm to compute the observer gain. It can be used to monitor structural element state: healthy or damaged. The simulation results show that the developed method can successfully identify structural damages.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1183-1188
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• 2011

The novel metal position detection method is proposed where conventional techniques, in high temperature, moisture and particle environment, are not able to be applied. It is known that electronic devices, utilizing microwave, ultrasonic or optical technique, are hard to apply for sensing application where temperature is exceeding above 300 degree centigrade. Metal position detection technique, which was consisted with passive elements facing hot sensing surface, utilizing electromagnetic wave was investigated, and the metal detection sensitivity was measured by varying sensor frequency and sensing distance. Measurement result in laboratory test set-up showed position measurement resolution up to 1mm, when distance between two sensing elements were 500mm, and possibility to measure position of hot metal sheet having very high surface temperature.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.721-728
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• 2005

This paper suggests effective object detection system for moving objects with specified color and motion information. The proposed detection system includes the object extraction and definition process which uses MCN(Multiple Color Normalization) and MCWUPC(Moving Color Weighted Unmatched Pixel Count) computation to decide the existence of moving object and object segmentation technique using signature information is used to exactly extract the objects with high probability. Finally, real time detection system is implemented to verify the effectiveness of the technique and experiments show that the success rate of object tracking is more than $89\%$ of total 120 image frames.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3862-3879
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• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• Structural Engineering and Mechanics
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• v.87 no.6
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• pp.555-574
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• 2023

An efficient optimization algorithm and damage-sensitive objective function are two main components in optimization-based Finite Element Model Updating (FEMU). A suitable combination of these components can considerably affect damage detection accuracy. In this study, a new hybrid damage-sensitive objective function is proposed based on combining two different objection functions to detect the location and extent of damage in structures. The first one is based on Generalized Pseudo Modal Strain Energy (GPMSE), and the second is based on the element's Generalized Flexibility Matrix (GFM). Four well-known population-based metaheuristic algorithms are used to solve the problem and report the optimal solution as damage detection results. These algorithms consist of Cuckoo Search (CS), Teaching-Learning-Based Optimization (TLBO), Moth Flame Optimization (MFO), and Jaya. Three numerical examples and one experimental study are studied to illustrate the capability of the proposed method. The performance of the considered metaheuristics is also compared with each other to choose the most suitable optimizer in structural damage detection. The numerical examinations on truss and frame structures with considering the effects of measurement noise and availability of only the first few vibrating modes reveal the good performance of the proposed technique in identifying damage locations and their severities. Experimental examinations on a six-story shear building structure tested on a shake table also indicate that this method can be considered as a suitable technique for damage assessment of shear building structures.

• Convergence Security Journal
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• v.19 no.5
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• pp.19-24
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• 2019

It is impossible to apply the routing protocol in the wired environment because MANET consists of only mobile nodes. Therefore, routing protocols considered these characteristics are required. In particular, if malicious nodes are not excluded in the routing phase, network performance will be greatly reduced. In this paper, we propose intrusion detection technique based on region to improve routing performance. In the proposed technique, the whole network is divided into certain areas, and then attack detection within the area using area management node is performed. It is a proposed method that can detect attack nodes in the path through cooperation with each other by using completion message received from member nodes. It also applied a method that all nodes participating in the network can share the attack node information by storing the detected attack node and sharing. The performance evaluation of the proposed technique was compared with the existing security routing techniques through the experiments and the superior performance of the proposed technique was confirmed.