• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.11
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• pp.895-903
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• 2016

In a fire disaster in a tunnel, people should be rescued immediately using the information obtained from cameras or sensors. However, in heavy smoke from a fire, people cannot be clearly identified by a mounted CCTV, which is only effective in a clear environment. A thermal camera can be an alternative to this in smoky situations and is capable of detecting people from their emitted thermal energy. On the other hand, the thermal image camera has a smaller field of view than an ordinary camera due to its lens characteristics and temperature error, etc. In order to cover a relatively wide area, panoramic image construction needs to be implemented. In this work, a template-based similarity matching algorithm for constructing the panorama image is proposed and its performance is verified through experiments. This scheme provides guidelines for coping with difficulty in image construction, which requires an exact correspondence search for two images in cases of heavy smoke.

• Journal of Ocean Engineering and Technology
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• v.2 no.1
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• pp.135-149
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• 1988

Friction welding has been shown to have significant economic and technical advantages. However, one of the major concerns in using friction welding is the reliability of the weld quality. No reliable nondestructive test method is available at present for detecting weld quality, particularly in a production environment. Friction welds are formed by the mechanisms of diffusion as well as mechanical interlocking. The severe plastic flow at the interface by forge action of the process brings the subsurfaces so close together that detection of any unbonded area becomes very difficult. This paper presents an attempt to determine the friction weld strength quantitatively using the ultrasonic pulse-echo method. Instead of detecting flaws or cracks at the interface, the new approach calculates the coefficient of reflection based on measured amplitudes of the echoes. It has been finally confirmed that this coefficient could provide the quantitative relationship to the weld quality such as tensile strength, torsional strength, impact value, hardness, etc. So a new nondestructive analysis system of friction weld strength of dissimilar metals using an ultrasonic technique could be well developed.

• Spatial Information Research
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• v.19 no.2
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• pp.19-28
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• 2011

Three dimensional modeling for seafloor topography is essential to monitoring displacements in underwater structures as well as all sorts of disasters along the shore. MBES is a system that is capable of high-density water depth measurement for seafloor topography and is in broad uses for gathering 3D data and detecting displacements. MBES data, however, contain random errors that take place in the equipment offset and surveying process and require systematic researches on the correction of wrong depth measurements. Thus this study set out to propose a post-processing technique to eliminate an array of random errors taking place after equipment offset correction and basic noise correction in the MBES system and analyze its applicability to seafloor topography modeling by applying it to the subject area.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.185-191
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• 2011

Recently, the surveillance system is highly being attention. Various Technologies as detecting object from image than determining and recognizing if the object are person are universally being used. Therefore, In this paper shows detecting on this kind of object and local context based facial feather detection algorithm is being advocated. Detect using Gabor Bunch in the same time Bayesian detection method for revision to find feather point is being described. The entire system to search for object area from image, context-based face detection, feature extraction methods applied to improve the performance of the system.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.609-614
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• 2009

The purpose of this paper is to design the metal object detection system for drive inside protection. To do this, we propose the algorithm for designing the color filter that can detect the metal object using fuzzy theory and the algorithm for detecting area of the driver's face using fuzzy skin color filter. Also, by using the proposed algorithm, we propose the algorithm for detecting the metallic object candidate regions. And, the metallic object color filter is then applied to find the candidate regions. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Journal of the Korea Society for Simulation
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• v.27 no.1
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• pp.39-49
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• 2018

An SLBM (submarine-launched ballistic missile) seriously threatens the national security due to its stealthiness that makes it difficult to detect in advance. We consider a destroyer deployment optimization problem for effectively detecting an SLBM. An optimization model is based on the two-person zero-sum game in which an adversary determines the firing and arriving places with an appropriate trajectory that provides a low detection probability, and we establish a destroyer deployment plan that guarantees the possibly highest detection probability. The proposed two-person zero-sum game model can be solved with the corresponding linear programming model, and we perform computational studies with a randomly generated area and scenario and show the optimal mixed strategies for both the players in the game.

• Computers and Concrete
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• v.23 no.6
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• pp.445-457
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• 2019

Cracks are an important distress of concrete bridges, and may reduce the life and safety of bridges. However, the traditional manual crack detection means highly depend on the experience of inspectors. Furthermore, it is time-consuming, expensive, and often unsafe when inaccessible position of bridge is to be assessed, such as viaduct pier. To solve this question, the real-time automatic crack detecting system with unmanned aerial vehicle (UAV) become a choice. This paper designs a new automatic detection system based on real-time comprehensive image processing for bridge crack. It has small size, light weight, low power consumption and can be carried on a small UAV for real-time data acquisition and processing. The real-time comprehensive image processing algorithm used in this detection system combines the advantage of connected domain area, shape extremum, morphology and support vector data description (SVDD). The performance and validity of the proposed algorithm and system are verified. Compared with other detection method, the proposed system can effectively detect cracks with high detection accuracy and high speed. The designed system in this paper is suitable for practical engineering applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5978-5999
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• 2018

Pedestrian detection is a challenging area in the intelligent vehicles domain. During the last years, many works have been proposed to efficiently detect motion in images. However, the problem becomes more complex when it comes to detecting moving areas while the vehicle is also moving. This paper presents a variational optical flow-based method for motion estimation in vehicular traffic scenarios. We introduce a framework for detecting motion areas with small and large displacements by computing optical flow using a multilevel architecture. The flow field is estimated at the shortest level and then successively computed until the largest level. We include a filtering parameter and a warping process using bicubic interpolation to combine the intermediate flow fields computed at each level during optimization to gain better performance. Furthermore, we find that by including a penalization function, our system is able to effectively reduce the presence of outliers and deal with all expected circumstances in real scenes. Experimental results are performed on various image sequences from Daimler Pedestrian Dataset that includes urban traffic scenarios. Our evaluation demonstrates that despite the complexity of the evaluated scenes, the motion areas with both moving and static camera can be effectively identified.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.