• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.44-55
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• 2022

In order to automatically provide information on fruits in agricultural related broadcasting contents, instance image segmentation of target fruits is required. In addition, the information on the 3D pose of the corresponding fruit may be meaningfully used. This paper represents research that provides information about tomatoes in video content. A large amount of data is required to learn the instance segmentation, but it is difficult to obtain sufficient training data. Therefore, the training data is generated through a data augmentation technique based on a small amount of real images. Compared to the result using only the real images, it is shown that the detection performance is improved as a result of learning through the synthesized image created by separating the foreground and background. As a result of learning augmented images using images created using conventional image pre-processing techniques, it was shown that higher performance was obtained than synthetic images in which foreground and background were separated. To estimate the pose from the result of object detection, a point cloud was obtained using an RGB-D camera. Then, cylinder fitting based on least square minimization is performed, and the tomato pose is estimated through the axial direction of the cylinder. We show that the results of detection, instance image segmentation, and cylinder fitting of a target object effectively through various experiments.

• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.45-52
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• 2006

This paper presents an image-space algorithm to real-time collision detection, which is run completely by GPU. For a single object or for multiple objects with no collision, the front and back faces appear alternately along the view direction. However, such alternation is violated when objects collide. Based on these observations, the algorithm propose the depth peeling method which renders the minimal surface of objects, not whole surface, to find colliding. The Depth peeling method utilizes the state-of-the-art functionalities of GPU such as framebuffer object, vertexbuffer object, and occlusion query. Combining these functions, multi-pass rendering and context switch can be done with low overhead. Therefore proposed approach has less rendering times and rendering overhead than previous image-space collision detection. The algorithm can handle deformable objects and complex objects, and its precision is governed by the resolution of the render-target-texture. The experimental results show the feasibility of GPU-based collision detection and its performance gain in real-time applications such as 3D games.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.19-24
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• 2017

The growing number of electronic financial transactions (e-banking) has entailed the rapid increase in security threats such as extortion and falsification of financial transaction data. Against such background, rigid security and countermeasures to hedge against such problems have risen as urgent tasks. Thus, this study aims to implement an improved case model by applying the Fraud Detection System (hereinafter, FDS) in a financial corporation 'A' using big data technique (e.g. the function to collect/store various types of typical/atypical financial transaction event data in real time regarding the external intrusion, outflow of internal data, and fraud financial transactions). As a result, There was reduction effect in terms of previous scenario detection target by minimizing false alarm via advanced scenario analysis. And further suggest the future direction of the enhanced FDS.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.186-191
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• 2019

In this paper, we propose a implementation approach of the high-speed feature extraction algorithm. The proposed method is based on the block type classification algorithm which reduces the computation time when target macro block is divided to smooth block type that has no image features. It is quantitatively identified that occurs at 29.5% of the total image using 200 standard test images with $64{\times}64$ macro block size. This means that within a standard test image containing various image information, 29.5% can reduce the complexity of the operation. When the proposed approach is applied to the Canny edge detection, the required latency of the edge detection can be completely eliminated, such as 2D derivative filter, gradient magnitude/direction computation, non-maximal suppression, adaptive threshold calculation, hysteresis thresholding. Also, it is expected that operation time of the feature detection can be reduced by applying block type classification algorithm to various feature extraction algorithms in this way.

• The Journal of the Acoustical Society of Korea
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• v.23 no.4
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• pp.288-295
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• 2004

Detection and localization of a slow and quiet target in shallow water environments is a challenging problem for which it is well known that snapshot is deficient because of a fast and strong interferer. This paper presents waveguide interference filtering technique that mitigate strong interferer problems in adaptive matched field processing. MCM (multiple constraint method) based on NDC (null direction constraint) has been proposed for new spatial interferer filter. MCM-NDC using replica force a interferer component to be filtered through CSDM (cross-spectral density matrix). This filtering have an effect on sidelobe reduction and restoring of signal gain of a quiet target. This technique was applied to a simulation on Pekeris waveguide and vertical array data from MAPLE03 (matched acoustic properties and localization experiment) in the East Sea and was shown to improve SBNR (signal-to-background-and-noise ratio) over the standard MVDR (minimum-variance distortionless response) and NSP (null space projection) technique.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.5
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• pp.431-437
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• 2006

In this paper vector code correlation(VCC) method and an algorithm to promote the image-processing performance in building an effective measurement system using cameras are described far automatically berthing and controlling the ship equipped with side-thrusters. In order to realize automatic ship berthing, it is indispensable that the berthing assistant system on the ship should continuously trace a target in the berth to measure the distance to the target and the ship attitude, such that we can make the ship move to the specified location. The considered system is made up of 4 apparatuses compounded from a CCD camera, a camera direction controller, a popular PC with a built-in image processing board and a signal conversion unit connected to parallel port of the PC. The object of this paper is to reduce the image-processing time so that the berthing system is able to ensure the safety schedule against risks during approaching to the berth. It could be achieved by composing the vector code image to utilize the gradient of an approximated plane found with the brightness of pixels forming a certain region in an image and verifying the effectiveness on a commonly used PC. From experimental results, it is clear that the proposed method can be applied to the measurement system for automatic ship berthing and has the image-processing time of fourfold as compared with the typical template matching method.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.38 no.3
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• pp.249-255
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• 2002

This paper describes on the extraction of ship's real-time movement information using a combination full-function ARPA radar and ECS system that displays radar images and an electronic chart together on a single PC screen. The radar target extractor(RTX) board, developed by Marine Electronics Corporation of Korea, receives radar video, trigger, antenna bearing pulse and heading pulse signals from a radar unit and processes these signals to extract target information. The target data extracted from each pulse repetition interval in DSPs of RTX that installed in 16 bit ISA slot of a IBM PC compatible computer is formatted into a series of radar target messages. These messages are then transmitted to the host PC and displayed on a single screen. The position data of target in range and azimuth direction are stored and used for determining the center of the distributed target by arithmetic averaging after the detection of the target end. In this system, the electronic chart or radar screens can be displayed separately or simulaneously and in radar mode all information of radar targets can be recorded and replayed In spite of a PC based radar system, all essential information required for safe and efficient navigation of ship can be provided.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.487-490
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• 2003

In this research, we will devise an obstacle avoidance algorithm for a previously unmanned vehicle. Whole systems consist mainly of the vehicle system and the control system. The two systems are separated; this system can communicate with the vehicle system and the control system through wireless RF (Radio Frequency) modules. These modules use wireless communication. And the vehicle system is operated on PIC Micro Controller. Obstacle avoidance method for unmanned vehicle is based on the Virtual Force Field (VFF) method. An obstacle exerts repulsive forces and the lane center point applies an attractive force to the unmanned vehicle. A resultant force vector, comprising of the sum of a target directed attractive force and repulsive forces from an obstacle, is calculated for a given unmanned vehicle position. With resultant force acting on the unmanned vehicle, the vehicle's new driving direction is calculated, the vehicle makes steering adjustments, and this algorithm is repeated.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.445-450
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• 2018

Reliable detection and parameter estimation of a radar cross section(RCS) fluctuating target have been known as a difficult task. To reduce the effect of RCS fluctuation, various diversity techniques have been considered. This paper presents a new method for synthesizing a covariance matrix applicable to a coherent multi-input multi-output(MIMO) radar with frequency diversity. It is achieved by efficiently combining covariance matrices corresponding to different carrier frequencies such that the signal-to-noise ratio(SNR) in the combined covariance matrix is maximized. The value of a synthesized covariance matrix is assessed by examining the phase curves of its entries and the improvement on direction of arrival(DOA) estimation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4492-4507
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• 2021

In camera sensor networks (CSNs), in order to better identify the point, full-view problem requires capture any facing direction of target (point or intruder), and its coverage prediction and sensor density issues are more complicated. At present, a lot of research supposes that a large number of homogeneous camera sensors are randomly distributed in a bounded square monitoring region to obtain full-view rate which is close to 1. In this paper, we deduce the sensor density prediction model in heterogeneous deployed CSNs with arbitrary full-view rate. Aiming to reduce the influence of boundary effect, we introduce the concepts of expanded monitoring region and maximum detection area. Besides, in order to verify the performance of the proposed sensor density model, we carried out different scenarios in simulation experiments to verify the theoretical results. The simulation results indicate that the proposed model can effectively predict the sensor density with arbitrary full-view rate.