• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.28-34
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• 2020

This paper proposes an approach to the fusion of two heterogeneous sensors with two different fields-of-view (FOV): LIDAR and an RGB camera. Registration between data captured by LIDAR and an RGB camera provided the fusion results. Registration was completed once a depthmap corresponding to a 2-dimensional RGB image was generated. For this fusion, RPLIDAR-A3 (manufactured by Slamtec) and a general digital camera were used to acquire depth and image data, respectively. LIDAR sensor provided distance information between the sensor and objects in a scene nearby the sensor, and an RGB camera provided a 2-dimensional image with color information. Fusion of 2D image and depth information enabled us to achieve better performance with applications of object detection and tracking. For instance, automatic driver assistance systems, robotics or other systems that require visual information processing might find the work in this paper useful. Since the LIDAR only provides depth value, processing and generation of a depthmap that corresponds to an RGB image is recommended. To validate the proposed approach, experimental results are provided.

• Journal of IKEEE
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• v.24 no.1
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• pp.97-105
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• 2020

A thermal imaging sensor receives the radiating energy from the target and the background, so it has been widely used for detection, tracking, and classification of targets at night for military purpose. In recognizing the target automatically using thermal images, if the correct edges of object are used then it can generate the classification results with high accuracy. However since the thermal images have lower spatial resolution and more blurred edges than color images, the accuracy of the classification using thermal images can be decreased. In this paper, to overcome this problem, a new hierarchical classifier using both shape and local features based on the segmentation reliabilities, and the class/pose updating method for vehicle classification are proposed. The proposed classification method was validated using thermal video sequences of more than 20,000 images which include four types of military vehicles - main battle tank, armored personnel carrier, military truck, and estate car. The experiment results showed that the proposed method outperformed the state-of-the-arts methods in classification accuracy.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3A
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• pp.274-281
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• 2006

Sensor network applications need synchronized time extremely such as object tracking, consistent state updates, duplicate detection, and temporal order delivery. This paper describes reliable time synchronization protocol (RTSP) for wireless sensor networks. In the proposed method, synchronization error is decreased by creating hierarchical tree with lower depth and reliability is improved by maintaining and updating information of candidate parent nodes. The RTSP reduces recovery time and communication overheads comparing to TPSN when there are topology changes owing to moving of nodes, running out of energy and physical crashes. Simulation results show that RTSP has about 20% better performance than TPSN in synchronization accuracy. And the number of message in the RTSP is $20%{\sim}60%$ lower than that in the TPSN when nodes are failed in the network. In case of different transmission range of nodes, the communication overhead in the RTSP is reduced up to 40% than that in the TPSN at the maximum.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.250-252
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• 2022

Recently, with the development of IoT technology and AI, unmanned and automated systems are progressing in various fields, and various application technologies are being studied in systems using algorithms such as object detection, recognition, and tracking. In the case of a system operating based on an image, noise removal is performed as a pre-processing process, and precise noise removal is sometimes required depending on the environment of the system. In this paper, we propose a modified central weight filter algorithm using pixel division of local regions to minimize the blurring that tends to occur in the filtering process and to emphasize the details of the resulting image. In the proposed algorithm, when a pixel of a local area is divided into two areas, the center of the dominant area among the divided areas is set as a criterion for the weight filter algorithm. The resulting image is calculated by convolving the transformed center weight with the pixel value inside the filtering mask.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.12
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• pp.2563-2567
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• 2011

Advances in smart sensors, embedded systems, low-power design, ad-hoc networks and MEMS have allowed the development of low-cost small sensor nodes with computation and wireless communication capabilities that can form distributed wireless sensor networks. Time information and time synchronization are fundamental building blocks in wireless sensor networks since many sensor network applications need time information for object tracking, consistent state updates, duplicate detection and temporal order delivery. Various time synchronization protocols have been proposed for sensor networks because of the characteristics of sensor networks which have limited computing power and resources. However, none of these protocols have been designed with time representation scheme in mind. Global time format such as UTC TOD (Universal Time Coordinated, Time Of Day) is very useful in sensor network applications. In this paper we propose network time protocol extension for global time presentation in wireless sensor networks.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.38-48
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• 1996

We implemented and evaluated the preformance of a task level pipelined multicomputer "RV860-PIPE(Realtime Vision i860 system using PIPEline)" for computer vision applications. RV860-PIPE is a message-passing MIMD computer having ring interconnection network which is appropriate for vision processing. We designed the node computer of RV860-PIPE using a 64-bit microprocessor to have generality and high processing power for various vision algorithms. Furthermore, to reduce the communication overhead between node computers and between node computer and a frame grabber, we designed dedicated high speed communication channels between them. We showed the practical applicability of the implemented system by evaluting performances of various computer vision applications like edge detection, real-time moving object tracking, and real-time face recognition.

• Atmosphere
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• v.31 no.5
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• pp.489-510
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• 2021

In this paper, we propose an algorithm for detecting convective initiation (CI) using GEO-KOMPSAT-2A/advanced meteorological imager data. The algorithm identifies clouds that are likely to grow into convective clouds with radar reflectivity greater than 35 dBZ within the next two hours. This algorithm is developed using statistical and qualitative analysis of cloud characteristics, such as atmospheric instability, cloud top height, and phase, for convective clouds that occurred on the Korean Peninsula from June to September 2019. The CI algorithm consists of four steps: 1) convective cloud mask, 2) cloud object clustering and tracking, 3) interest field tests, and 4) post-processing tests to remove non-convective objects. Validation, performed using 14 CI events that occurred in the summer of 2020 in Korean Peninsula, shows a total probability of detection of 0.89, false-alarm ratio of 0.46, and mean lead-time of 39 minutes. This algorithm can be useful warnings of rapidly developing convective clouds in future by providing information about CI that is otherwise difficult to predict from radar or a numerical prediction model. This CI information will be provided in short-term forecasts to help predict severe weather events such as localized torrential rainfall and hail.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12C
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• pp.744-752
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• 2011

In intelligent surveillance system, various methods for detecting abnormal behavior were proposed recently. However, most researches are not robust enough to be utilized for actual reality which often has occlusions because of assumption the researches have that individual objects can be tracked. This paper presents a novel method to detect abnormal behavior by analysing major motion of the scene for complex environment in which object tracking cannot work. First, we generate Visual Word and Visual Document from motion information extracted from input video and process them through LDA(Latent Dirichlet Allocation) algorithm which is one of document analysis technique to obtain major motion information(location, magnitude, direction, distribution) of the scene. Using acquired information, we compare similarity between motion appeared in input video and analysed major motion in order to detect motions which does not match to major motions as abnormal behavior.

• Journal of the Korean Society of Radiology
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• v.12 no.1
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• pp.65-70
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• 2018

Computed tomography system is widely used on various fields because section image of an object can be acquired. During several step to obtain section image, artifacts by many error factors can be added on the image. Ring artifact induced by the CT system is examined in this study. A test phantom of $512{\times}512$ size was constructed numerically, and the ring artifact was investigated by the algebraic reconstruction technique. The computer program was realized using Visual C++ under the fan beam geometry with projections of 720 and detector pixel of 1,280. The generation of ring artifact was verified by applying different detection efficiency on detector pixels. The ring intensity became large as increasing the ring value, and the ring artifacts were strongly emphasized near the center of the reconstructed image. The ring artifact may be eliminated by tracking the position of ring artifact on the reconstructed image and by calibrating the detector pixel.