• Journal of Korean Society for Geospatial Information Science
• /
• v.13 no.3 s.33
• /
• pp.59-67
• /
• 2005

Building information is primary source in many applications such as mapping, telecommunication, car navigation and virtual city modeling. While aerial CCD images which are captured by passive sensor(digital camera) provide horizontal positioning in high accuracy, it is far difficult to process them in automatic fashion due to their inherent properties such as perspective projection and occlusion. On the other hand, LIDAR system offers 3D information about each surface rapidly and accurately in the form of irregularly distributed point clouds. Contrary to the optical images, it is much difficult to obtain semantic information such as building boundary and object segmentation. Photogrammetry and LIDAR have their own major advantages and drawbacks for reconstructing earth surfaces. The purpose of this investigation is to automatically obtain spatial information of 3D buildings by fusing LIDAR data with aerial CCD image. The experimental results show that most of the complex buildings are efficiently extracted by the proposed method and signalize that fusing LIDAR data and aerial CCD image improves feasibility of the automatic detection and extraction of buildings in automatic fashion.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.39 no.2
• /
• pp.235-240
• /
• 2015

Active Safety system is requested on the market of the medium and heavy duty commercial vehicle over 4.5ton beside the market of passenger car with advancement of the digital equipment proportionally. Unlike the passenger car, the mounting position of camera in case of the medium and heavy duty commercial vehicle is relatively high, it is disadvantaged conditions for lane recognition in contradiction to passenger car. In this work, we show the method of lane recognition through the Sobel edge, based on the spatial domain processing, Hough transform and color conversion correction. Also we suggest the low error method of front vehicles recognition in order to reduce the detection error through Haar-like, Adaboost, SVM and Template matching, etc., which are the object recognition methods by frontal camera vision. It is verified that the reliability over 98% on lane recognition is obtained through the vehicle test.

• Journal of Internet Computing and Services
• /
• v.18 no.5
• /
• pp.69-77
• /
• 2017

In this paper, a disaster response scheme based on deep learning and augmented reality technology is proposed and a network resource reservation scheme is presented accordingly. The features of deep learning, augmented reality technology and its relevance to the disaster areas are explained. Deep learning technology can be used to accurately recognize disaster situations and to implement related disaster information as augmented reality, and to enhance disaster response capabilities by providing disaster response On-site disaster response agent, ICS (Incident Command System) and MCS (Multi-agency Coordination Systems). In the case of various disasters, the fire situation is focused on and it is proposed that a plan to strengthen disaster response capability effectively by providing fire situation recognition based on deep learning and augmented reality information. Finally, a scheme to secure network resources to utilize the disaster response method of this paper is proposed.

• Convergence Security Journal
• /
• v.2 no.1
• /
• pp.77-86
• /
• 2002

In this paper, we propose a segmentation algorithm that can reliably separate moving objects from noisy background in the image sequence received from a camera at the fixed position. Image segmentation is one of the most difficult process in image processing and an adoption in the change of environment must be considered for the increase in the accuracy of the image. The proposed algorithm consists of four process : generation of the difference image between the input image and the reference image, removes the background noise using the background nois modeling to a difference image histogram, then selects the candidate initial region using local maxima to the difference image, and gradually expanding the connected regions, region by region, using the shape information. The test results show that the proposed algorithm can detect moving objects like intruders very effectively in the noisy environment.

• The KIPS Transactions:PartA
• /
• v.12A no.4 s.94
• /
• pp.273-280
• /
• 2005

The mobility or highly interconnected and communicating devices and users has implications for the QoS in a ubiquitous computing environment. Therefore, it is important for location aware systems to detect location of mobile object correctly and Provide high quality services in ubiquitous environment. However, it is not easy that location aware systems offer highly reliable QoS to users because process strategies of location aware systems are limited by the capability according to the applied detection target objects. In this paper, we design an autonomic architecture which analyzes the location aware system condition and autonomously chooses the best appropriate process strategy. We also have simulated the Proposed architecture in order to verify its performance. The test results show us that the architecture using self-optimizing mechanism provides higher QoS to users in variable bandwidth.

• Progress in Medical Physics
• /
• v.31 no.3
• /
• pp.111-123
• /
• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.6
• /
• pp.171-181
• /
• 2011

Proposed in this paper is the intelligent video surveillance system to effectively detect multiple loitering objects even that disappear from the out of camera's field of view and later return to a target zone. After the background and foreground are segmented using Gaussian mixture model and shadows are removed, the objects returning to the target zone is recognized using the chromaticity histogram and the duration of loitering is preserved. For more accurate measurement of the loitering behavior, the camera calibration is also applied to map the image plane to the real-world ground. Hence, the loitering behavior can be detected by considering the time duration of the object's existence in the real-world space. The experiment was performed using loitering video and all of the loitering behaviors are accurately detected.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.2
• /
• pp.187-192
• /
• 2021

This study proposes a system for take-off in a forest or similar complex environment using an object detector. In the simulator, a raspberry pi is mounted on a quadcopter with a length of 550mm between motors on a diagonal line, and the experiment is conducted based on edge computing. As for the images to be used for learning, about 150 images of 640⁎480 size were obtained by selecting three points inside Kunsan University, and then converting them to black and white, and pre-processing the binarization by placing a boundary value of 127. After that, we trained the SSD_Inception model. In the simulation, as a result of the experiment of taking off the drone through the model trained with the verification image as an input, a trajectory similar to the takeoff was drawn using the label.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.1
• /
• pp.111-118
• /
• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.12
• /
• pp.1949-1955
• /
• 2021

With the recent COVID-19 situation, countries are seriously feeling the need for medical personnel and their technologies. PDepending on the aging society, the number of medical staff is actually decreasing, and in order to solve this problem, research is needed to replace the part that does not require high expertise among actual medical practices performed by doctors. This paper describes and proposes actual research methods related to unmanned medical systems that use various deep learning image processing-based technologies to check the recovery status applicable to rehabilitation areas where medical staff should face patients directly. The proposed method replaces passive calculations such as a protractor or a method of drawing a line in a photograph, which is the method used for actual motion comparison. Since it is performed in real time, it helps to diagnose quickly, and it is easy for medical staff to provide necessary information because data on the degree of match of motion performance can be checked.