• Journal of the Korean Society for Nondestructive Testing
• /
• v.30 no.2
• /
• pp.98-103
• /
• 2010

In this paper, imaging processing study obtained from CCD image and thermography image was performed in order to treat easily thermographic data without any risks of personnels who conduct the condition monitoring for the abnormal or failure status occurrable in industrial power plants. This imaging processing is also applicable to the predictive maintenance. For confirming the broad monitoring, a methodology producting single image from the panorama technique was developed no matter how many cameras are employed, including fusion method for discrete configuration for the target. As results, image fusion from quick realtime processing was obtained and it was possible to save time to track the location monitoring in matching the images between CCTV and thermography.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.6
• /
• pp.448-455
• /
• 2003

A system design technique of automatic thickness measurement and defect inspection system, which measures the thickness of the ALC(Autoclaved Lightweight Concrete) block and inspects the defect on a realtime basis is proposed. The image processing system was established with a CCD camera, an image grabber, and a personal computer without using assembled measurement equipment. The image obtained by this system was analyzed by a devised algorithm, specially designed for the enhanced measurement accuracy. For the realization of the proposed algorithm, the preprocessing method that can be applied to overcome uneven lighting environment, an enhanced edge decision method using 8 edge-pairs with irregular and rough surface, the unit length decision method in uneven condition with rocking objects, and the curvature calibration method of camera using a constructed grid are developed. The experimental results, show that the required measurement accuracy specification is sufficiently satisfied using our proposed method.

• Journal of Korea Multimedia Society
• /
• v.20 no.4
• /
• pp.606-613
• /
• 2017

When the planar area is captured by the depth camera, the shape of the plane in the captured image has perspective projection distortion according to the position of the camera. We can correct the distorted image by the depth information in the plane in the captured area. Previous depth information based perspective distortion correction methods fail to satisfy the real-time property due to a large amount of computation. In this paper, we propose the method of applying the conversion table selectively by measuring the motion of the plane and performing the correction process by parallel processing for correcting perspective projection distortion. By appling the proposed method, the system for correcting perspective projection distortion correct the distorted image, whose resolution is 640x480, as 22.52ms per frame, so the proposed system satisfies the real-time property.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.2
• /
• pp.58.1-58.1
• /
• 2019

We have developed Ground Software (GSW) of BITSE. The ground software includes mission operation software, data visualization software and data processing software. Mission operation software is implemented using COSMOS. COSMOS is a command and control system providing commanding, scripting and data visualization capabilities for embedded systems. Mission operation software send commands to flight software and control coronagraph. It displays every telemetry packets and provides realtime graphing of telemetry data. Data visualization software is used to display and analyze science image data in real time. It is graphical user interface (GUI) and has various functions such as directory listing, image display, and intensity profile. The data visualization software shows also image information which is FITS header, pixel resolution, and histogram. It helps users to confirm alignment and exposure time during the mission. Data processing software creates 4-channel polarization data from raw data.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2023.07a
• /
• pp.237-240
• /
• 2023

본 논문은 철도 건널목(교차로)에서 발생하는 차량, 보행자 및 야생 동물 사고 등의 상황에서 발생하는 위험 요소를 설정하고 철도 건널목(교차로)의 운행상황을 확인할 수 있도록 모형 철도 주변에 유형별 센서들을 설치하고 데이터를 인지하여 시스템에 저장하고, 유효한 데이터 분석을 통해 Orange3 머신러닝 기법을 적용한다. 철도 건널목에 관련된 이미지 중 위험인자로서 차량, 보행자 및 야생동물등의 객체를 감지하고 데이터를 수집하여 활용한다. 또한 이러한 데이터들은 이용자 상황에 맞는 철도 데이터 운영 시스템으로 적용할 수 있도록 위험 예측 시스템을 제안한다.

• The Journal of the Korea Contents Association
• /
• v.11 no.1
• /
• pp.92-101
• /
• 2011

However, in order to create a stereoscopic 3D image, independently, it is required to spend expensive manufacturing costs and to have special techniques. As 3D display devices have been generalized, there is an increasing need for implementing a stereoscopic 3D image without a burden of expensive costs. This paper proposes some methods to implement stereoscopic 3D images easily by utilizing a marker tracking technology using a single camera. In addition, the study made it possible for the resolution of an image to be adjustable dynamically. This paper will be committed to the promotion of the field of UCC (User Created Contents) using stereoscopic 3D images by attracting the active participation of general users in the field of the implementation of stereoscopic 3D images.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.11 no.6
• /
• pp.311-317
• /
• 2011

In this paper, we proposed realtime image enhancing method on the three noise types of input images, such as haze, low contrast and back light images. Some conventional de-hazing algorithms have good performance but need large memories and high computational burdens. We proposed the efficient algorithm which not only removes the haze but also reduces memory usage and computational complexity. We implemented the realtime system by using DM6446 DSP chip, and it showed the excellent result in these three problems; haze, low contrast and back light. We implemented the system with the processing speed at 15 frames/sec.

• Journal of the Korean Society of Radiology
• /
• v.10 no.8
• /
• pp.637-643
• /
• 2016

Due to the popularity of artificial intelligent, medical image processing using artificial neural network is increasingly attracting the attention of academic and industry researches. Deep learning with a convolutional neural network has been proved to very effective representation of images. However, the training process requires high performance H/W platform. Thus, the realtime learning of a large number of high dimensional samples within low-power devices is a challenging problem. In this paper, we attempt to establish this possibility by presenting a realtime neural network method on Raspberry pi using online sequential extreme learning machine. Our experiments on high-dimensional dataset show that the proposed method records an almost real-time execution.

• Journal of Digital Contents Society
• /
• v.19 no.6
• /
• pp.1207-1212
• /
• 2018

The ultrasound image uses ultrasonic pulses to receive the reflected waves and construct an image necessary for diagnosis. At this time, when the signal becomes weak, noise is generated and a slight difference in brightness occurs. In addition, fluctuation of image due to breathing phenomenon, which is the characteristic of ultrasound image, and change of motion in real time occurs. Such a noise is difficult to recognize and diagnose visually in the analysis process. In this paper, morphological features are automatically extracted by using image processing technique on ultrasound acquired images. In this paper, we implemented a GPU - based fast filter using a cloud big data processing platform for image processing. In applying the GPU - based high - performance filter, the algorithm was run with performance 4.7 times faster than CPU - based and the PSNR was 37.2dB, which is very similar to the original.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.8
• /
• pp.640-646
• /
• 2003

We presents an edge-based point correlation algorithm for fast and stable visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties in applying automated inspection systems to real factory environment. First of all, NGC algorithms involve highly complex computation and thus require high performance hardware for realtime process. In addition, lighting condition in realistic factory environments is not stable and therefore intensity variation from uncontrolled lights gives many troubles for applying NGC directly as pattern matching algorithm. We propose an algorithm to solve these problems, using thinned and binarized edge data, which are obtained from the original image. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the computational complexity. Matching edges instead of using original gray-level image pixels overcomes problems in NGC method and pyramid of edges also provides fast and stable processing. All proposed methods are proved by the experiments using real images.