• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.6
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• pp.874-887
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• 1998

A 2-frame PTV (particle tracking velocimetry) system using the concept of match probability between two consequent image frames has been developed to obtain instantaneous velocity fields. The overall 2-frame PTV system including image pre-processing, tracking algorithm and post-processing routine was implemented to apply to real flows. The developed 2-frame PTV system has several advantages such as high recovery ratio of velocity vectors, low error ratio and small computational time compared with the conventional 4-frame PTV and the FFT-based cross-correlation PIV technique. The 2-frame PTV system was applied to a turbulent channel flow over a rectangular block to check its reliability and usefulness. Total 96 sequential image frames have been captured and processed to get both mean and fluctuating velocity vector fields over the recirculating region. The mean velocity and turbulent intensity profiles were well agreed with hte LDV measurements in the separated region behind the block. Time-averaged reattachment length is about 6.3 times of the block height.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1812-1816
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• 2009

이미지 해석에 의한 유속장 측정방법은 유체역학분야에서 지난 30 여년 동안 많이 활용되어온 속도측정 기법으로 오늘날에는 이를 수공학 분야에서 이를 유량측정 등 수리현상 해석에 활용하려는 시도가 다각적으로 이루어지고 있다. 이에 본 연구에서는 이미지 해석에 의한 유속장 측정방법을 용담댐 시험유역에 적용하여 그의 자연하천에서의 적용성을 검토하고자 한다. 이미지 해석에 의한 유속장 측정방법은 PIV(Particle Image Velocimetry)로 통칭되고 있으며, PIV는 seeding, illumination, recording, 및 image processing의 네 가지 요소로 구성된다. seeding을 위해서 유체를 따라 흐를수 있는 작은 입자를 유체에 첨가한다. 유체를 따라 흐르는 입자들의 선명한 이미지를 얻기 위해서illumination이 필요하다. PIV를 이용하여 흐름을 해석하기 위한 illumination은 일반적으로 이중펄스 레이저가 이용된다. 이렇게 유속장 해석을 하려는 유체에 대하여 seeding 및 illumination이 준비되면 단일노출- 다중 프레임법, 혹은 다중노출-단일 프레임법으로 흐름을 recording을 한다. image processing은 이미지를 다운로드하고, 디지타이징 및 화질향상을 하는 전처리(pre-processing), 상관계수의 산정에 의한 유속 벡터의 결정 및 에러 벡터를 제거하고 유속장을 그래프화하는 후처리(post-processing) 과정으로 구성된다. LSPIV(Large Scale PIV)는 PIV의 기본원리를 근거로 하여 기존의 PIV에 비하여 실험실 내에서의 수리모형실험이나 일반 하천에서의 유속측정과 같은 큰 규모$(4m^2\sim45,000m^2$)의 흐름해석을 할 수 있도록 Fujita et al.(1994)와 Aya et al.(1995)이 확장시킨 것이다. PIV와 비교시 LSPIV의 다른 점은 넓은 흐름 표면적을 포함하기 위하여 촬영시에 카메라의 광축과 흐름 사이의 각도가 PIV에서 이용하는 수직이 아닌 경사각을 이용하였고 이에 따라 발생하는 이미지의 왜곡을 제거하기 위하여 이미지 변환기법을 적용하여 왜곡이 없는 정사촬영 이미지로 변환시킨다. 이후부터는 PIV의 이미지 처리 방법이 적용되어 표면유속을 산정한다. 다만 이미지 변환을 PIV 이미지 처리 전에 하느냐 후에 하느냐에 따라 유속장 해석결과에 차이가 있다. PIV의 네가지 단계를 포함하여 LSPIV의 각 단계를 구분하면, seeding, illumination, recording, image transformation,image processing 및 post-processing의 여섯 단계로 나뉘어진다 (Li, 2002). LSPIV를 적용시 물표면 입자의 Tracing을 위하여 자연하천에서 사용하기에 적합한 환경친화적인 seeding 재료인 Wood Mulch를 사용하여 유속을 측정하였다. 적용지점은 용담댐 상류의 동향수위관측소 지점으로 이 지점은 한국수자원공사의 수자원시험유역이 위치하고 있다. 이미지의 촬영은 가정용 비디오 캠코더 (Sony DCR-PC 350)을 이용하여 두 줄기의 흐름에 대하여 각각 약 5분 동안의 영상을 촬영한후 이중에서 seeding의 분포가 잘 이루어진 약 1분간을 추출한후 이를 이용하여 PIV 분석에 이용하였다. 대체적으로 유속장의 계산이 무난하게 이루어지었으나 비교적 수질 상태가 양호하고, 수심이 낮고, 하상재료가 자갈로 이루어져 있어 비슷한 색상의 seeding 재료를 추적하기 어려운 구간이 발생한 부분에서는 유속의 계산이 정확히 이루어지지 않았다.

• Journal of the Korean Society for Nondestructive Testing
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• v.37 no.1
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• pp.13-20
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• 2017

A conventional phased array ultrasonic system offers the ability to steer an ultrasonic beam by applying independent time delays of individual elements in the array and produce an ultrasonic image. In contrast, full matrix capture (FMC) is a data acquisition process that collects a complete matrix of A-scans from every possible independent transmit-receive combination in a phased array transducer and makes it possible to reconstruct various images that cannot be produced by conventional phased array with the post processing as well as images equivalent to a conventional phased array image. In this paper, a basic algorithm based on the LLL mode total focusing method (TFM) that can image crack type flaws is described. And this technique was applied to reconstruct flaw images from the FMC dataset obtained from the experiments and ultrasonic simulation.

• Journal of the Korea Computer Industry Society
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• v.5 no.5
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• pp.737-746
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• 2004

Two-channel multiwavelet system is investigated for image compression application in this paper. Generally, multiwavelets are known for their superb capability of compressing non-stationary signals like voice. However, multivavelet system have a critical problem in processing and compressing image data due to mesh-grid visual artifacts. In our two-channel multiwavelet system we have investigated incorporation of pre and post filtering to the multiwavelet transform and compression system for alleviating those ingerent visual artifacts due to multiwavelet effect. In addition, to quantify the image data compression performance of proposed multiwavelet system, computer simulations have been performed using various image data. For bit allocation and quantization, the Lagrange multiplier technique considering data rate vs. distortion rate along with a nonlinear companding method are applied equallly to all systems considered, here. The simulation results have yielded 1 ~ 2 dB compression enhancement over the scalar savelet systems. If the more advanced compression methods like SPIHT and run-length channel coding were adopted for the proposed multiwavelet system, a much higher compression gain could be obtained.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.10
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• pp.1508-1515
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• 2003

In this study, the image processing program for deducing parameters of the elliptic shape of the partially overlapped liquid droplets was developed using the randomized Hough transform and the parameter decomposition. The procedure for the shape detection consists of three steps. For the first step, the candidate centers of ellipses are determined by the geometric property of the ellipse. Next, the rest parameters are estimated by the randomized Hough transform. In the final step for the post-processing, optimally approximated parameters of ellipses are determined. The developed program was applied to the simulated overlapped ellipses, real overlapped droplets, and real spray droplets. The shape detection was very excellent unless there existed inherent problems in original images. Moreover, this method can be used as an effective separating method for the overlapped small particles.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1783-1788
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• 2003

In this study, the image processing program for deducing parameters of the elliptic shape of the partially overlapped liquid droplets was developed using the randomized Hough transform and the parameter decomposition. The procedure for the shape detection consists of three steps. For the first step, the candidate centers of ellipses are determined by the geometric property of the ellipse. Next, the rest parameters are estimated by the randomized Hough transform. In the final step for the post-processing, optimally approximated parameters of ellipses are determined. The developed program was applied to the simulated overlapped ellipses, real overlapped droplets, and real spray droplets. The shape detection was very excellent unless there existed inherent problems in original images. Moreover, this method can be used as an effective separating method for the overlapped small particles.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.9
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• pp.719-726
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• 1991

The symplified segmentation method was proposed for hardware implementation based on the human visual system. The segmentation method using fuzzy logic and just noticeable difference(JND) is composed of pre-filtering, initial segmentation and post processing. Experimental coding results show that reconstructed image using the proposed method is good on visual percerption even at a high compression ratio of 30:1.

• Journal of Biosystems Engineering
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• v.17 no.1
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.1-6
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• 2014

Color image processing based on smoke detection is suitable detecting target to early detection of fire smoke. A method for detecting the smoke is processed in the pre-processing movement and color. And Next, characteristics of smoke such as diffusion, texture, shape, and directionality are used to post-processing. In this paper, propose the detection method of density distribution characteristic in characteristics of smoke. the generate a candidate regions by color thresholding image in Detecting the movement of smoke to the 10Frame interval and accumulated while 1second image. then check whether the pattern of the smoke by candidate regions to applying OBTP(Object Block Ternary Pattern). every processing is Block-based processing, moving detection is decided the candidate regions of the moving object by applying an adaptive threshold to frame difference image. The decided candidate region accumulates one second and apply the threshold condition of the smoke color. make the ternary pattern compare the center block value with block value of 16 position in each candidate region of the smoke, and determine the smoke by compare the candidate ternary pattern and smoke ternary pattern.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.