• Title/Summary/Keyword: Image Edge

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A study on the development of the fin-tube heat exchanger pollution ratio evaluation algorithm using Image Processing and Affine Transformation (영상처리 및 어파인변환을 이용한 핀튜브 열교환기 오염율 평가 알고리즘 개발에 관한 연구)

  • Park, Sungmin;Jung, Myungin;Whang, Kwangil;Cho, Gyeongrae
    • Journal of the Korean Society of Visualization
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    • v.20 no.1
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    • pp.11-17
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    • 2022
  • Among the various factors that cause the performance decrease of heat exchangers used in many industries, flow path blocking is one of the important and serious factor. In order to solve this problem, proper maintenance and management of the heat exchanger is important and emphasized. In this study, we developed and algorithm that can quantitatively determine and diagnose the normal and blocked areas of fin-tube heat exchanger using pattern analysis, Gaussian Edge Detection, Image Processing and Affine Transformation techniques. The developed algorithms was applied to the actual heat exchanger and the performance was evaluated by comparing with the manual results. From these results, it was proved that the developed algorithm is effective in evaluating the pollution ratio of the fin-tube heat exchanger.

Smartphone-based structural crack detection using pruned fully convolutional networks and edge computing

  • Ye, X.W.;Li, Z.X.;Jin, T.
    • Smart Structures and Systems
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    • v.29 no.1
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    • pp.141-151
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    • 2022
  • In recent years, the industry and research communities have focused on developing autonomous crack inspection approaches, which mainly include image acquisition and crack detection. In these approaches, mobile devices such as cameras, drones or smartphones are utilized as sensing platforms to acquire structural images, and the deep learning (DL)-based methods are being developed as important crack detection approaches. However, the process of image acquisition and collection is time-consuming, which delays the inspection. Also, the present mobile devices such as smartphones can be not only a sensing platform but also a computing platform that can be embedded with deep neural networks (DNNs) to conduct on-site crack detection. Due to the limited computing resources of mobile devices, the size of the DNNs should be reduced to improve the computational efficiency. In this study, an architecture called pruned crack recognition network (PCR-Net) was developed for the detection of structural cracks. A dataset containing 11000 images was established based on the raw images from bridge inspections. A pruning method was introduced to reduce the size of the base architecture for the optimization of the model size. Comparative studies were conducted with image processing techniques (IPTs) and other DNNs for the evaluation of the performance of the proposed PCR-Net. Furthermore, a modularly designed framework that integrated the PCR-Net was developed to realize a DL-based crack detection application for smartphones. Finally, on-site crack detection experiments were carried out to validate the performance of the developed system of smartphone-based detection of structural cracks.

Demosaicing Algorithm by Gradient Edge Detection Filtering on Color Component (컬러 성분 에지 기울기 검출 필터링을 이용한 디모자이킹 알고리즘)

  • Jeon, Gwan-Ggil;Jung, Tae-Young;Kim, Dong-Hyung;Kim, Seung-Jong;Jeong, Je-Chang
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.12C
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    • pp.1138-1146
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    • 2009
  • Digital cameras adopting a single CCD detector collect image color by subsampling in three color planes and successively interpolating the information to reconstruct full-resolution color images. Therefore, to recovery of a full-resolution color image from a color filter array (CFA) like the Bayer pattern is generally considered as an interpolation issue for the unknown color components. In this paper, we first calculate luminance component value by combining R, G, B channel component information which is quite different from the conventional demosaicing algorithm. Because conventional system calculates G channel component followed by computing R and B channel components. Integrating the obtained gradient edge information and the improved weighting function in luminance component, a new edge sensitive demosaicing technique is presented. Based on 24 well known testing images, simulation results proved that our presented high-quality demosaicing technique shows the best image quality performance when compared with several recently presented techniques.

Decreasing Parameter Decision in Edge Strength Hough Transform (경계선 강도 허프 변환에서 감쇄 파라미터의 결정)

  • Woo, Young-Woon;Heo, Gyeong-Yong;Kim, Kwang-Baek
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2007.06a
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    • pp.728-731
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    • 2007
  • Though the Hough transform is a well-known method for detecting analytical shape represented by a number of free parameters, the basic property of the Hough transform, the one-to-many mapping from an image space to a Hough space, causes the innate problem, the sensitivity to noise. To remedy this problem, Edge Strength Hough Transform (ESHT) was proposed and proved to reduce the noise sensitivity. However the performance of ESHT depends on the size of a Hough space and image and some other parameters, which play an important role in ESHT and should be decided experimentally. In this paper, we derived a formula to decide decreasing parameter. Using the derived formulae, the decreasing parameter value can be decided only with the pre-determined values, the size of a Hough space and an image, which make it possible to decide them automatically.

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A Study on Image Restoration for Removing Mixed Noise while Considering Edge Information (에지정보를 고려한 복합잡음 제거를 위한 영상복원에 관한 연구)

  • Gao, Yinyu;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.10
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    • pp.2239-2246
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    • 2011
  • In image signal processing, image signal is corrupted by various noises and caused the degradation phenomenon. And Images often corrupted by AWGN(additive white gaussian noise) and impulse noise which called mixed noise. In this paper, the algorithm is proposed to remove mixed noise while keeping edge information. The proposed algorithm first classifies the noise type, if the classify result is AWGN, then the mean of the output after using self-adaptive weighted mean filter and median value will be the outfiltering value. And if the noise type is impulse noise, then the noise is removed by a modified nonlinear filter. Also we compare existing methods through the simulation and using PSNR(peak signal to noise ratio) as the standard of judgement of improvement effect. The result of computer simulation on test images indicates that the proposed method is superior to traditional filtering algorithms.

Up-Sampling Method of Depth Map Using Weighted Joint Bilateral Filter (가중치 결합 양방향 필터를 이용한 깊이 지도의 업샘플링 방법)

  • Oh, Dong-ryul;Oh, Byung Tae;Shin, Jitae
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.6
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    • pp.1175-1184
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    • 2015
  • A depth map is an image which contains 3D distance information. Generally, it is difficult to acquire a high resolution (HD), noise-removed, good quality depth map directly from the camera. Therefore, many researches have been focused on acquisition of the high resolution and the good quality depth map by up-sampling and pre/post image processing of the low resolution depth map. However, many researches are lack of effective up-sampling for the edge region which has huge impact on image perceptual-quality. In this paper, we propose an up-sampling method, based on joint bilateral filter, which improves up-sampling of the edge region and visual quality of synthetic images by adopting different weights for the edge parts that is sensitive to human perception characteristics. The proposed method has gains in terms of PSNR and subjective video quality compared to previous researches.

An Extracting Text Area Using Adaptive Edge Enhanced MSER in Real World Image (실세계 영상에서 적응적 에지 강화 기반의 MSER을 이용한 글자 영역 추출 기법)

  • Park, Youngmok;Park, Sunhwa;Seo, Yeong Geon
    • Journal of Digital Contents Society
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    • v.17 no.4
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    • pp.219-226
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    • 2016
  • In our general life, what we recognize information with our human eyes and use it is diverse and massive. But even the current technologies improved by artificial intelligence are exorbitantly deficient comparing to human visual processing ability. Nevertheless, many researchers are trying to get information in everyday life, especially concentrate effort on recognizing information consisted of text. In the fields of recognizing text, to extract the text from the general document is used in some information processing fields, but to extract and recognize the text from real image is deficient too much yet. It is because the real images have many properties like color, size, orientation and something in common. In this paper, we applies an adaptive edge enhanced MSER(Maximally Stable Extremal Regions) to extract the text area in those diverse environments and the scene text, and show that the proposed method is a comparatively nice method with experiments.

Visually Weighted Group-Sparsity Recovery for Compressed Sensing of Color Images with Edge-Preserving Filter (컬러 영상의 압축 센싱을 위한 경계보존 필터 및 시각적 가중치 적용 기반 그룹-희소성 복원)

  • Nguyen, Viet Anh;Trinh, Chien Van;Park, Younghyeon;Jeon, Byeungwoo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.9
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    • pp.106-113
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    • 2015
  • This paper integrates human visual system (HVS) characteristics into compressed sensing recovery of color images. The proposed visual weighting of each color channel in group-sparsity minimization not only pursues sparsity level of image but also reflects HVS characteristics well. Additionally, an edge-preserving filter is embedded in the scheme to remove noise while preserving edges of image so that quality of reconstructed image is further enhanced. Experimental results show that the average PSNR of the proposed method is 0.56 ~ 4dB higher than that of the state-of-the art group-sparsity minimization method. These results prove the excellence of the proposed method in both terms of objective and subjective qualities.

Occlusion Processing in Simulation using Improved Object Contour Extraction Algorithm by Neighboring edge Search and MER (이웃 에지 탐색에 의한 개선된 객체 윤곽선 추출 알고리즘과 MER을 이용한 모의훈련에서의 폐색처리)

  • Cha, Jeong-Hee;Kim, Gye-Young;Choi, Hyung-Il
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.2
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    • pp.206-211
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    • 2008
  • Trainee can enhance his perception of and interaction with the real world by displayed virtual objects in simulation using image processing technology. Therefore, it is essential for realistic simulation to determine the occlusion areas of the virtual object produces after registering real image and virtual object exactly. In this paper, we proposed the new method to solve occlusions which happens during virtual target moves according to the simulated route on real image using improved object contour extraction by neighboring edge search and picking algorithm. After we acquire the detailed contour of complex objects by proposed contour extraction algorithm, we extract the three dimensional information of the position happening occlusion by using MER for performance improvement. In the experiment, we compared proposed method with existed method and preyed the effectiveness in the environment which a partial occlusions happens.

A Image Post-processing Method using Modified MSDS (수정된 MSDS를 이용한 영상의 후처리 기법)

  • 김은석;채병조;오승준
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.24 no.8B
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    • pp.1480-1489
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    • 1999
  • In this paper, we propose a new post-processing method which can solve a problem of MSDS(Mean Squared Difference of Slope) method. Using that method the blocking artifacts can significantly be reduced without any restriction, which is a major drawback of block-based DCT compression method. In this approach, the OSLD(Overlapped Sub-Laplacian Distribution) of dequantized block boundary pixel difference values is defined and used to categorize each block of an image into one of four types. Those types are also classified into one of two classes: an edge and a non-edge classes. A slope across the block boundary is used to quantify discontinuity of the image. If an absolute estimated quantization error value of a DCT coefficient is greater than the corresponding quantization step size, it is saturated to the step size in the edge class. The proposed post-processing method can improve not only the PSNR value up to 0.1~O.3 dB but visual quality without any constraints determined by ad-hoc manner.

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