• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.1-13
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• 2007

We make image that accumulate two principal component after change picture to use GLCM(Gray Level Co-Occurrence Matrix)'s texture feature information. And then these images use preprocess to achieved corner detection and area detection. Experiment results, two principle component conversion accumulation images had most informations about six kind textures by Eigen value 94.6%. When compared with red tide area that uses sea color and red tide area of image that have all principle component, displayed the most superior result. Also, we creates Euclidian space using Euclidian distance measurement about red tide area and clear sea. We identify of red tide area by red tide area and clear sea about random sea area through Euclidian distance and spatial distribution.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.112-120
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• 2000

In this paper, we implement a real-time textile dimension inspection system. It can detect various types of real defects which determine the quality of fabric product, defect positions of textile, classify the distortion angel of moving textile and the density. In the implemented system, we measure the density of textile using zone-crossing method with optical lens to solve the noise and real-time problems. And we compensate distortion angel of textile with the classification of distortion types using gaussian gradient and mean gradient features. And also, it detecs real defects of textile and its positions using gray level co-occurrence matrix features. The implemented texile demension inspection systemcan inspect textile dimensions such as density, distortion angle, defect of textile and defect position at real-time. In the implemented proposed texitile dimension inspection system, It is possible to calculate density and detect default of textile at real-time dimension inspection system, it is possible to calculate density and detect default of textile at textile states throughout at all the significant working process such as dyeing, manufacturing, and other texitle processing.

• Journal of Magnetics
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• v.22 no.1
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• pp.34-42
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• 2017

Material defects in the Steam Generator Tubes (SGT) of sodium cooled fast breeder reactor (PFBR) can lead to leakage of water into sodium. The water and sodium reaction will lead to major accidents. Therefore, the examination of steam generator tubes for the early detection of defects is an important requirement for safety and economic considerations. In this work, the Magnetic Flux Leakage (MFL) based Non Destructive Testing (NDT) technique is used to perform the defect detection process. The rectangular notch defects on the outer surface of steam generator tubes are modeled using COMSOL multiphysics 4.3a software. The obtained MFL images are de-noised to improve the integrity of flaw related information. Grey Level Co-occurrence Matrix (GLCM) features are extracted from MFL images and taken as input parameter to train the neural network. A comparative study on characterization have been carried out using feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) algorithms. The results of both algorithms are evaluated with Mean Square Error (MSE) as a prediction performance measure. The average percentage error for length, depth and width are also computed. The result shows that the feed-forward back propagation network model performs better in characterizing the defects.

• Journal of the Korean Society of Safety
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• v.34 no.4
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• pp.15-21
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• 2019

In order to secure the safety of various machinery, it is very important to develop a technique for accurately and quickly measuring the cracks generated in the mechanical equipment and evaluating the mechanical characteristics. The evaluation of the mechanical properties is accompanied by an appropriate strain measurement according to the material and crack occurrence of the target structure. Especially, when micro cracks are generated, the evaluation method is very important. Digital image correlation is an optical full field displacement measuring method which is using currently with speckles in the interested area. However the evaluation method and conditions of image distributions have to be considered carefully to measure the crack occurrence because the images of the speckle patterns affect the quality of displacement results. In this study, the speckle pattern density is characterized to improve the accuracy of the measurement method. And also the micro crack initiation is detected by the measured displacement in the adopted speckle pattern distribution. It is shown that the proposed method is useful to determine the density pattern distribution for the accurate measurement and crack detection.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.365-370
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• 1997

In this paper we suggest a development of surface defect inspection algorithms for cold mill strip using tree structure. The defects which exist in a surface of cold mill strip have a scattering or singular distribution. This paper consists of preprocessing, feature extraction and defect classification. By preprocessing, the binarized defect image is achieved. In this procedure, Top-hit transform, adaptive thresholding, thinning and noise rejection are used. Especially, Top-hit transform using local min/max operation diminishes the effect of bad lighting. In feature extraction, geometric, moment, co-occurrence matrix, histogram-ratio features are calculated. The histogram-ratio feature is taken from the gray-level image. For the defect classification, we suggest a tree structure of which nodes are multilayer neural network clasifiers. The proposed algorithm reduced error rate comparing to one stage structure.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.275-278
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• 2006

본 연구는 전정색(panchromatic) 고해상도 위성영상을 이용하여 산화피해림과 비산화림을 대상으로 수종별로 구분하여 조사하였다. 제안된 방법은 회색단계 공발생 행렬(Gray Level Co-occurrence Matrix, GLCM)을 통하여 생성된 질감 영상(textural images)과 웨이블릿 분해 영상(wavelet decomposition images)의 융합을 실시하여 질감 영상에서 추출될 수 있는 정보와 웨이블릿 분해를 통해 얻을 수 있는 정보를 획득하고자 하였다. 그 결과로 동일 수종을 형성하는 임반이나 산화피해 정도가 유사한 산림의 경우 영상의 밝기값의 분포가 일정한 범위 내에서 형성되어 수종 분류 및 산화피해 등급의 구분이 가능했으나, 영상 내 경계효과(edge effect) 현상은 일부 영상에서 나타났다.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.145-150
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• 2003

고해상도 위성영상을 이용한 산림의 분석은 기존의 중ㆍ저해상도 영상의 분석과 다른 접근이 필요하다. 본 연구는 임분 특성을 해석하는데 중요한 판독기준인 texture를 이용하여 영상 안에서 임상, 임목직경급, 수관울폐도 등에 따른 Texture 정보를 비교 분석하고자 한다. 울산 일부 산림지역을 대상으로 3개의 가시광선 밴드와 1개의 근적외선 밴드의 1m IKONOS 영상을 이용하여 Texture 정보를 추출하는데 일반적으로 사용되는 통계적인 방법 중에 하나인 GLCM(Gray-Level Co-occurrence matrix)을 통해 Texture 분석을 하였다. 또한 1996년도에 제작된 4차 임상도를 통해 추출된 산림 특성별 Texture 정보를 비교 검토하여 고해상도 위성영상을 활용하여 산림 특성을 해석하는데 최적의 Texture 정보를 제시하고자 하였다. 고해상도 영상에서 나타나는 임분의 특성별 질감정보는 임상, 직경, 임목밀도에 따라 다양하게 나타났다.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.499-505
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• 1998

In developing an automated surface inspect algorithm, we have designed a hierarchical classifier using neural network. The defects which exist on the surface of cold mill strip have a scattering or singular distribution. We have considered three major problems, that is preprocessing, feature extraction and defect classification. In preprocessing, Top-hit transform, adaptive thresholding, thinning and noise rejection are used Especially, Top-hit transform using local minimax operation diminishes the effect of bad lighting. In feature extraction, geometric, moment, co-occurrence matrix, and histogram ratio features are calculated. The histogram ratio feature is taken from the gray-level image. For defect classification, we suggest a hierarchical structure of which nodes are multilayer neural network classifiers. The proposed algorithm reduced error rate by comparing to one-stage structure.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11a
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• pp.767-770
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• 2002

영상 검색의 수행 방법으로 사람의 시각 시스템의 특성을 기반으로 웨이블릿 변환의 고주파수 에너지와 형태학적 필터링을 이용하여 분할된 객체의 효과적인 특징 추출을 통한 계층적인 검색 시스템을 제안한다. 영상 고유의 특징을 얻기 위해 객체의 형태 정보와 질감(texture) 방향성 및 칼라 정보를 이용한다. 본 논문에서는 객체의 형태 정보의 추출을 위하여 사용자의 질의(query)영상에서 객체의 윤곽선의 편각차분 변동율에 의한 형태 특징 벡터를 추출하고 GLCM (Gray Level Co-occurrence Matrix)의 Contrast를 질감 특징으로 추출한다. 이들 두 특징을 이용하여 1차 분류 과정을 거치고 2차 검사에서는 보다 정확한 검색을 수행하기 위하여 1차로 분류된 후보영상들에 대하여 세부 정보인 칼라 정보를 기반으로 유사도를 측정함으로써 유사한 칼라와 형태를 가지는 영상뿐만 아니라 칼라가 다른 유사한 영상에도 효율적인 검색 성능을 보였다.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.234-239
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• 2002

영상 검색의 수행 방법으로 사람의 시각 시스템의 특성을 기반으로 효과적인 특징 추출 통한 계층적인 내용 기반 검색 시스템을 제안한다. 영상 고유의 특징을 얻기 위해 영상내에 존재하는 형태 정보와 질감 방향성 및 칼라 정보를 이용한다. 본 논문에서는 형태 정보의 추출을 위하여 사용자의 질의 영상에서 에지 특징 정보를 추출하고 부분 영역으로 분할된 영상에서 GLCM(Gray Level Co-occurrence Matrix)의 Contrast를 질감 특징으로 추출한다. 이들 두 특징을 이용하여 1차 분류 과정을 거치고 2차 검사에서는 보다 정확한 검색을 수행하기 위하여 1차로 분류된 후보영상들에 대하여 영상의 세부 정보인 칼라 정보를 기반으로 유사도를 측정함으로써 유사한 칼라와 형태를 가지는 영상뿐만 아니라 칼라가 다른 유사한 영상에도 효율적인 검색 성능을 보였다.