• 전자공학회논문지B
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• 제31B권3호
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• pp.91-102
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• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.

• 대한산업공학회지
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• 제31권1호
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• pp.68-78
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• 2005

A new emotion-based image retrieval method is proposed in this paper. The research was motivated by Soen's evaluation of human emotion on color patterns. Thirteen pairs of adjective words expressing emotion pairs such as like-dislike, beautiful-ugly, natural-unnatural, dynamic-static, warm-cold, gay-sober, cheerful-dismal, unstablestable, light-dark, strong-weak, gaudy-plain, hard-soft, heavy-light are modeled by 19-dimensional color array and $4{\times}3$ gray matrix in off-line. Once the query is presented in text format, emotion model-based query formulation produces the associated color array and gray matrix. Then, images related to the query are retrieved from the database based on the multiplication of color array and gray matrix, each of which is extracted from query and database image. Experiments over 450 images showed an average retrieval rate of 0.61 for the use of color array alone and an average retrieval rate of 0.47 for the use of gray matrix alone.

• 한국멀티미디어학회논문지
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• 제20권1호
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• pp.43-50
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• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 1999년도 제29회 춘계학술대회
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• pp.240-246
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• 1999

The effect of microstructure of gray cast iron disk was investigated by using a pad-on-disk type friction tester. Three different rotors with different microstructures were studied in this work. They showed a pearlitic matrix, a ferritic matrix, and a martensitic structure, respectively. All of them have graphite flakes in common. Drag tests at different pressure and speed conditions were carried out to study friction stability, temperature rise during drags. The rotor containing pearlitic matrix showed lower values of friction coefficient, small amount of temperature rise, and less fading. The results showed that gray cast iron disk containing pearlitic matrix has good friction characteristics.

• 한국정밀공학회지
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• 제16권5호통권98호
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• pp.48-57
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• 1999

Applications of thresholding technique are based on the assumption that object and background pixels in a digital image can be distinguished by their gray level values. For the segmentation of more complex images, it is necessary to resort to multiple threshold selection techniques. This paper describes a new method for multiple threshold selection of gray level images which are not clearly distinguishable from the background. The proposed method consists of three main stages. In the first stage, a probability distribution function for a gray level histogram of an image is derived. Cluster points are defined according to the probability distribution function. In the second stage, fuzzy partition matrix of the probability distribution function is generated through the fuzzy clustering process. Finally, elements of the fuzzy partition matrix are classified as clusters according to gray level values by using max-membership method. Boundary values of classified clusters are selected as multiple threshold. In order to verify the performance of the developed algorithm, automatic inspection process of ball grid array is presented.

• 한국기계가공학회지
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• 제16권4호
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• pp.76-82
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• 2017

The cylinder liner of a diesel engine is commonly made of gray cast iron. However, this cylinder liner can be damaged by the cavitation phenomenon in wet conditions. This cavitation has remained an unsolved problem until now. In this study, the cause of cavitation corrosion due to antifreeze solution was examined using a scanning electron microscope (SEM) and a 3D microscope. The necessary data to prevent the damage caused by cavitation erosion and the corrosion of gray cast iron cylinder liner was obtained. Analysis determined that the gray iron structure consists of an ${\alpha}-matrix$, flake graphite, and steadite. Cavitation erosion was initiated in the coarse flake graphite and propagated into the steadite with pitting. Under repetitive reaction conditions, the ${\alpha}-matrix$ was partially separated from the gray cast iron. This study is expected to be used as the basic data for the prevention of gray cast iron cavitation erosion and corrosion by controlling the graphite and steadite phases.

• 한국멀티미디어학회논문지
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• 제21권2호
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• pp.130-137
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• 2018

Recently, human life is getting longer due to change of living environment and development of medical technology, and silver medical technology has been in the limelight. Geriatric skin disease is difficult to detect early, and when it is missed, it becomes a malignant disease and is difficult to treatment. Melanoma is one of the most common diseases of geriatric skin disease and initially has a similar modality with the nevus. In order to overcome this problem, we attempted to perform a feature analysis in order to attempt automatic detection of melanoma-like lesions. In this paper, one is first order analysis using information of pixels in radiomic feature. The other is a gray-level co-occurrence matrix and a gray level run length matrix, which are feature extraction methods for converting image information into a matrix. The features were extracted through these analyses. And classification is implemented by SVM.

• 한국광학회:학술대회논문집
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• 한국광학회 2001년도 하계학술발표회
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• pp.124-125
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• 2001

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1991년도 추계학술대회
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• pp.57-60
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• 1991

A new approach to texture classification for ultrasound liver diagnosis using run difference matrix was developed. The run difference matrix consists of the gray level difference along with distance. From this run difference matrix, we defined several parameters such as LDE, LDEL, NUF, SMO, SMG, SHP etc. and three vectors namely DOD, DGD and DAD. Each parameter value calculated in fatty cirrhotic, chronic hepatitic and normal liver mage was plotted in two dimensional plane. We compared our results with run length method. There are several advantages of run difference matrix method over the run lengths. 1) It is more sensitive to small difference of gray level distribution. 2) The parameters provide more statistically significant value. Images were classified with the extracted parameters to each diseases using neural networks. In preliminary clinical exprements, this approach showed satisfying results.

• 한국지능시스템학회논문지
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• 제25권2호
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• pp.133-138
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• 2015

본 논문에서는 비균일 양자화에 기반을 둔 영상의 질감분석에 널리 이용되고 있는 gray level co-occurrence matrix(GLCM)의 성능개선을 제안하였다. 여기서 비균일 양자화는 평균자승오차의 최소화를 위한 반복계산 기법인 Lloyd 알고리즘을 이용하였다. 이는 영상에서의 비균일 양자화 과정으로 얻어지는 비선형의 명암레벨을 GLCM의 생성에 이용함으로써 행렬의 차원을 감소시켜, GLCM의 생성과 질감특성 파라미터들의 계산에 따른 부하를 줄이기 위함이다. 제안된 기법을 30개의 $120{\times}120$ 픽셀의 256 그레이 레벨을 가진 영상들을 대상으로 적용하여 angular second moment, contrast, variance, entropy, correlation, inverse difference moment 6개의 질감특성 파라미터들을 각각 계산한 실험결과, 양자화를 수행하지 않은 256 레벨 GLCM에 비해 계산시간과 저장 공간에서 개선된 성능이 있음을 확인하였다. 특히 48, 32, 16, 12, 8의 비균일 양자화 레벨 중에서 16일 때 가장 우수한 질감특성분석 성능이 있음을 알 수 있었다.