• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.197-206
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• 2010

High-quality and low-price digital printing devices are nowadays abused to print or forge official documents and bills. Identifying color laser printers will be a step for media forensics. This paper presents a new method to identify color laser printers with printed color images. Since different printer companies use different manufactural systems, printed documents from different printers have little difference in visual. Analyzing this artifact, we can identify the color laser printers. First, high-frequency components of images are extracted from original images with discrete wavelet transform. After calculating the gray-level co-occurrence matrix of the components, we extract some statistical features. Then, these features are applied to train and classify the support vector machine for identifying the color laser printer. In the experiment, total 2,597 images of 7 printers (HP, Canon, Xerox DCC400, Xerox DCC450, Xerox DCC5560, Xerox DCC6540, Konica), are tested to classify the color laser printer. The results prove that the presented identification method performs well with 96.9% accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5073-5086
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• 2015

The rock fracture detection by image analysis is significant for fracture measurement and assessment engineering. The paper proposes a novel image segmentation algorithm for the centerline tracing of a rock fracture based on Hessian Matrix at Multi-scales and Steger algorithm. A traditional fracture detection method, which does edge detection first, then makes image binarization, and finally performs noise removal and fracture gap linking, is difficult for images of rough rock surfaces. To overcome the problem, the new algorithm extracts the centerlines directly from a gray level image. It includes three steps: (1) Hessian Matrix and Frangi filter are adopted to enhance the curvilinear structures, then after image binarization, the spurious-fractures and noise are removed by synthesizing the area, circularity and rectangularity; (2) On the binary image, Steger algorithm is used to detect fracture centerline points, then the centerline points or segments are linked according to the gap distance and the angle differences; and (3) Based on the above centerline detection roughly, the centerline points are searched in the original image in a local window along the direction perpendicular to the normal of the centerline, then these points are linked. A number of rock fracture images have been tested, and the testing results show that compared to other traditional algorithms, the proposed algorithm can extract rock fracture centerlines accurately.

• Proceedings of the IEEK Conference
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• 2001.09a
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• pp.407-410
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• 2001

최근 영상 검색(retrieval)과 분류(classification)에서 질감 특징(texture feature)을 이용한 연구들이 활발하게 진행되고 있다. 본 논문에서는 효율적인 질감 특징 추출을 위해 명암도 상호발생 행렬법(gray level co-occurrence matrix)과 웨이블릿 변환(wavelet transform)을 이용하여 질감의 특징을 추출한 후 특징의 중요도에 따라서 가중치를 부여하는 방법을 제안한다. 이렇게 추출된 가중치 대표 벡터들을 기반으로 베이시안 분류기(Bayesian classifier)를 통해 임의의 질감을 인식하였다.

• 전기의세계
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• v.38 no.10
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• pp.4-12
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• 1989

최근에는 전자 장치가 소형화, 경량화되는 추세이다. 이러한 전자 장치와 인간의 접속 방법으로 표시기(display)가 사용된다. 표시 기술은 표1에 나타난 것처럼 CRT, PDP, LCD, ECD, 기타 등으로 구별할 수 있다. 그중에서 LCD(Liquid Crystal Display)는 낮은 전력 소모, 낮은 구동 전압, 소형, 경량, gray scale 기능, 저가격 등의 장점이 있기 때문에 많은 연구가 수행되고 있다. 액정 지시기는 외부 전장에 의한 분자의 배열이 이루어져, 빛을 선별 투과 혹은 반사시켜 빛의 세기를 조절하는 특성을 이용하여 정보 전달 기능을 갖는다. LCD는 시계, 전자계산기, TV, 컴퓨터 단말기 등에 이용된다. LCD에 사용되는 액정은 구조적 특성에 따라서 semetic, cholestic, nematic액적으로 분류된다. 그런데 nematic액정이 분자 재배열에 필요한 반응 시간이 가장 빠르기 때문에 많이 사용된다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.502-504
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• 2000

현재 내용 기반 검색에 대해서 많은 연구가 이루어지고 있다. 본 논문은 이러한 내용기반 검색 방법중에서 영상의 방향 성분을 이용한 텍스쳐 영상 검색 방법을 제안한다. 본 논문에서 제안하는 검색방법은 웨이블릿(Wavelet) 변화후에 생기는 고대역 부밴드들의 Energy 값을 가지고 텍스쳐 영상의 방향 성분을 구한 다음에 방향 성분에 따른 고대역 부밴드의 Energy와 저대역 부밴드의 GLCM(Gray Level Co-occurence Matrix) Energy 값을 가지고 텍스쳐 영상을 검색하도록 하는 방식으로, 실험을 통해서 검색시 좋은 결과를 보여주는 것을 알 수 있었다.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 2002.08a
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• pp.308-315
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• 2002

해양구조물 설치(Offshore Engineering)에 대한 수요가 급증함에 따라 보다 정확한 설계와 시공을 위한 해저지형 및 지질환경에 대한 탐사(Geophysical Survey)수요가 급격히 증가하고 있다. 전자기파의 감쇄가 심한 해수로 덮여 있는 해저에 대한원격탐사 매체로는 SONAR(SOund Navigation And Ranging)시스템이 일반적으로 이용되고 있다. (중략)

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.737-740
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• 2004

본 논문에서는 차종 식별을 위해 차량 영상의 질감 특징을 사용하였다. 차량의 질감 특징 정보를 얻기 위한 관심영역으로 라디에이터 그릴 부분을 선택하였다. 추출된 관심영역으로부터 GLCM(Gray Level Co-occurrence Matrix)을 사용하여 질감 특징 값을 추출하였고, 그 특징 값들을 입력으로 취하는 3층의 신경회로망을 구성한 후 역전파 학습 알고리즘을 사용하여 학습을 시켜서 차종 식별을 시도하였다.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.403-410
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• 2004

Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.

• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.1020-1023
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• 2002

We successfully developed a-Si TFT controlled active matrix cathode (AMC) with gated Mo emitters. Also, we could remove emitter failures of the AMC through a novel surface treatment of Mo-tips, which indicates reduction of $MoO_3$ or chemical wet etching of $MoO_3$ by surface treatment. Transient behaviors of the AMC are strongly dependent on not only DC characteristics of device but also the device structure. Brightness and gray scale were well realized by low-voltage scan and data signals addressed to a-Si TFTs.

• The Journal of Engineering Geology
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• v.18 no.2
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• pp.145-152
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• 2008

A fault gouge zone which is about 25cm thick crops out along a small valley in Yangbuk-myeon, Gyeongju city. It is divided into greenish brown gouge and bluish gray gouge by color. Under the microscope, the gouges have a lot of porphyroclasts composed of old gouge fragments, quartz, feldspar and iron minerals. Clay minerals are abundant in matrix, defining strikingly P foliation by preferred orientation. Microstructural differences between bluish pay gouge and greenish brown gouge are as follows: greenish brown gouge compared to bluish gray gouge is (1) rich in clay minerals, (2) small in size and number of porphyroclasts, and (3) plentiful in iron minerals which are mostly hematites, while chiefly pyrites in bluish gray gouge. Hematites are considered to be altered from pyrites in the early-formed greenish brown gouge under the influence of hydrothermal fluids accompanied during the formation of bluish gray gouge that also precipitated pyrites. It is believed that the fault core including bluish gray gouge zone and greenish brown gouge zone was formed by progressive cataclastic flow. In the first stage the fault core initiates from damage zone of early faulting. In the second stage damage zone actively transforms into breccia zone by repeated fracturing. The third stage includes greenish brown (old) gouge formation in the center of the fault core mainly by particle grinding. In the third stage further deformation leads to the formation of new (bluish gray) gouge zone while old gouge zone undergoes strain hardening. Consequently, the whole gouge zone in the core widens.