• 제목/요약/키워드: texture analysis

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구조적 특성을 갖는 Texture 영상의 해석 (The Analysis of Texture Images with Structural Characteristics)

  • 갑재섭;박래홍
    • 대한전자공학회논문지
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    • 제24권4호
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    • pp.675-683
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    • 1987
  • In general, texture images with regular patterns can be described by using the standard texture model regularity vectors for their shape analysis. Early methods not only take much time but also have computational complexity in obtaining regularity vectors. The proposed some improved preprocessing algorithms for texture analysis. Finally, we showed the utility of the proposed method through texture synthesis by making use of the results of texture analysis.

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RHEOLOGY - TEXTURE ANALYSIS: new keys for access to cosmetic formulation texture.

  • Roso, Alicia;Brinet, Riva
    • 대한화장품학회:학술대회논문집
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    • 대한화장품학회 2003년도 IFSCC Conference Proceeding Book II
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    • pp.286-293
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    • 2003
  • In cosmetic formulations, texture plays a key role in ingredient choice and formulation optimization. But texture parameters are often measured by sensorial analysis in the last stages of formulation development. Rheology or texture analysis, used separately, has the benefit of characterizing the behavior of raw materials (e.g. polymers) and controlling and predicting the stability of formulations. SEPPIC has developed rheology and texture analysis protocols to obtain a better understanding of the influence of raw materials on the cosmetic texture of formulations. When used in combination, these two methodologies are complementary and provide useful data regarding the impact of raw material choice on all the development steps: manufacturing procedure, formulation stability, skin feeling.

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GLCM/GLDV 기반 Texture 알고리즘 구현과 고 해상도 영상분석 적용 (Implementation of GLCM/GLDV-based Texture Algorithm and Its Application to High Resolution Imagery Analysis)

  • 이기원;전소희;권병두
    • 대한원격탐사학회지
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    • 제21권2호
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    • pp.121-133
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    • 2005
  • 화소들 사이의 관계를 고려해 Texture 영상을 생성해 내는 것을 의미하는 Texture 영상화는 유용한 영상 분석 방법 중의 하나로 잘 알려져 있고, 대부분의 상업적인 원격 탐사 소프트웨어들은 GLCM이라는 Texture 분석 기능을 제공하고 있다. 본 연구에서는, GLCM 알고리즘에 기반한 Texture 영상화 프로그램이 구현되었고, 추가적으로 GLDV에 기반을 둔 Texture 영상화 모듈 프로그램을 제공한다. 본 프로그램에서는 Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment(ASM), Contrast 등과 같은 GLCN/GLDV의 6가지 Texture 변수에 따라 각각 이에 해당하는 Texture 영상들을 생성해 낸다. GLCM/GLDV Texture 영상 생성에서는 방향 의존성을 고려해야 하는데, 이 프로그램에서는 기본적으로 동-서, 북동-남서, 북-남, 북서-남동 등의 기본적인 방향설정을 제공한다. 또한 이 논문에서 새롭게 구현된 커널내의 모든 방향을 고려해서 평균값을 계산하는 Omni 방향 모드와 커널내의 중심 화소를 정하고_그 주변 화소에 대한 원형 방향을 고려하는 원형방향 모드를 지원한다. 또한 본 연구에서는 여러 가지 변수와 모드에 따라 얻어진 Texture 영상의 분석을 위하여 가상 영상 및 실제 위성 영상들에 의하여 생성된 Texture 영상간의 특징 분석과 상호상관 분석을 수행하였다. Texture 영상합성 응용시에는 영상의 생성시에 적용된 변수들에 대한 이해와 영상간의 상관도를 분석하는 과정이 필요할 것으로 생각된다.

Evaluation of Volumetric Texture Features for Computerized Cell Nuclei Grading

  • Kim, Tae-Yun;Choi, Hyun-Ju;Choi, Heung-Kook
    • 한국멀티미디어학회논문지
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    • 제11권12호
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    • pp.1635-1648
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    • 2008
  • The extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we applied three-dimensional (3D) texture feature extraction methods to cell nuclei images and evaluated the validity of them for computerized cell nuclei grading. Individual images of 2,423 cell nuclei were extracted from 80 renal cell carcinomas (RCCs) using confocal laser scanning microscopy (CLSM). First, we applied the 3D texture mapping method to render the volume of entire tissue sections. Then, we determined the chromatin texture quantitatively by calculating 3D gray-level co-occurrence matrices (3D GLCM) and 3D run length matrices (3D GLRLM). Finally, to demonstrate the suitability of 3D texture features for grading, we performed a discriminant analysis. In addition, we conducted a principal component analysis to obtain optimized texture features. Automatic grading of cell nuclei using 3D texture features had an accuracy of 78.30%. Combining 3D textural and 3D morphological features improved the accuracy to 82.19%. As a comparative study, we also performed a stepwise feature selection. Using the 4 optimized features, we could obtain more improved accuracy of 84.32%. Three dimensional texture features have potential for use as fundamental elements in developing a new nuclear grading system with accurate diagnosis and predicting prognosis.

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Region of Interest Heterogeneity Assessment for Image using Texture Analysis

  • Park, Yong Sung;Kang, Joo Hyun;Lim, Sang Moo;Woo, Sang-Keun
    • 한국컴퓨터정보학회논문지
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    • 제21권11호
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    • pp.17-21
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    • 2016
  • Heterogeneity assessment of tumor in oncology is important for diagnosis of cancer and therapy. The aim of this study was performed assess heterogeneity tumor region in PET image using texture analysis. For assessment of heterogeneity tumor in PET image, we inserted sphere phantom in torso phantom. Cu-64 labeled radioisotope was administrated by 156.84 MBq in torso phantom. PET/CT image was acquired by PET/CT scanner (Discovery 710, GE Healthcare, Milwaukee, WI). The texture analysis of PET images was calculated using occurrence probability of gray level co-occurrence matrix. Energy and entropy is one of results of texture analysis. We performed the texture analysis in tumor, liver, and background. Assessment textural features of region-of-interest (ROI) in torso phantom used in-house software. We calculated the textural features of torso phantom in PET image using texture analysis. Calculated entropy in tumor, liver, and background were 5.322, 7.639, and 7.818. The further study will perform assessment of heterogeneity using clinical tumor PET image.

Texture Electron Diffraction Pattern에 의한 결정구조 해석 (Crystal Structure Analysis by Texture Electron Diffraction Pattern)

  • 이수정;주형태;김윤중;문희수
    • Applied Microscopy
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    • 제32권3호
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    • pp.185-193
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    • 2002
  • Texture electron diffraction pattern을 이용한 결정구조 해석 이론은 러시아어로 씌여졌거나, 영문판 저서의 일부에 간단히 소개 되어있어 이해에 어려움이 있다. 이들의 이론은 벡터의 이론과 관련된 여러 관계식을 이용해서 설명될 수 있으며, 이로서 몇 개의 식에 포함된 오류를 수정하였다.

Implementation for Texture Imaging Algorithm based on GLCM/GLDV and Use Case Experiments with High Resolution Imagery

  • Jeon So Hee;Lee Kiwon;Kwon Byung-Doo
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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    • pp.626-629
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    • 2004
  • Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program for GLCM algorithm is newly implemented in the MS Visual IDE environment. While, additional texture imaging modules based on GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV texture variables, it composed of six types of second order texture function in the several quantization levels of 2(binary image), 8, and 16: Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality, four directions are provided as $E-W(0^{\circ}),\;N-E(45^{\circ}),\;S-W(135^{\circ}),\;and\;N-S(90^{\circ}),$ and W-E direction is also considered in the negative direction of E- W direction. While, two direction modes are provided in this program: Omni-mode and Circular mode. Omni-mode is to compute all direction to avoid directionality problem, and circular direction is to compute texture variables by circular direction surrounding target pixel. At the second phase of this study, some examples with artificial image and actual satellite imagery are carried out to demonstrate effectiveness of texture imaging or to help texture image interpretation. As the reference, most previous studies related to texture image analysis have been used for the classification purpose, but this study aims at the creation and general uses of texture image for urban remote sensing.

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투사정보를 이용한 구조적 텍스처의 분석 및 합성 (Analysis and Synthesis of Structural Textures Using Projection Information)

  • 김한빈;박래홍
    • 대한전자공학회논문지
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    • 제26권9호
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    • pp.1428-1435
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    • 1989
  • In this paper we propose a new algorithm which extracts spatial arrangement information of texture elements in structural textures. In the proposed algorithm, by the use of projection information in several directions obtained from the texture image we can get two directions which determine the texture structure and the parallelogram grid which isolates texture elements. The isolated texture elements are analyzed and used to synthesize texture images. Computer simulation shows that the proposed method can extract proper spatial structure of the texture element even when the texture image is highly corrupted by additive noise.

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합성곱신경망을 이용한 구조적 텍스처 분석연구 (A Study on the Analysis of Structural Textures using CNN (Convolution Neural Network))

  • 이봉규
    • 한국인터넷방송통신학회논문지
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    • 제20권4호
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    • pp.201-205
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    • 2020
  • 구조적인 텍스처는 텍스처를 구성하는 기본요소인 텍셀 (texel)이 규칙적으로 반복되는 형태로 정의된다. 구조적 텍스처 분석/인식은 직물류의 자동검사, 금속표면 자동테스트 및 마이크로 이미지의 자동 분석 등, 산업적인 응용이 다양하다. 본 논문에서는 구조적 텍스처 분석을 위한 합성곱신경망 (Convolution Neural Network, CNN) 기반의 시스템을 제안한다. 제안한 방법은 합성곱신경망이 분류 대상 텍스처들의 구성 요소인 텍셀을 학습한다. 인식 단계에서는 입력되는 텍스처 영상에서 얻은 부분 영상을 이용하여 학습된 합성곱신경망이 텍스처를 인식하다. 실제 구현 및 실험을 통하여 제안된 방법의 우수성을 보인다.

의복재질의 시각적 감성연구 (A Study on the Visual Sensibility of Clothing Texture)

  • 오해순;이경희
    • 한국의류학회지
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    • 제26권10호
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    • pp.1412-1423
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    • 2002
  • The purpose of this study is to objectively explain the visual sensibility of clothing torture that satisfies the consumer's sensibility. The photo stimuli on clothing texture are divided into hard, soft transparent and brilliant. For the study of image 38 kinds of costume samples is used. The Study was measured by using Semantic Differential method. The subjects were 410 females in twenties. The data were analyzed by factor analysis, ANOVA, MDS and regression analysis. Data were analyzed by SPSS. The major findings of this research were as follows: 1. As a result of the factor analysis,5 factors of visual sensibility were consist of high qualities, touches, looks, lightness, and warmness or coolness.2. There were significant difference in visual sensibility based on classification of clothing texture.3. The clothing texture was classified as thin-full, flat-lumpy. 4. As a result of the regression analysis, preferences of consumers can be connected directly with buying behavior and satisfaction can be closely related with preferences and positive buying behavior.