• Title, Summary, Keyword: texture analysis

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

  • 갑재섭;박래홍
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.24 no.4
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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
    • Proceedings of the SCSK Conference
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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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Implementation of GLCM/GLDV-based Texture Algorithm and Its Application to High Resolution Imagery Analysis (GLCM/GLDV 기반 Texture 알고리즘 구현과 고 해상도 영상분석 적용)

  • Lee Kiwon;Jeon So-Hee;Kwon Byung-Doo
    • Korean Journal of Remote Sensing
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    • v.21 no.2
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    • pp.121-133
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    • 2005
  • Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of the 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 based on GLCM algorithm is newly implemented. As well, texture imaging modules for GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV Texture imaging parameters, it composed of six types of second order texture functions such as Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality in GLCM/GLDV, two direction modes such as Omni-mode and Circular mode newly implemented in this program are provided with basic eight-direction mode. Omni-mode is to compute all direction to avoid directionality complexity in the practical level, and circular direction is to compute texture parameters by circular direction surrounding a target pixel in a kernel. At the second phase of this study, some case studies with artificial image and actual satellite imagery are carried out to analyze texture images in different parameters and modes by correlation matrix analysis. It is concluded that selection of texture parameters and modes is the critical issues in an application based on texture image fusion.

Evaluation of Volumetric Texture Features for Computerized Cell Nuclei Grading

  • Kim, Tae-Yun;Choi, Hyun-Ju;Choi, Heung-Kook
    • Journal of Korea Multimedia Society
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    • v.11 no.12
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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
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.11
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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.

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

  • Lee, Su-Jeong;Jou, Hyeong-Tae;Kim, Youn-Joong;Moon, Hi-Soo
    • Applied Microscopy
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    • v.32 no.3
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    • pp.185-193
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    • 2002
  • The works of texture electron diffraction patterns for crystal structure analysis are written in Russian or introduced briefly in books written in English, which makes it difficult to be understood. In addition to working out the equations, vector theory corrects some errors included in the established formulas for texture electron diffraction patterns.

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
    • Proceedings of the KSRS Conference
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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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Analysis and Synthesis of Structural Textures Using Projection Information (투사정보를 이용한 구조적 텍스처의 분석 및 합성)

  • 김한빈;박래홍
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.26 no.9
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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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A Study on the Analysis of Structural Textures using CNN (Convolution Neural Network) (합성곱신경망을 이용한 구조적 텍스처 분석연구)

  • Lee, Bongkyu
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.4
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    • pp.201-205
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    • 2020
  • The structural texture is defined as a form which a texel is regularly repeated in the texture. Structural texture analysis/recognition has various industrial applications, such as automatic inspection of textiles, automatic testing of metal surfaces, and automatic analysis of micro images. In this paper, we propose a Convolution Neural Network (CNN) based system for structural texture analysis. The proposed method learns texles, which are components of textures to be classified. Then, this trained CNN recognizes a structural texture using a partial image obtained from input texture. The experiment shows the superiority of the proposed system.

Texture Classification Based on Gabor-like Feature (유사 가버 특징에 기반한 텍스쳐 분류)

  • Son, Ji-Hoon;Kim, Sung-Young
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.10 no.2
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    • pp.147-153
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    • 2017
  • Efficient texture representation is very important in computer vision fields. The performance of texture classification or/and segmentation can be improved based on efficient texture representation. Gabor filter is a representation method that has long history for texture representation based on multi-scale analysis. Gabor filter shows good performance in texture classification and segmentation but requires much processing time. In this paper, we propose new texture representation method that is also based on multi-scale analysis. The proposed representation can provide similar performance in texture classification but can reduce processing time against Gabor filter. Experimental results show good performance of our method.