The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 "마블링 스코어" 개념과 질감 분석을 이용하여 육질을 판단하는 방법을 기술한다. 마블링 점수는 등뼈 부위의 지방 밀도 분포를 측정한 것입니다. 그레이더에게 설문지를 나눠 조사한 결과를 바탕으로 고기의 마블링을 질감 패턴으로 간주하고 질감 특징을 활용한 등급 시스템 구현 방법을 제안합니다. 본 시스템에서는 텍스처 영상의 그레이 레벨에 대한 일반적인 XNUMX차 통계인 그레이 레벨 동시 발생 매트릭스를 텍스처 특징으로 사용하고, 이를 기반으로 각 등급에 대한 표준 텍스처-특징 벡터를 결정한다. 평가되지 않은 이미지의 등급은 평가되지 않은 이미지의 텍스처-특징 벡터를 표준 텍스처-특징 벡터와 비교하여 결정됩니다. 실험 결과는 제안된 방법이 효과적임을 보여주었다.
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부
Kazuhiko SHIRANITA, Kenichiro HAYASHI, Akifumi OTSUBO, "Determination of Meat Quality Using Texture Features" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 9, pp. 1790-1796, September 2000, doi: .
Abstract: In this paper, we describe a method of determining meat quality using the concepts of "marbling score" and texture analysis. The marbling score is a measure of the density distribution of fat in the rib-eye region. Based on the results of an investigation carried out by handing out questionnaires to graders, we consider the marbling of meat to be a texture pattern and propose a method for the implementation of a grading system using a texture feature. In this system, we use a gray level co-occurrence matrix as the texture feature, which is a typical second-order statistic of gray levels of a texture image, and determine standard texture-feature vectors for each grade based on it. The grade of an unevaluated image is determined by comparing the texture-feature vector of this unevaluated image with the standard texture-feature vectors. Experimental results show the proposed method to be effective.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_9_1790/_p
부
@ARTICLE{e83-d_9_1790,
author={Kazuhiko SHIRANITA, Kenichiro HAYASHI, Akifumi OTSUBO, },
journal={IEICE TRANSACTIONS on Information},
title={Determination of Meat Quality Using Texture Features},
year={2000},
volume={E83-D},
number={9},
pages={1790-1796},
abstract={In this paper, we describe a method of determining meat quality using the concepts of "marbling score" and texture analysis. The marbling score is a measure of the density distribution of fat in the rib-eye region. Based on the results of an investigation carried out by handing out questionnaires to graders, we consider the marbling of meat to be a texture pattern and propose a method for the implementation of a grading system using a texture feature. In this system, we use a gray level co-occurrence matrix as the texture feature, which is a typical second-order statistic of gray levels of a texture image, and determine standard texture-feature vectors for each grade based on it. The grade of an unevaluated image is determined by comparing the texture-feature vector of this unevaluated image with the standard texture-feature vectors. Experimental results show the proposed method to be effective.},
keywords={},
doi={},
ISSN={},
month={September},}
부
TY - JOUR
TI - Determination of Meat Quality Using Texture Features
T2 - IEICE TRANSACTIONS on Information
SP - 1790
EP - 1796
AU - Kazuhiko SHIRANITA
AU - Kenichiro HAYASHI
AU - Akifumi OTSUBO
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2000
AB - In this paper, we describe a method of determining meat quality using the concepts of "marbling score" and texture analysis. The marbling score is a measure of the density distribution of fat in the rib-eye region. Based on the results of an investigation carried out by handing out questionnaires to graders, we consider the marbling of meat to be a texture pattern and propose a method for the implementation of a grading system using a texture feature. In this system, we use a gray level co-occurrence matrix as the texture feature, which is a typical second-order statistic of gray levels of a texture image, and determine standard texture-feature vectors for each grade based on it. The grade of an unevaluated image is determined by comparing the texture-feature vector of this unevaluated image with the standard texture-feature vectors. Experimental results show the proposed method to be effective.
ER -