On a Fluency Image Coding System for Beef Marbling Evaluation

Toraichi, Kazuo; Kwan, Paul; Katagishi, Kazuki; Sugiyama, Tetsuo; Wada, Koichi; Mitsumoto, Mitsuru; Nakai, Hiroyasu; Yoshikawa, Fumito

doi:10.1016/s0167-8655(02)00077-6

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…Traditionally, evaluation of marbling is conducted by trained graders that estimates a score by comparing the proportion of intramuscular fat in the longissimus dorsi muscles with a reference standard (Liu et al, 2012;Yoshikawa et al, 2000). Therefore, in the last decades some researches about objecttive marbling assessment has been carried out for beef and pork (Shiranita et al, 2000;Yoshikawa et al, 2000;Toraichi et al, 2002;Jackman et al, 2008;Huang et al, 2012). Hyperspectral imaging technology (HSI) has been considered as a promising tool for evaluation of food quality and safety (Siche et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Prediction of beef marbling using Hyperspectral Imaging (HSI) and Partial Least Squares Regression (PLSR)

Aredo¹,

Velásquez²,

Siché³

2017

Sci. Agropecu

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The aim of this study was to build a model to predict the beef marbling using HSI and Partial Least Squares Regression (PLSR). Totally 58 samples of longissmus dorsi muscle were scanned by a HSI system (400 -1000 nm) in reflectance mode, using 44 samples to build the PLSR model and 14 samples to model validation. The Japanese Beef Marbling Standard (BMS) was used as reference by 15 middle-trained judges for the samples evaluation. The scores were assigned as continuous values and varied from 1.2 to 5.3 BMS. The PLSR model showed a high correlation coefficient in the prediction (r = 0.95), a low Standard Error of Calibration (SEC) of 0.2 BMS score, and a low Standard Error of Prediction (SEP) of 0.3 BMS score.

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Section: Introductionmentioning

confidence: 99%

Prediction of beef marbling using Hyperspectral Imaging (HSI) and Partial Least Squares Regression (PLSR)

Aredo¹,

Velásquez²,

Siché³

2017

Sci. Agropecu

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“…Visual standards grade the marbling in seven scores by the National Pork Board (NPB, 1999). Some research works were reported for assessing beef or pork marbling by machine vision (Faucitano, Huff, Teuscher, Gariepy, & Wegner, 2005;Shiranita, Hayashi, Otsubo, Miyajima, & Takiyama, 2000;Tan, 2004;Toraichi et al, 2002;Yoshikawa et al, 2000). Shiranita, Miyajima, and Takiyama (1998) used a co-occurrence matrix to extract standard texture vectors from beef samples sorted by professional grader and successfully classified the unevaluated samples.…”

Section: Introductionmentioning

confidence: 99%

Pork quality and marbling level assessment using a hyperspectral imaging system

Qiao

Ngadi

Wang

et al. 2007

Journal of Food Engineering

186

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“…To the straight line, transformation to required resolution can be realized by multiplying transformation ratio to their parameters directly. To the transformation of the quadratic Fluency sampling function that takes the coordinates of a sequence of pixels as its parameters, we can refer to the algorithm (15) proposed by one of the authors of this paper.…”

Section: Procedures Of Blood-vessel Shape Map Creation Of Required Resmentioning

confidence: 99%

A Shape-Directed Scaling Method for Fundus Image with Maintenance to Blood-Vessel Shapes and Color Reality

et al. 2005

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Non-member When digitized fundus images are used in medicine, important information for clinicians' diagnosis should be maintained well after scaling. Conventional scaling methods select the interpolation kernel based on Shannon's sampling theorem which is only appropriate for band-limited signals and usually generate image with jaggy noise or blurred blood-vessel shapes that the tortuosity and diameter change of blood vessels which are important information to clinicians' diagnosis can not be maintained clearly. To solve these problems, we select the quadratic Fluency sampling function as the interpolation kernel to maintain the color reality of fundus image based on the Fluency information theory. Scaling is then realized by interpolation directed to the blood-vessel shape map of required resolution which is generated from two kinds of Fluency sampling functions. These two kinds of Fluency sampling functions are precedingly utilized to approximate shapes of blood vessels on the original fundus image according to the suggestion of specialists in fundus field. Resulting images by the proposed method show good shape maintenance of blood vessels and have a better quantitative evaluation than the generally recognized best conventional method.

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On a Fluency Image Coding System for Beef Marbling Evaluation

Cited by 16 publications

References 14 publications

Prediction of beef marbling using Hyperspectral Imaging (HSI) and Partial Least Squares Regression (PLSR)

Prediction of beef marbling using Hyperspectral Imaging (HSI) and Partial Least Squares Regression (PLSR)

Pork quality and marbling level assessment using a hyperspectral imaging system

A Shape-Directed Scaling Method for Fundus Image with Maintenance to Blood-Vessel Shapes and Color Reality

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