scholarly journals Image Analysis Prediction of Beef Carcass Composition from the Cross Section

1992 ◽  
Vol 63 (8) ◽  
pp. 846-854
Author(s):  
Katsuhito ANADA ◽  
Yoshiyuki SASAKI
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Carcass Composition ◽  
The Cross ◽  
Analysis Prediction ◽  
Beef Carcass

Estimation of carcass composition by computer image analysis in the cross sections of cross-bred steers

1996 ◽  
Vol 76 (4) ◽  
pp. 497-506 ◽  
Author(s):  
A. B. Karnuah ◽  
K. Moriya ◽  
Y. Sasaki ◽  
K. Mitani ◽  
T. Yamazaki
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Computer Image ◽  
Correlation Coefficients ◽  
Carcass Composition ◽  
Data Sets ◽  
Computer Image Analysis ◽  
Data Set ◽  
Stepwise Procedure ◽  
The Cross

Estimation equations for carcass composition were obtained using the information extracted from the carcass cross section by Computer Image Analysis (CIA). The total kilograms of lean, fat, and bone, and their percentages, were measured on the left side of the carcasses of F1 (cross-bred between Japanese Black and Holstein) steers by physical dissection. Traced data of the cross section between the 5th and 6th ribs (Data set I) and pictures of carcass cross section between the 7th and 8th ribs (Data set II) were subjected to image analysis. Various information on both the individual muscles and the overall outline of the cross section was extracted by the CIA technique. Maximum R2 improvement method of the stepwise procedure was used to choose the best regression equation to estimate carcass composition as total kilograms and percentages of lean, fat, and bone. The data sets were also adjusted for age and the stepwise procedure was also conducted. Coefficients of determination, adjusted for the degrees of freedom (adjusted R2) of the regression equations for estimating carcass composition, were high, i.e., 0.779 to 0.959 for kilograms of lean, fat, and bone, whereas for the percentages of lean, fat, and bone were high, i.e., 0.788 to 0.952, respectively. For the adjusted data, the adjusted R2 for estimating kilograms of lean, fat, and bone with Data sets I and II were 0.729, 0.633, and 0.598, and 0.813, 0.806, and 0.878, respectively, while for the percentages of lean, fat, and bone were 0.793, 0.623, and 0.378, and 0.953, 0.989, and 0.467, respectively. When the estimation equation obtained from the unadjusted Data set I was fitted with the information extracted from Data set II, the correlation coefficients between the values estimated by the equation and the values obtained by physical dissection on carcass composition were high, ranging from 0.70 to 0.92. On the other hand, the correlation coefficients obtained from the adjusted data sets were low. Key words: Estimation equation, computer image analysis, carcass composition, carcass cross section, F1 steers

scholarly journals Estimation of Carcass Composition from the Cross-Section at Ribloin of Japanese Black Steers by Computer Image Analysis

2001 ◽  
Vol 72 (9) ◽  
pp. 313-320
Author(s):  
Toshihiro NADE ◽  
Arthur Bob KARNUAH ◽  
Yasuhisa MASUDA ◽  
Satsuki HIRABARA ◽  
Kazuhisa FUJITA
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Computer Image ◽  
Carcass Composition ◽  
Computer Image Analysis ◽  
The Cross

A Novel User-Friendly Image Analysis Technique for Determination of Electrospun Nanofibrous Mats Thickness

NANO ◽  
2017 ◽  
Vol 12 (11) ◽  
pp. 1750130 ◽  
Author(s):  
Bentolhoda Hadavi Moghadam ◽  
Shohreh Kasaei ◽  
A. K. Haghi
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Depth Estimation ◽  
Thickness Measurement ◽  
Relative Depth ◽  
Image Analysis Technique ◽  
Analysis Technique ◽  
The Cross ◽  
User Friendly

In this study, a novel technique for measuring the thickness of electrospun nanofibrous mat based on image analysis techniques is proposed. The thicknesses of electrospun polyacrylonitrile (PAN), polyvinyl alcohol (PVA), and polyurethane (PU) nanofibrous mats are calculated using depth estimation in different views. The images are captured by a fixed scanning electron microscope (SEM) where the mat sample is rotated by 15[Formula: see text], 30[Formula: see text], and 45[Formula: see text] angles. By calculating the disparity value (the distance between two corresponding points in two images), the relative depth of images and consequently the thickness of nanofibrous mat are obtained. Furthermore, the thickness of three electrospun mats are measured from the cross-section view of the nanofibrous mat by scanning the electron microscopy. A close agreement between results obtained by this method at low angle views (15[Formula: see text]) and the direct thickness measurement obtained from the cross-section view is achieved. Comparison of the average thickness from the direct measurement and the proposed method for different samples exhibits a linear relationship with the high regression coefficient of 0.96. By using the proposed method, the quantitative evaluation of the thickness measurement becomes feasible over the entire surface of electrospun mats.

scholarly journals Estimation of Carcass Composition by Computer Image Analysis in the Cross Sections of Japanese Black Steers

1995 ◽  
Vol 66 (12) ◽  
pp. 987-993
Author(s):  
Arthur Bob KARNUAH ◽  
Kazuyuki MORIYA ◽  
Yoshiyuki SASAKI ◽  
Mitsuru MITSUMOTO ◽  
Tadayoshi MITSUHASHI ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cross Sections ◽  
Computer Image ◽  
Carcass Composition ◽  
Computer Image Analysis ◽  
The Cross

scholarly journals Computer Image Analysis Information Extracted from Beef Carcass Cross Section and its Precision

1994 ◽  
Vol 65 (6) ◽  
pp. 515-524
Author(s):  
Arthur Bob KARNUAH ◽  
Kazuyuki MORIYA ◽  
Yoshiyuki SASAKI
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Computer Image ◽  
Computer Image Analysis ◽  
Beef Carcass

scholarly journals Evaluation and genetics of carcass cross section of beef carcass by computer image analysis

2006 ◽  
Vol 34 (2) ◽  
pp. 45-52 ◽  
Author(s):  
Keigo KUCHIDA ◽  
Takefumi OSAWA ◽  
Takeshi HORI ◽  
Hitoshige KOTAKA ◽  
Shin MARUYAMA
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Computer Image ◽  
Computer Image Analysis ◽  
Beef Carcass

Estimation of the carcass composition from a cross-section of the rib-loin from crossbred Japanese Black × Limousin F2 cattle by computer image analysis

2007 ◽  
Vol 78 (6) ◽  
pp. 567-574 ◽  
Author(s):  
Toshihiro NADE ◽  
Jun-ichi SABURI ◽  
Tsuyosi ABE ◽  
Tetsuo NAKAGAWA ◽  
Toshiaki OKUMURA ◽  
...  
Keyword(s):  
Image Analysis ◽  
Cross Section ◽  
Computer Image ◽  
Carcass Composition ◽  
Computer Image Analysis

DISTRIBUTION OF SHEAR STRAIN IN SQUARE CROSS-SECTION SHAFT MEASURED USING IMAGE ANALYSIS UNDER LARGE DEFORMATION

2020 ◽  
Vol 7 (2) ◽  
Author(s):  
Yasuyuki Kato
Keyword(s):  
Image Analysis ◽  
Natural Rubber ◽  
Shear Strain ◽  
Large Deformation ◽  
Cross Section ◽  
Image Data ◽  
Gauge Length ◽  
Strain Theory ◽  
Test Pieces ◽  
The Cross

In the present study, the distribution of shear strain and warping in a square cross-section shaft generated under a large torsion is investigated using the image analysis based on the Natural Strain theory. The scribe lines are drawn in a grid on a surface of the test pieces made of natural rubber, and the image data of each element in the horizontal direction along the cross-section at the center and upper positions are taken by using a high-pixel camera equipped with macro lens. The distributions for shear strain and warping along the cross-section are obtained from that image data. These measured distributions under large deformation are compared with the distributions based on the conventional torsional theory of a square cross-section shaft, i.e., Saint Venant's theory for torsion. Moreover, taking the effect due to the elongation on the surface of specimens into consideration, the distributions modified elongation of gauge length are compared with experiments, and the validity of the present experimental results are confirmed in this paper.

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