Analysis of Meat Color Change using Computer Vision

2020 ◽  
Author(s):  
Gustavo Meza ◽  
Claudia N. Sanchez ◽  
Maria T. Orvananos-Guerrero ◽  
Julieta Dominguez-Soberanes
Keyword(s):  
Computer Vision ◽  
Color Change ◽  
Meat Color

Study on Color Classification of Pork with Computer Vision System Based on Different Color Difference Formula

2011 ◽  
Vol 474-476 ◽  
pp. 1638-1642 ◽  
Author(s):  
Hong Xia Zhao ◽  
Zhi Yang Luo ◽  
Xin Yuan ◽  
Yuan Li
Keyword(s):  
Computer Vision ◽  
Color Change ◽  
Vision System ◽  
Close Relation ◽  
Color Difference ◽  
Computer Vision System ◽  
Color Classification ◽  
Difference Formula ◽  
Different Color

The bad degree of meat and the color change have a close relation. Computer vision is not under the influence of physiological and psychological factor of person; Appraisement standard is objective and steady. Therefore, computer vision system has been established, which can do collection, management, display, and appraisement of porky color. The CIEL*a*b* and CIEDE2000 color difference formula were introduced into the classification of pork, the result was identical. Because the calculation course of CIEDE2000 color difference is complex, so the CIE1976L*a*b* color difference is still adopted to calculate color change at actual production.

Measurement of meat color using a computer vision system

Meat Science ◽  
2013 ◽  
Vol 93 (1) ◽  
pp. 111-118 ◽  
Author(s):  
Antonio Girolami ◽  
Fabio Napolitano ◽  
Daniela Faraone ◽  
Ada Braghieri
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Meat Color ◽  
Computer Vision System

Color Change Can Be Earliest Sign of Melanoma

2005 ◽  
Vol 38 (22) ◽  
pp. 16
Author(s):  
TIMOTHY F. KIRN
Keyword(s):  
Color Change

Human Vision and Computer Vision

1985 ◽  
Vol 30 (1) ◽  
pp. 47-47
Author(s):  
Herman Bouma
Keyword(s):  
Computer Vision ◽  
Human Vision

Computer vision for industrial applications

1983 ◽  
Vol 2 (5) ◽  
pp. 130
Author(s):  
J.A. Losty ◽  
P.R. Watkins
Keyword(s):  
Computer Vision ◽  
Industrial Applications

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

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