A Computer Vision System for Automatic Classification of Most Consumed Brazilian Beans

2015 ◽  
pp. 45-53 ◽  
Author(s):  
S. A. Araújo ◽  
W. A. L. Alves ◽  
P. A. Belan ◽  
K. P. Anselmo
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Automatic Classification ◽  
Computer Vision System

scholarly journals Study and classification of the Crystallographic Orientation Distribution Function of a non-grain oriented electrical steel using computer vision system

2019 ◽  
Vol 8 (1) ◽  
pp. 1070-1083
Author(s):  
Roberto Fernandes Ivo ◽  
Douglas de Araújo Rodrigues ◽  
José Ciro dos Santos ◽  
Francisco Nélio Costa Freitas ◽  
Luis Flaávio Gaspar Herculano ◽  
...  
Keyword(s):  
Computer Vision ◽  
Distribution Function ◽  
Orientation Distribution Function ◽  
Crystallographic Orientation ◽  
Electrical Steel ◽  
Vision System ◽  
Orientation Distribution ◽  
Computer Vision System

A computer vision system for the detection and classification of vehicles at urban road intersections

2005 ◽  
Vol 8 (1-2) ◽  
pp. 17-31 ◽  
Author(s):  
Stefano Messelodi ◽  
Carla Maria Modena ◽  
Michele Zanin
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Urban Road ◽  
Road Intersections

scholarly journals Estimation of fat cover of bovine carcases by means of computer vision system (CVS)

2021 ◽  
Vol 854 (1) ◽  
pp. 012087
Author(s):  
S Simunovic ◽  
S Rajic ◽  
V Djordjevic ◽  
V Tomovic ◽  
D Vujadinovic ◽  
...  
Keyword(s):  
Decision Making ◽  
Computer Vision ◽  
Classification System ◽  
Vision System ◽  
Fat Content ◽  
Computer Vision System ◽  
The Future ◽  
Reference Images ◽  
Very High

Abstract The aims of this study were to obtain percentages of meat and fat cover for SEUROP classification system reference images using a computer vision system (CVS) and to calculate classification intervals which could be used in the future for construction of cheap and easy to use classification devices for small slaughterhouses. Lowest percentages of fat cover were found for the first class marked as “low” (the lowest fat content) and they gradually increased to the last class marked as “very high” (the highest fat content). Based on the obtained results, decision making intervals were proposed. In the present study, classification only refers to classification of adult bovine animals based on fat cover.

Computer vision system and near-infrared spectroscopy for identification and classification of chicken with wooden breast, and physicochemical and technological characterization

2019 ◽  
Vol 96 ◽  
pp. 303-310 ◽  
Author(s):  
Bruna Caroline Geronimo ◽  
Saulo Martiello Mastelini ◽  
Rafael Humberto Carvalho ◽  
Sylvio Barbon Júnior ◽  
Douglas Fernandes Barbin ◽  
...  
Keyword(s):  
Computer Vision ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Vision System ◽  
Computer Vision System ◽  
Wooden Breast

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.

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