scholarly journals Color measurement of animal source foods

2022 ◽  
Vol 6 (4) ◽  
pp. 311-319
Author(s):  
B. R. Milovanovic ◽  
I. V. Djekic ◽  
V. M. Tomović ◽  
D. Vujadinović ◽  
I. B. Tomasevic
Keyword(s):  
Quality Control ◽  
Vision System ◽  
Objective Assessment ◽  
Color Measurement ◽  
Color Analysis ◽  
Computer Vision System ◽  
Animal Source Foods ◽  
Animal Source ◽  
Study Results ◽  
Perceived Color

Rapid and objective assessment of food color is necessary in quality control. The color evaluation of animal source foods using a computer vision system (CVS) and a traditional colorimeter is examined. With the same measurement conditions, color results deviated between these two approaches. The color returned by the CVS had a close resemblance to the perceived color of the animal source foods, whereas the colorimeter returned not typical colors. The effectiveness of the CVS is confirmed by the study results. Considering these data, it could be concluded that the colorimeter is not representative method for color analysis of animal source foods, therefore, the color read by the CVS seemed to be more similar to the real ones.

scholarly journals The feasibility of animal source foods’ color measurement using CVS

2021 ◽  
Vol 854 (1) ◽  
pp. 012060
Author(s):  
Bojana Milovanovic
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Color Measurement ◽  
Computer Vision System ◽  
Animal Source Foods ◽  
Animal Source ◽  
Actual Color

Abstract Color assessment of animal source foods was investigated using a computer vision system (CVS) and a traditional colorimeter. With the same measurement conditions, color readings varied between these two approaches. The color measured by CVS was highly similar to the actual color of animal source foods, and ranged from 75.0%-100.0% of actual colors, whereas colors read by a Minolta colorimeter showed non-typical appearances. The CVS-obtained colors were more similar to the color of food visualized on the monitor, compared to colorimeter-generated color chips. Considering these results, it could be concluded that the CVS is a superior alternative for replacing traditional devices by providing better accuracy.

Vision-Based Precision Deburring and Edge Breaking Process Quality Control

2020 ◽  
Author(s):  
Kartik Gupta ◽  
Cindy Grimm ◽  
Burak Sencer ◽  
Ravi Balasubramanian
Keyword(s):  
Computer Vision ◽  
Quality Control ◽  
Vision System ◽  
Quality Measures ◽  
Cad Model ◽  
Computer Vision System ◽  
Part Geometry ◽  
Specialized Hardware ◽  
Original Part

Abstract This paper presents a computer vision system for evaluating the quality of deburring and edge breaking on aluminum and steel blocks. This technique produces both quantitative (size) and qualitative (quality) measures of chamfering operation from images taken with an off-the-shelf camera. We demonstrate that the proposed computer vision system can detect edge chamfering geometry within a 1–2mm range. The proposed technique does not require precise calibration of the camera to the part nor specialized hardware beyond a macro lens. Off-the-shelf components and a CAD model of the original part geometry are used for calibration. We also demonstrate the effectiveness of the proposed technique on edge breaking quality control.

scholarly journals Rancang Bangun Computer Vision System (CVS) Sebagai Instrumen Pengukuran Warna Buah-Buahan

2020 ◽  
Vol 40 (1) ◽  
pp. 21
Author(s):  
Ferlando Jubelito Simanungkalit ◽  
Rosnawyta Simanjuntak
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Vision System ◽  
Agricultural Products ◽  
Color Measurement ◽  
Percentage Error ◽  
Computer Vision System ◽  
Accuracy Rate ◽  
Processing Software ◽  
Image Processing Software

Color had a correlation with physical appearance, nutritional and chemical content as well as sensory properties which determine the quality of agricultural products and foods. Conventional color measurements were performed destructively using laboratory equipment. Therefore, color measurement methods of agricultural products were needed more quickly, accurately and non-destructively. This study aimed to develop a Computer Vision System (CVS) that can be used as a tool to measure the color of fruits. The designed CVS consists of a 60x60x60 cm black mini photo studio; a pair 15 watt LED lighting, sony α6000 digital camera, a set of laptop and an image processing software applications. Image processing software was programmed using VB.Net 2008 programming language. The developed CVS was calibrated using 24 color charts Macbeth Colorchecker (Gretag-Macbeth, USA). The calibration results of 24 color chart of Macbeth Colorchecker was resulted in a MAPE (Mean Absolute Percentage Error) value of component R / Red = 0%; G / Green = 0% and B / Blue = 0,5%; with 99% accuracy rate. In color measurement, the developed CVS had a 95% accuracy rate.

A computer vision system for objective fabric smoothness appearance assessment with an ensemble classifier

2019 ◽  
Vol 90 (3-4) ◽  
pp. 333-343 ◽  
Author(s):  
Jingan Wang ◽  
Kangjun Shi ◽  
Lei Wang ◽  
Ruru Pan ◽  
Weidong Gao
Keyword(s):  
Computer Vision ◽  
Information Gain ◽  
Vision System ◽  
Objective Assessment ◽  
Ensemble Classifier ◽  
Image Feature ◽  
Support Vector ◽  
Computer Vision System ◽  
Processing Technologies ◽  
Feature Sets

Fabric smoothness appearance assessment plays an important role in the textile and apparel industry. To evaluate fabric smoothness objectively, different methods have been proposed based on computer vision technology. To further improve the performance and promote the application of the assessment methods, this paper reports a hybrid computer vision system for objective assessment of fabric smoothness appearance with an ensemble classifier to integrate the advantages of the different image feature sets, which are extracted based on different image processing technologies. The image acquisition environment is established in this system with the selection of illumination parameters—intensity, position angle and altitudinal angle—by a designed strategy. The main steps of the strategy include determination of priority by information gain analysis and parameter selection by classifier performance analysis. The support vector machine classifiers trained by each feature sets are grouped into an ensemble by a self-adapting weighted voting method and the redundant feature sets are eliminated based on the weights of the feature sets. The final result shows evaluation accuracies with 82.86% under 0-degree error, 97.14% under 0.5-degree error and 100% under 1-degree error, which outperforms the other methods in the same environment and verifies the applicability of the proposed system.

scholarly journals A Grading Method for Mangoes on the Basis of Peel Color Measurement Using a Computer Vision System

2016 ◽  
Vol 07 (06) ◽  
pp. 327-334 ◽  
Author(s):  
Yoshio Makino ◽  
Kenjiro Goto ◽  
Seiichi Oshita ◽  
Akari Sato ◽  
Masato Tsukada
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Color Measurement ◽  
Computer Vision System ◽  
Peel Color
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