scholarly journals COMPUTER VISION SYSTEM FOR COLOR MEASUREMENTS OF MEAT AND MEAT PRODUCTS: A REVIEW

2018 ◽  
Vol 3 (4) ◽  
pp. 4-15
Author(s):  
Igor B. Tomasevic
Keyword(s):  
Computer Vision ◽  
Statistical Analysis ◽  
Traditional Method ◽  
Vision System ◽  
Meat Products ◽  
Three Dimensions ◽  
Comparison Study ◽  
Computer Vision System ◽  
Color Measurements ◽  
Instrumental Values

Meat and meat products color evaluation ability of a computer vision system (CVS) is investigated by a comparison study with color measurements from a traditional colorimeter. A statistical analysis revealed significant differences between the instrumental values in all three dimensions (L*, a*, b*) between the CVS and colorimeter. The CVS-generated colors were more similar to the sample of the meat products visualized on the monitor, compared to colorimeter-generated colors in all (100 %) individual trials performed. The use of CVS should be considered a superior alternative to the traditional method for measuring color of meat and meat products.

Evaluation of poultry meat colour using computer vision system and colourimeter

2019 ◽  
Vol 121 (5) ◽  
pp. 1078-1087 ◽  
Author(s):  
Igor Tomasevic ◽  
Vladimir Tomovic ◽  
Predrag Ikonic ◽  
Jose Manuel Lorenzo Rodriguez ◽  
Francisco J. Barba ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Meat Products ◽  
Rank Correlation ◽  
Poultry Meat ◽  
Computer Vision System ◽  
Colour Difference ◽  
Content Type ◽  
Trained Panel ◽  
Sample Test

Purpose The purpose of this paper is to investigate the ability of the computer vision system (CVS) to evaluate the colour of poultry meat. The advantages of the CVS over traditional methods were also explored. Design/methodology/approach The research was carried out on m. pectoralis major samples of three animals for each of the following four species: chicken, turkey, duck and goose. The total colour difference (ΔE) and the degree of difference of hue, chroma and lightness between the methods were calculated. In addition, a trained panel of 14 people was used to carry out three different similarity tests analysed using χ2 one sample test and one-way ANOVA. The correlation coefficient between CVS and colourimeter measures was evaluated using the Spearman rank correlation test. Findings The total colour difference (ΔE) between the methods employed was so large that the generated colour(s) could be considered more opposite than similar. The CVS-generated colour chips were more similar to the sample of the meat products visualised on the monitor compared to colourimeter-generated colour chips in all (100 per cent) individual trials performed. The use of the colourimeter for colour evaluation of lighter coloured poultry meat (chicken and turkey) was unrepresentative. Practical implications In this study, a CVS was developed to measure the colour of poultry meat as an alternative to conventional colourimeters. Originality/value The research has demonstrated that the use of a CVS should be considered a superior alternative to the traditional method for measuring colour of chicken, turkey, duck and goose meat.

scholarly journals Pros and cons of using a computer vision system for color evaluation of meat and meat products

2019 ◽  
Vol 333 ◽  
pp. 012008
Author(s):  
B Milovanovic ◽  
I Djekic ◽  
V Djordjevic ◽  
V Tomovic ◽  
F Barba ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Meat Products ◽  
Computer Vision System ◽  
Pros And Cons

scholarly journals Noncontact Heart and Respiratory Rate Monitoring of Preterm Infants Based on a Computer Vision System: Protocol for a Method Comparison Study

10.2196/13400 ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. e13400 ◽  
Author(s):  
Kim Gibson ◽  
Ali Al-Naji ◽  
Julie-Anne Fleet ◽  
Mary Steen ◽  
Javaan Chahl ◽  
...  
Keyword(s):  
Computer Vision ◽  
Respiratory Rate ◽  
Preterm Infants ◽  
Vision System ◽  
Method Comparison ◽  
Neonatal Unit ◽  
Physiological Data ◽  
Comparison Study ◽  
Computer Vision System ◽  
Method Comparison Study

Background Biomedical research in the application of noncontact methods to measure heart rate (HR) and respiratory rate (RR) in the neonatal population has produced mixed results. This paper describes and discusses a protocol for conducting a method comparison study, which aims to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead electrocardiogram (ECG) in preterm infants in the neonatal unit. Objective The aim of this preliminary study is to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead ECG in preterm infants in the neonatal unit. Methods A single-center cross-sectional study was planned to be conducted in the neonatal unit at Flinders Medical Centre, South Australia, in May 2018. A total of 10 neonates and their ECG monitors will be filmed concurrently for 10 min using digital cameras. Advanced image processing techniques are to be applied later to determine their physiological data at 3 intervals. These data will then be compared with the ECG readings at the same points in time. Results Study enrolment began in May 2018. Results of this study were published in July 2019. Conclusions The study will analyze the data obtained by the noncontact system in comparison to data obtained by ECG, identify factors that may influence data extraction and accuracy when filming infants, and provide recommendations for how this noncontact system may be implemented into clinical applications. International Registered Report Identifier (IRRID) RR1-10.2196/13400

scholarly journals Noncontact Heart and Respiratory Rate Monitoring of Preterm Infants Based on a Computer Vision System: Protocol for a Method Comparison Study (Preprint)

2019 ◽  
Author(s):  
Kim Gibson ◽  
Ali Al-Naji ◽  
Julie-Anne Fleet ◽  
Mary Steen ◽  
Javaan Chahl ◽  
...  
Keyword(s):  
Computer Vision ◽  
Respiratory Rate ◽  
Preterm Infants ◽  
Vision System ◽  
Method Comparison ◽  
Neonatal Unit ◽  
Physiological Data ◽  
Comparison Study ◽  
Computer Vision System ◽  
Method Comparison Study

BACKGROUND Biomedical research in the application of noncontact methods to measure heart rate (HR) and respiratory rate (RR) in the neonatal population has produced mixed results. This paper describes and discusses a protocol for conducting a method comparison study, which aims to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead electrocardiogram (ECG) in preterm infants in the neonatal unit. OBJECTIVE The aim of this preliminary study is to determine the accuracy of a proposed noncontact computer vision system to detect HR and RR relative to the HR and RR obtained by 3-lead ECG in preterm infants in the neonatal unit. METHODS A single-center cross-sectional study was planned to be conducted in the neonatal unit at Flinders Medical Centre, South Australia, in May 2018. A total of 10 neonates and their ECG monitors will be filmed concurrently for 10 min using digital cameras. Advanced image processing techniques are to be applied later to determine their physiological data at 3 intervals. These data will then be compared with the ECG readings at the same points in time. RESULTS Study enrolment began in May 2018. Results of this study were published in July 2019. CONCLUSIONS The study will analyze the data obtained by the noncontact system in comparison to data obtained by ECG, identify factors that may influence data extraction and accuracy when filming infants, and provide recommendations for how this noncontact system may be implemented into clinical applications. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR1-10.2196/13400

scholarly journals Comparison of a computer vision system vs. traditional colorimeter for color evaluation of meat products with various physical properties

Meat Science ◽  
2019 ◽  
Vol 148 ◽  
pp. 5-12 ◽  
Author(s):  
Igor Tomasevic ◽  
Vladimir Tomovic ◽  
Bojana Milovanovic ◽  
Jose Lorenzo ◽  
Vesna Đorđević ◽  
...  
Keyword(s):  
Computer Vision ◽  
Physical Properties ◽  
Vision System ◽  
Meat Products ◽  
Computer Vision System

Colour assessment of milk and milk products using computer vision system and colorimeter

2021 ◽  
pp. 105084
Author(s):  
Bojana Milovanovic ◽  
Ilija Djekic ◽  
Jelena Miocinovic ◽  
Bartosz G. Solowiej ◽  
Jose M. Lorenzo ◽  
...  
Keyword(s):  
Computer Vision ◽  
Vision System ◽  
Computer Vision System ◽  
Milk Products

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

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