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Development of a computer vision system to estimate the colour indices of Kinnow mandarins

JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE 2019年第4期56卷 2305-2311页

作者： Hadimani, Lingaraj Mittal, Neerja Acad Sci & Innovat Res AcSIR Ghaziabad 201002 India CSIR Cent Sci Instruments Org Dept Computat Instrumentat Chandigarh 160030 India KLE Dr MS Sheshgiri Coll Engn & Technol Belagavi 590008 India

Maturity of a citrus fruit is generally expressed by a numerical value called citrus colour index (CCI). The success of methods employed in estimating the maturity depends on the cultivar and climatic conditions of growing regions. In this work, an image processing based method using CIELAB color model has been developed to estimate the CCI of Kinnow mandarin fruits. A polynomial transformation based camera characterization method was employed to reduce the number of transformations required for RGB to Lab colour space transformation, which resulted into a colour difference of 2.191 with CIELAB E 2000 colour difference formula. In order to analyse the performance of this method, linear regression and partial least square (PLS) models were built on a dataset of 271 Kinnow fruit images wherein spectrophotometer was used for the validation of computed CCI values. The proposed method achieved a high adjusted R2 value of 0.9660 using PLS regression, which ascertain the feasibility of image processing based system in estimating the maturity of Kinnow fruits. Additionally, a correlation analysis between colour coordinates and physicochemical properties was conducted to analyze the relation between the fruit's external peel colour and its internal characteristics.

关键词： computer vision system Citrus colour index Colour characterization Colour difference Physicochemical parameters Kinnow mandarins

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Comparison of computer vision system and colour spectrophotometer for colour measurement of mozzarella cheese

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APPLIED FOOD RESEARCH 2021年第2期1卷

作者： Minz, Prashant Saurabh Saini, Charanjiv Singh ICAR Natl Dairy Res Inst Dairy Engn Div Karnal 132001 Haryana India St Longowal Inst Engn & Technol Dept Food Engn & Technol Longowal 148106 Punjab India

The colour of food is one of the most important selection criteria affecting purchasing decision of a consumer. Conventionally, colour is measured using colorimeters and colour spectrophotometer. In recent times, computer vision technology has emerged as a robust technique and can be implemented as an alternative to conventional colour measurement methods. A cost-effective computer vision system was developed for measuring the colour of mozzarella cheese with provision to mount different cameras. Various CCD and CMOS based digital cameras were calibrated and evaluated for image acquisition of cheese samples. An additional validation step was introduced to characterize different image acquisition devices on the basis of Root Mean Square Deviation (RMSD) and Mean Normalized Error (MNE). The colour measurement algorithm extracts RGB values from the sample image and coverts into CIE L*a*b * units. The conversion between two colour spaces was done employing standard equations. The colour of mozzarella cheese samples was measured in the CIE L*a*b * color space with the computer vision system (CVS) and commercial colour spectrophotometer. Both the systems yielded equivalent results and developed CVS can be used to study colorimetric quality parameters of mozzarella cheese.

关键词： computer vision system spectrophotometer colour measurement mozzarella cheese

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The algorithm development for operation of a computer vision system via the OpenCV library

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Procedia computer Science 2020年 169卷 662-667页

作者： Stepan Sivkov Leonid Novikov Galina Romanova Anastasia Romanova Denis Vaganov Marat Valitov Sergey Vasiliev Technological Institute National Research Nuclear University MEPhI Lesnoy Sverdlovsk Region the Russian Federation

The article is devoted to the effective way of getting images from the information board of the counting register for utility metering. This method is based on the use of the open source library OpenCV. The image processing algorithm has been represented. The method of calibration of the video camera allows us to reduceerror probability in text recognition. Here are some results of the experiment.

关键词： computer vision computer vision system calibration of video camera counting registers for utility metering

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A computer vision system Supporting Blind People - The Supermarket Case 12th

A Computer Vision System Supporting Blind People - The Super...

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12th International Conference on computer vision systems (ICVS)

作者： Georgiadis, Kostas Kalaganis, Fotis Migkotzidis, Panagiotis Chatzilari, Elisavet Nikolopoulos, Spiros Kompatsiaris, Ioannis Ctr Res & Technol Hellas Inst Informat Technol Thermi 57001 Greece Aristotle Univ Thessaloniki Artificial Intelligence & Informat Anal Lab Dept Informat Serres Greece

ISBN: (纸本)9783030349950;9783030349943

The proposed application builds on the latest advancements of computer vision with the aim to improve the autonomy of people with visual impairment at both practical and emotional level. More specifically, it is an assistive system that relies on visual information to recognise the objects and faces surrounding the user. The system is supported by a set of sensors for capturing the visual information and for transmitting the auditory messages to the users. In this paper, we present a computer vision application, e-vision, in the context of visiting the supermarket for buying groceries.

关键词： computer vision system Assistive device Visually impaired

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computer vision system: A Tool for Evaluating the Quality of Wheat in a Grain Tank 10

Computer Vision System: A Tool for Evaluating the Quality of...

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10th International Conference on Machine vision (ICMV)

作者： Minkin, Uryi Igorevish Panchenko, Aleksei Vladimirovich Shkanaev, Aleksandr Yurievich Konovalenko, Ivan Andreevich Putintsev, Dmitry Nikolaevich Sadekov, Rinat Nailevish JSC Cognit Moscow Russia RAS CSC FRC Inst Syst Anal Moscow Russia Inst Engn Phys MEI Serphukhov Russia

ISBN: (数字)9781510619425

ISBN: (纸本)9781510619425

The paper describes a technology that allows for automatizing the process of evaluating the grain quality in a grain tank of a combine harvester. Special recognition algorithm analyzes photographic images taken by the camera, and that provides automatic estimates of the total mass fraction of broken grains and the presence of non-grains. The paper also presents the operating details of the tank prototype as well as it defines the accuracy of the algorithms designed.

关键词： Precision agriculture broken grain non-grain computer vision system pattern recognition statistical learning

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Pulsed electric fields processing of apple tissue: Spatial distribution of electroporation by means of magnetic resonance imaging and computer vision system

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INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES 2018年 47卷 120-126页

作者： Dellarosa, Nicolo Laghi, Luca Ragni, Luigi Dalla Rosa, Marco Galante, Angelo Ranieri, Brigida Florio, Tiziana Marilena Alecci, Marcello Univ Bologna Dept Agr & Food Sci Cesena Italy Univ Bologna Interdept Ctr Agrifood Ind Res Cesena Italy Univ Aquila Dept Life Hlth & Environm Sci Laquila Italy Natl Inst Nucl Phys Gran Sasso Natl Lab INFN LNGS Assergi Italy Inst SPIN CNR Dept Phys & Chem Sci Laquila Italy

The optimal application of Pulsed Electric Fields (PEF) technology, used by food industry to assist mass transfer processes, depends on the effectiveness of the induced electroporation. The present work aimed at exploring the application of Magnetic Resonance Imaging (MRI) combined with computer vision system (CVS) analysis to assess the spatial distribution of electroporation in apple tissue. PEF-treated apple samples were compared with Dipping (Dip) and Vacuum Impregnation (VI) to gain insight into the spatial distribution of mechanisms that lead to microstructural modifications over time. CVS showed that electroporation modified heterogeneously apple microstructure, causing enzymatic browning unevenly across the samples. MRI transverse relaxation times (T-2) maps and longitudinal relaxation times (T-1)-weighted images throughout apple tissue confirmed the inhomogeneous distribution and extent of the cell disruption, along with the release of intracellular content toward the external solution. Industrial relevance: The novel applications of pulsed electric fields require fast and reliable methods to detect and estimate the breakage of the membranes integrity in order to boost their industrial adoption and optimization. The present study provided analytical tools able to monitor the spatial distribution of electroporation in plant tissue samples within minutes and consequently to speed up and improve the assessment of different PEF treatments.

关键词： PEF computer vision system Vacuum impregnation Electroporation distribution Magnetic resonance imaging Relaxation times

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Predicting pork loin intramuscular fat using computer vision system

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MEAT SCIENCE 2018年 143卷 18-23页

作者： Liu, J-H Sun, X. Young, J. M. Bachmeier, L. A. Newman, D. J. North Dakota State Univ Dept Anim Sci Fargo ND 58102 USA Univ Arkansas Dept Anim Sci Fayetteville AR 72701 USA Arkansas State Univ Coll Agr Engn & Technol Jonesboro AR 72467 USA

The objective of this study was to investigate the ability of computer vision system to predict pork intramuscular fat percentage (IMF%). Center-cut loin samples (n = 85) were trimmed of subcutaneous fat and connective tissue. Images were acquired and pixels were segregated to estimate image IMF% and 18 image color features for each image. Subjective IMF% was determined by a trained grader. Ether extract IMF% was calculated using ether extract method. Image color features and image IMF% were used as predictors for stepwise regression and support vector machine models. Results showed that subjective IMF% had a correlation of 0.81 with ether extract IMF% while the image IMF% had a 0.66 correlation with ether extract IMF%. Accuracy rates for regression models were 0.63 for stepwise and 0.75 for support vector machine. Although subjective IMF% has shown to have better prediction, results from computer vision system demonstrates the potential of being used as a tool in predicting pork IMF% in the future.

关键词： computer vision system Intramuscular fat Stepwise regression Support vector machine

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computer vision system (CVS): a powerful non-destructive technique for the assessment of red mullet (Mullus barbatus) freshness

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EUROPEAN FOOD RESEARCH AND TECHNOLOGY 2017年第12期243卷 2225-2233页

作者： Tappi, Silvia Rocculi, Pietro Ciampa, Alessandra Romani, Santina Balestra, Federica Capozzi, Francesco Dalla Rosa, Marco Univ Bologna Dept Agr & Food Sci Alma Mater Studiorum Campus Food SciPiazza Goidanich 60 Cesena FC Italy Univ Bologna Interdept Ctr Agri Food Ind Res Alma Mater Studiorum Campus Food SciVia Ravennate 933 Cesena FC Italy

The evaluation of fish freshness can be performed using chemical, sensory and physical methods. Besides sensory methods, several instrumental techniques have been applied with the objective of replacing sensory assessment. The aim of this study was to set up and test objective physical methods mainly based on computer vision system (CVS) to assess red mullet (Mullus barbatus) freshness evolution during 10 days of storage, at two different storage temperatures (0 and 4 A degrees C). To check the effectiveness of the purposed physical methods, CVS features (loss in the epidermis pigmentation, development of gill mucus and eye concavity index) and firmness have been compared with chemical trimethylamine content and sensory (QIM) attribute scores. As expected, fish degradation was faster at the higher temperature. Instrumental texture evaluation of fish by penetration test enabled to detect distinctive firmness changes due to onset and resolution of rigor mortis, and the successive tenderization phenomenon. Among CVS parameters, the epidermis pigmentation loss, and particularly the eye shape modification (eye concavity index) evidenced a high sensibility for the estimation of fresh red mullet quality loss, as a function of the two different storage conditions, and a good agreement with trimethylamine content and QIM response evolution.

关键词： Mullus barbatus Freshness computer vision system Eye concavity index

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An intelligent integrated control of hybrid hot air-infrared dryer based on fuzzy logic and computer vision system

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computerS AND ELECTRONICS IN AGRICULTURE 2017年 137卷 138-149页

作者： Nadian, Mohammad Hossein Abbaspour-Fard, Mohammad Hossein Martynenko, Alex Golzarian, Mahmood Reza Ferdowsi Univ Mashhad Dept Biosyst Engn Mashhad Iran Inst Res Fundamental Sci IPM Brain Engn Res Ctr POB 19395-5531 Tehran Iran Dalhousie Univ Dept Engn Halifax NS Canada

In this study, an intelligent fuzzy-machine vision control system (FMCS) was developed to control the operating variables throughout a hybrid hot air-infrared drying process. The total discoloration and the shrinkage of thin layer kiwifruit slices were monitored in real time using a computer vision system (CVS). These values along with calculated energy consumption obtained from preliminary experiments, were fed into a genetic algorithm (GA) framework to optimize a fuzzy logic control system. The performance of the fuzzy controller was evaluated for kiwifruit drying using a laboratory-scale hot air-infrared dryer. The results indicated that the hybrid drying could significantly reduce the drying load/time compared with the hot air drying. The FMCS application showed a good balance between energy consumption (0.158 kW h) and product quality (Delta E = 2.32). (C) 2017 Elsevier B.V. All rights reserved.

关键词： Kiwifruit Fuzzy control Genetic algorithm Hybrid hot air-infrared dryer computer vision system

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computer vision system for detecting the loss of rail fastening nuts based on kernel two-dimensional principal component - two-dimensional principal component analysis and a support vector machine

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT 2016年第8期230卷 1842-1850页

作者： Wang, Ling Zhang, Bing Wu, Jinjin Xu, Hong Chen, Xiai Na, Wenbo China Jiliang Univ Coll Mech & Elect Engn Hangzhou 310018 Zhejiang Peoples R China Hangzhou Automobile Adv Technicians Sch Hangzhou Zhejiang Peoples R China

In most railway companies in China, the loss of nuts from rail fasteners is still found by visual inspections performed by maintenance workers;this approach cannot meet the continuing requirements to reduce maintenance costs and improve maintenance efficiency. In this paper, a computer vision system for detecting the loss of nuts from rail fasteners is proposed that is based on kernel two-dimensional principal component - two-dimensional principal component analysis (K2DPCA-2DPCA) and a support vector machine (SVM). The framework of this information-technology-based system is introduced, and the calculation method to find the positions where nuts are missing is also proposed. The K2DPCA-2DPCA feature extraction method and SVM used for feature classification in the computer vision system are described in detail. Finally, the combination of K2DPCA-2DPCA and SVM is compared with other feature extraction and feature classification methods in an experiment on the detection of the loss of nuts from rail fasteners. In this paper, it is found that the algorithm based on K2DPCA-2DPCA and a SVM is a better method for the computer vision system. It can identify the loss of nuts from a rail fastener with a high recognition rate and reach the maximum recognition rate using only a relatively small number of features. Furthermore, for the same number of training samples, the algorithm based on K2DPCA-2DPCA and SVM is also better than other algorithms for use in the computer vision system.

关键词： Safety computer vision system detecting the loss of rail fastening nuts K2DPCA-2DPCA SVM

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