Computer visual inspection of pear quality

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dc.citation.epage31
dc.citation.issue1
dc.citation.journalTitleВимірювальна техніка та метрологія
dc.citation.spage25
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorIlchuk, Mykhailo
dc.contributor.authorStadnyk, Andrii
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2024-03-11T08:26:32Z
dc.date.available2024-03-11T08:26:32Z
dc.date.created2023-02-28
dc.date.issued2023-02-28
dc.description.abstractA brief description of the basic stages of image processing is given to pay attention to the segmentation stage as a possible way to improve efficiency in decision-making. The main characteristics of the presented model are visual signs, such as color, shape, the presence of a stem, and others. Due to the different approaches in image processing, a high level of truthfulness is achieved, which is expressed in the percentage ratio of the accuracy of decision-making and varies in the range from 90 to 96%. Therefore, the results obtained in this work make it possible to automate the process of visual inspection with the prospect of increasing the speed and quality of product sales for the consumer.
dc.format.extent25-31
dc.format.pages7
dc.identifier.citationIlchuk M. Computer visual inspection of pear quality / Mykhailo Ilchuk, Andrii Stadnyk // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 84. — No 1. — P. 25–31.
dc.identifier.citationenIlchuk M. Computer visual inspection of pear quality / Mykhailo Ilchuk, Andrii Stadnyk // Measuring Equipment and Metrology. — Lviv : Lviv Politechnic Publishing House, 2023. — Vol 84. — No 1. — P. 25–31.
dc.identifier.doidoi.org/10.23939/istcmtm2023.01.025
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61418
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofВимірювальна техніка та метрологія, 1 (84), 2023
dc.relation.ispartofMeasuring Equipment and Metrology, 1 (84), 2023
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dc.relation.referencesen[3] G. Kreiman. Biological and Computer Vision 52–53 (2021). Available: https://www.cambridge.org/core/books/biological-and-computervision/BB7E68A69AFE7A322F68F3C4A297F3CF
dc.relation.referencesen[4] R. Szeliski. Computer Vision: Algorithms and Applications, 42–43. 2022. Available: https://szeliski.org/Book/
dc.relation.referencesen[5] J. Watt, R. Borhani, A. Katsaggelos. Machine Learning Refined: Foundations, Algorithms, and Applications, 45–46 [2020]. Available: https://people.engr.tamu.edu/guni/csce421/files/Machine_Learning_Refined.pdf
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dc.relation.referencesen[7] J. Kelleher. Deep Learning 12–13 [2019]. Available: https://direct.mit.edu/books/book/4556/Deep-Learning
dc.relation.referencesen[8] T. Amaratunga. Deep Learning on Windows: Building Deep Learning Computer Vision Systems on Microsoft Windows 88–89 (2021). Available: https://link.springer.com/book/10.1007/978-1-4842-6431-7
dc.relation.referencesen[9] M. Favorskaya, L. Jain. Computer Vision in Control Systems, 55–56, 2015. Available: https://link.springer.com/book/10.1007/978-3-319-10653-3
dc.relation.referencesen[10] K. Mehrotra, Ch. Mohan, H. Huang. Anomaly Detection Principles and Algorithms, 165–166, 2017. Available: https://www.amazon.com/Detection-Principles-Algorithms-Terrorism-Computation/dp/3319675249
dc.relation.referencesen[11] S. Prince. Computer Vision: Models, Learning, and Inference, 222–223, 2022. Available: http://www.computervisionmodels.com
dc.relation.referencesen[12] G. Garrido, P. Joshi. OpenCV 3.x with Python By Example, 33–34 (2018). Available: https://www.packtpub.com/product/opencv-3x-with-python-byexample-second-edition/9781788396905
dc.relation.referencesen[13] N. Madanchi. Model-Based Approach for Energy and Resource Efficient Machining Systems, 111–112, 2022. Available: https://link.springer.com/book/10.1007/978-3-030-87540-4
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dc.relation.referencesen[19] R. Shanmugamani. Deep Learning for Computer Vision. 68–69, 2021. Available: https://www.oreilly.com/library/view/deep-learning-for/9781788295628/edf4fbcccca0-4aaa-b9f4-d7c2292c520d.x.html
dc.relation.referencesen[20] J. Howse, P. Joshi, M. Beyeler. OpenCV: Computer Vision Projects with Python, 122–123, 2020. Available: https://www.oreilly.com/library/view/opencv-computervision/9781787125490/ch16s04.html
dc.relation.urihttps://link
dc.relation.urihttps://www.sciencedirect.com/science/article/abs/pii/S0736584502000169
dc.relation.urihttps://www.cambridge.org/core/books/biological-and-computervision/BB7E68A69AFE7A322F68F3C4A297F3CF
dc.relation.urihttps://szeliski.org/Book/
dc.relation.urihttps://people.engr.tamu.edu/guni/csce421/files/Machine_Learning_Refined.pdf
dc.relation.urihttps://www.oreilly.com/library/view/practical-deep-learning/9781492034858/
dc.relation.urihttps://direct.mit.edu/books/book/4556/Deep-Learning
dc.relation.urihttps://link.springer.com/book/10.1007/978-1-4842-6431-7
dc.relation.urihttps://link.springer.com/book/10.1007/978-3-319-10653-3
dc.relation.urihttps://www.amazon.com/Detection-Principles-Algorithms-Terrorism-Computation/dp/3319675249
dc.relation.urihttp://www.computervisionmodels.com
dc.relation.urihttps://www.packtpub.com/product/opencv-3x-with-python-byexample-second-edition/9781788396905
dc.relation.urihttps://link.springer.com/book/10.1007/978-3-030-87540-4
dc.relation.urihttps://www.udemy.com/course/self-supervised-learning/
dc.relation.urihttps://link.springer.com/book/10.1007/978-94-017-0295-9
dc.relation.urihttps://medium.com/voxel51/tunnel-vision-in-computervision-can-chatgpt-see-e6ef037c535
dc.relation.urihttps://bayanbox.ir/view/5130918188419813120/Adrian-Rosebrock-Deep-Learning-for.pdf
dc.relation.urihttps://www.amazon.com/dp/0655324380?tag=uuid10-20
dc.relation.urihttps://www.oreilly.com/library/view/deep-learning-for/9781788295628/edf4fbcccca0-4aaa-b9f4-d7c2292c520d.x.html
dc.relation.urihttps://www.oreilly.com/library/view/opencv-computervision/9781787125490/ch16s04.html
dc.rights.holder© Національний університет “Львівська політехніка”, 2023
dc.subjectQuality control
dc.subjectComputer vision
dc.subjectAutomation
dc.titleComputer visual inspection of pear quality
dc.typeArticle

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Вимірювальна техніка та метрологія. – 2023. – Випуск 84, №1