Context-based segmentation of the longissimus muscle in beef with a deep neural network

• Autorzy: Talacha Karol , Świderski Bartosz , Kurek Jarosław , Kruk Michał , Półtorak Andrzej , Chmielewski Leszek J. , Wieczorek Grzegorz , Antoniuk Izabella , Pach Jakub , Orłowski Arkadiusz

Identyfikatory

DOI

10.22630/MGV.2019.28.1.5

• Języki publikacji: EN

Abstrakty

EN

The problem of segmenting the cross-section through the longissimus muscle in beef carcasses with computer vision methods was investigated. The available data were 111 images of cross-sections coming from 28 cows (typically four images per cow). Training data were the pixels of the muscles, marked manually. The AlexNet deep convolutional neural network was used as the classifier, and single pixels were the classified objects. Each pixel was presented to the network together with its small circular neighbourhood, and with its context represented by the further neighbourhood, darkened by halving the image intensity. The average classification accuracy was 96%. The accuracy without darkening the context was found to be smaller, with a small but statistically significant difference. The segmentation of the longissimus muscle is the introductory stage for the next steps of assessing the quality of beef for the alimentary purposes.

Słowa kluczowe

EN

beef carcasses; context-based; segmentation; longissimus muscle; classification; deep convolutional network; beef quality

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2019
• Tom: Vol. 28, No. 1/4
• Strony: 47--57
• Opis fizyczny: Bibliogr. 13 poz., il.

Twórcy

autor

Talacha Karol

• Institute of Human Nutrition Sciences, Warsaw University of Life Sciences – SGGW, Poland

autor

Świderski Bartosz

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-7490-8930

autor

Kurek Jarosław

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-2789-4732

autor

Kruk Michał

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-3451-6879

autor

Półtorak Andrzej

• Institute of Human Nutrition Sciences, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-6482-5904

autor

Chmielewski Leszek J.

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-9725-2479

autor

Wieczorek Grzegorz

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-1654-1982

autor

Antoniuk Izabella

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0003-3157-3381+

autor

Pach Jakub

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-3483-4340

autor

Orłowski Arkadiusz

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Poland

• https://orcid.org/0000-0002-6755-1830+

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).