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Reference TypeConference Paper (Conference Proceedings)
Last Update2021: (UTC)
Metadata Last Update2021: (UTC)
Citation KeyWojcikMenoHill:2021:SeGrGe
TitleSegmentation and graph generation of muzzle images for cattle identification
Access Date2021, Oct. 19
Number of Files1
Size935 KiB
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Author1 Wojcik, Lucas Matheus Leite
2 Jorge Junior
3 Menotti, David
4 Hill, João
Affiliation1 Federal University of Paraná (UFPR)
2 Federal University of Paraná (UFPR)
3 Federal University of Paraná (UFPR)
4 Institute of Rural Development of Paraná (IDR)
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado (Virtual), Brazil
DateOctober 18th to October 22nd, 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeWork in Progress
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsAnimal biometrics
Computer vision
Pattern recognition
AbstractThe current methods for the organizing the records (i.e., cataloguing) of cattle are known to be archaic and inefficient, and often harmful to the animal. Such methods include the use of metal tags attached to the animal's ears like earrings and of branding irons on their necks. Previous research on new methods of livestock branding based on computer vision techniques utilized a mixture of texture features such as Gabor Filters and Local Binary Pattern as a means of extracting identifying features for each animal. The presented approach proposes a new technique using the muzzle image as an individual identifier as a novel technique, assuming that the muzzle RoI taken as input for the model pipeline is already extracted and cropped. This task is performed in three steps. First, the muzzle image is segmented via a convolutional neural network, resulting in a bitmap from which a graph structure is extracted in the second phase. The final phase consists of matching the resulting graph with the ones previously extracted and stored in the database for an optimal match. The results for the segmentation quality show a fidelity of around seventy percent, while the extracted graph perfectly represents the extracted bitmap. The matching algorithm is currently in progress.
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