Détail du document
Identifiant
oai:arXiv.org:2307.11695
Sujet
Computer Science - Machine Learnin...Auteur
Strzałka, Krystian Mazurek, Szymon Wielgosz, Maciej Russek, Paweł Caputa, Jakub Łukasik, Daria Krupiński, Jan Grzeszczyk, Jakub Karwatowski, Michał Frączek, Rafał Jamro, Ernest Pietroń, Marcin Koryciak, Sebastian Dąbrowska-Boruch, Agnieszka Wiatr, KazimierzCatégorie
Computer Science
Année
2023
Date de référencement
26/07/2023
Mots clés
acquisition medicine veterinaryRésumé
This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs.
The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions.
The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits.
Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy.
Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models.
By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine.
Strzałka, Krystian,Mazurek, Szymon,Wielgosz, Maciej,Russek, Paweł,Caputa, Jakub,Łukasik, Daria,Krupiński, Jan,Grzeszczyk, Jakub,Karwatowski, Michał,Frączek, Rafał,Jamro, Ernest,Pietroń, Marcin,Koryciak, Sebastian,Dąbrowska-Boruch, Agnieszka,Wiatr, Kazimierz, 2023, Using simulation to calibrate real data acquisition in veterinary medicine
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