Towards Automatic Rat's Gait Analysis Under Suboptimal Illumination Conditions

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2019

Autores

Ana F. Adonias (Autor) Outra; Ferreira-Gomes J. (Autor) FMUP; Raquel Alonso (Autor) Outra; Fani Neto (Autor) Outra; Jaime S. Cardoso (Autor) FEUP

Ata de Conferência Internacional

Título: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Páginas: 247-259

9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019

1 July 2019 through 4 July 2019

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00R-F9X

DOI: 10.1007/978-3-030-31321-0_22

Abstract (EN): Rat¿s gait analysis plays an important role in the assessment of the impact of certain drugs on the treatment of osteoarthritis. Since movement-evoked pain is an early characteristic of this degenerative joint disease, the affected animal modifies its behavior to protect the injured joint from load while walking, altering its gait¿s parameters, which can be detected through a video analysis. Because commercially available video-based gait systems still present many limitations, researchers often choose to develop a customized system for the acquisition of the videos and analyze them manually, a laborious and time-consuming task prone to high user variability. Therefore, and bearing in mind the recent advances in machine learning and computer vision fields, as well as their presence in many tracking and recognition applications, this work is driven by the need to find a solution to automate the detection and quantification of the animal¿s gait changes making it an easier, faster, simpler and more robust task. Thus, a comparison between different methodologies to detect and segment the animal under degraded luminance conditions is presented in this paper as well as an algorithm to detect, segment and classify the animal¿s paws. © 2019, Springer Nature Switzerland AG.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13