A semi-automatic toolbox for markerless effective semantic feature extraction

Vito Paolo Pastore; Matteo Moro; Francesca Odone

doi:10.1038/s41598-022-16014-8

A semi-automatic toolbox for markerless effective semantic feature extraction

Sci Rep. 2022 Jul 13;12(1):11899. doi: 10.1038/s41598-022-16014-8.

Authors

Vito Paolo Pastore^{1

2}, Matteo Moro³, Francesca Odone³

Affiliations

¹ Italian Institute of Technology (IIT), Genova, Italy. Vito.Paolo.Pastore@unige.it.
² Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova and with the Machine Learning Genoa (MaLGa) Center, Genova, Italy. Vito.Paolo.Pastore@unige.it.
³ Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova and with the Machine Learning Genoa (MaLGa) Center, Genova, Italy.

Abstract

VisionTool is an open-source python toolbox for semantic features extraction, capable to provide accurate features detectors for different applications, including motion analysis, markerless pose estimation, face recognition and biological cell tracking. VisionTool leverages transfer-learning with a large variety of deep neural networks allowing high-accuracy features detection with few training data. The toolbox offers a friendly graphical user interface, efficiently guiding the user through the entire process of features extraction. To facilitate broad usage and scientific community contribution, the code and a user guide are available at https://github.com/Malga-Vision/VisionTool.git .

MeSH terms

Cell Tracking
Semantics*
Software*