Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB Content
Introduction
The Skinned Multi-Infant Linear model (SMIL)
The Moving INfants In RGB-D (MINI-RGBD) data set
Contact
Publications
Introduction
Infant motion analysis enables early detection of neurodevelopmental disorders like cerebral palsy (CP). The quality of spontaneous movements, in particular of the general movements (GMs), at the corrected age of 2-4 months accurately reflects the state of the infant's nervous system. The general movement assessment (GMA) method achieves the highest reliability for the detection of CP at an early age. In order to remove the human variability and the effort of regular training of GMA experts, we aim at automating medical infant motion analysis.
The Skinned Multi-Infant Linear model (SMIL)
Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis
Hesse, N., Pujades, S., Romero, J., Black, M. J., Bodensteiner, C., Arens, M., Hofmann, U. G., Tacke, U., Hadders-Algra, M., Weinberger, R., Müller-Felber, W., Schroeder, A. S.
In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2018
pdf supplementary results video extended paper extended video DOI bib
The SMIL model is available for research purposes.
To download the SMIL model, please click the following button:
By downloading and/or using the model, you agree to the license terms, which can be found here.
The Moving INfants In RGB-D (MINI-RGBD) data set
Computer Vision for Medical Infant Motion Analysis: State of the Art and RGB-D Data Set
Hesse, N., Bodensteiner, C., Arens, M., Hofmann, U. G., Weinberger, R., Schroeder, A. S.
In European Conference on Computer Vision Workshops (ECCVW), September 2018
pdf bib video
By downloading and/or using the data set, you agree to the license terms, which can be found here.
Note: The infants shown in the images above / the video / the paper / the data set were created using the SMIL model with generated textures and shapes and therefore do not depict any existing infants.
To download the data set, please click the following button:
After unzipping the .zip file, you'll find a README.txt that explains the folder and data structures.
Contact
Nikolas Hesse
Christoph Bodensteiner
Publications
2019
Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences
Hesse, N., Pujades, S., Black, M. J., Arens, M., Hofmann, U. G., Schroeder, A. S.
Transactions on Pattern Analysis and Machine Intelligence, 42 (10), Special Issue on RGB-D Vision, pp. 2540-2551, 2019.
pdf video DOI bib
2018
Learning an Infant Body Model from RGB-D Data for Accurate Full Body Motion Analysis
Hesse, N., Pujades, S., Romero, J., Black, M. J., Bodensteiner, C., Arens, M., Hofmann, U. G., Tacke, U., Hadders-Algra, M., Weinberger, R., Müller-Felber, W., Schroeder, A. S.
In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), September 2018
pdf supplementary results video extended video DOI bib
Computer Vision for Medical Infant Motion Analysis: State of the Art and RGB-D Data Set
Hesse, N., Bodensteiner, C., Arens, M., Hofmann, U. G., Weinberger, R., Schroeder, A. S.
In European Conference on Computer Vision Workshops (ECCVW), September 2018
pdf bib video
2017
Body Pose Estimation in Depth Images for Infant Motion Analysis
Hesse, N., Schröder, A. S., Müller-Felber, W., Bodensteiner, C., Arens, M., Hofmann, U. G.
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2017
pdf DOI bib
Markerless Motion Analysis for Early Detection of Infantile Movement Disorders
Hesse, N., Schroeder, A. S., Müller-Felber, W., Bodensteiner, C., Arens, M., Hofmann, U. G.
EMBEC & NBC 2017: Joint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC) 2017, Springer Singapore
pdf DOI bib
Entwicklungsneurologie - vernetzte Medizin und neue Perspektiven
Tacke, U., Weigand-Brunnhölzl, H., Hilgendorff, A., Giese, R. M., Flemmer, A. W., König, H., Warken-Madelung, B., Arens, M., Hesse, N., Schroeder, A. S.
Der Nervenarzt, 2017
DOI bib
2015
Estimating Body Pose of Infants in Depth Images Using Random Ferns
Hesse, N., Stachowiak, G., Breuer, T., Arens, M.
IEEE International Conference on Computer Vision Workshop (ICCVW) 2015
pdf DOI bib