Name
Abstract | ||
|---|---|---|
Motions of organs or extremities are important features for clinical diagnosis. However, tracking and segmentation of complex, quickly changing motion patterns is challenging, certainly in the presence of occlusions. Neither state-of-the-art tracking nor motion segmentation approaches are able to deal with such cases. Thus far, motion capture systems or the like were needed which are complicated to handle and which impact on the movements. We propose a solution based on a single video camera, that is not only far less intrusive, but also a lot cheaper. The limitation of tracking and motion segmentation are overcome by a new approach to integrate prior knowledge in the form of weak labeling intomotion segmentation. Using the example of Cerebral Palsy detection, we segment motion patterns of infants into the different body parts by analyzing body movements. Our experimental results show that our approach outperforms current motion segmentation and tracking approaches. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-11752-2_13 | PATTERN RECOGNITION, GCPR 2014 |
Field | DocType | Volume |
Computer vision,Motion capture,Segmentation,Computer science,Artificial intelligence,Clinical diagnosis,Video camera,Prior probability,Optical flow | Conference | 8753 |
ISSN | Citations | PageRank |
0302-9743 | 8 | 0.61 |
References | Authors | |
20 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hodjat Rahmati | 1 | 10 | 1.03 |
| Ralf Dragon | 2 | 18 | 1.93 |
| Ole Morten Aamo | 3 | 233 | 42.55 |
| Luc Van Gool | 4 | 27566 | 1819.51 |
| L. Adde | 5 | 12 | 1.89 |