Abstract / Description of output
This paper addresses the problem of reliably fitting an orientated model to
video data of laboratory mice assays by specifically locating semi-invariant
points on an extracted outline. In the case of mice, the rapid changes in
direction and shape often lead to failure when using explicit models. Here
we employ a standard background subtraction algorithm in order to derive
contour information from a well defined top-down view of the assay. Using
this contour, we compare three different approaches at locating head, tail-tip
and tail-base features that allow us to constrain orientation. We validate each
approach against an annotated gold-standard data-set, and conclude that a
composite method delivers the best results. This ultimately has benefits for
analysing higher-level behaviour where it is crucial to retain orientation.
video data of laboratory mice assays by specifically locating semi-invariant
points on an extracted outline. In the case of mice, the rapid changes in
direction and shape often lead to failure when using explicit models. Here
we employ a standard background subtraction algorithm in order to derive
contour information from a well defined top-down view of the assay. Using
this contour, we compare three different approaches at locating head, tail-tip
and tail-base features that allow us to constrain orientation. We validate each
approach against an annotated gold-standard data-set, and conclude that a
composite method delivers the best results. This ultimately has benefits for
analysing higher-level behaviour where it is crucial to retain orientation.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the British Machine Vision Conference |
| Publisher | BMVA Press |
| Pages | 84.1-84.10 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - 2008 |