Name
Abstract | ||
|---|---|---|
In the context of retinal microsurgery, visual tracking of instruments is a key component of robotics assistance. The difficulty of the task and major reason why most existing strategies fail on in-vivo image sequences lies in the fact that complex and severe changes in instrument appearance are challenging to model. This paper introduces a novel approach, that is both data-driven and complementary to existing tracking techniques. In particular, we show how to learn and integrate an accurate detector with a simple gradient-based tracker within a robust pipeline which runs at framerate. In addition, we present a fully annotated dataset of retinal instruments in in-vivo surgeries, which we use to quantitatively validate our approach. We also demonstrate an application of our method in a laparascopy image sequence. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33418-4_70 | MICCAI (2) |
Keywords | Field | DocType |
visual tracking,in-vivo surgery,retinal microsurgery,in-vivo image sequence,data-driven visual tracking,existing tracking technique,accurate detector,existing strategy,retinal instrument,laparascopy image sequence,novel approach | Computer vision,Optical coherence tomography,Data-driven,Gaussian pyramid,Pattern recognition,Computer science,Eye tracking,Artificial intelligence,Retinal,Image sequence,Detector,Robotics | Conference |
Volume | Issue | ISSN |
15 | Pt 2 | 0302-9743 |
Citations | PageRank | References |
23 | 1.29 | 10 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Raphael Sznitman | 1 | 287 | 27.75 |
| Karim Ali | 2 | 190 | 12.96 |
| Rogério Richa | 3 | 238 | 14.89 |
| Russell H. Taylor | 4 | 1970 | 438.00 |
| Gregory D. Hager | 5 | 3871 | 400.32 |
| Pascal Fua | 6 | 12768 | 731.45 |