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An online learning approach to in-vivo tracking using synergistic features

Austin David Reiter; Peter K. Allen

Title:
An online learning approach to in-vivo tracking using synergistic features
Author(s):
Reiter, Austin David
Allen, Peter K.
Date:
Type:
Articles
Department:
Computer Science
Permanent URL:
Book/Journal Title:
The IEEE/RSJ International Conference on Intelligent Robots and Systems: IROS 2010: Taipei International Convention Center, Taipei, Taiwan, October 18-22, 2010: conference proceedings
Publisher:
IEEE
Publisher Location:
Piscataway, N.J.
Abstract:
In this paper we present an online algorithm for robustly tracking surgical tools in dynamic environments that can assist a surgeon during in-vivo robotic surgery procedures. The next generation of in-vivo robotic surgical devices includes integrated imaging and effector platforms that need to be controlled through real-time visual feedback. Our tracking algorithm learns the appearance of the tool online to account for appearance and perspective changes. In addition, the tracker uses multiple features working together to model the object and discover new areas of the tool as it moves quickly, exits and re-enters the scene, or becomes occluded and requires recovery. The algorithm can persist through changes in lighting and pose by using a memory database, which is built online, using a series of features working together to exploit different aspects of the object being tracked. We present results using real in-vivo imaging data from a human partial nephrectomy.
Subject(s):
Robotics
Publisher DOI:
http://dx.doi.org/10.1109/IROS.2010.5650852
Item views:
137
Metadata:
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