Adaptive multi-affine (AMA) feature-matching algorithm and its application to minimally-invasive surgery images

Gustavo A. Puerto Souza, Mehrad Adibi, Jeffrey A Cadeddu, Gian Luca Mariottini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

We present our novel Adaptive Multi-Affine (AMA) feature-matching algorithm that finds correspondences between two views of the same non-planar object. The proposed method only uses monocular images to robustly match clusters of 2-D features according to their relative position on the object surface; finally, AMA adaptively finds the best number of clusters that maximizes the number of matching features. We use AMA to recover a feature tracker from failure (e.g., loss of points due to occlusions or deformations), by robustly matching the features in the images before and after such events. This is paramount in Augmented-Reality (AR) systems for Minimally-Invasive Surgery (MIS) to cope for frequent occlusions and organ deformations that can cause the tracked image-points to drastically reduce (or even disappear) in the current video. We validated our approach on a large set of MIS videos of partial-nephrectomy surgery; AMA achieves an increased number of matches, as well as a reduced feature-matching error when compared to state-of-the-art method.

Original languageEnglish (US)
Title of host publicationIROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Subtitle of host publicationCelebrating 50 Years of Robotics
Pages2371-2376
Number of pages6
DOIs
StatePublished - Dec 29 2011
Event2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11 - San Francisco, CA, United States
Duration: Sep 25 2011Sep 30 2011

Publication series

NameIEEE International Conference on Intelligent Robots and Systems

Other

Other2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11
Country/TerritoryUnited States
CitySan Francisco, CA
Period9/25/119/30/11

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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