TY - GEN
T1 - Adaptive multi-affine (AMA) feature-matching algorithm and its application to minimally-invasive surgery images
AU - Puerto Souza, Gustavo A.
AU - Adibi, Mehrad
AU - Cadeddu, Jeffrey A
AU - Mariottini, Gian Luca
PY - 2011/12/29
Y1 - 2011/12/29
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84455160879&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84455160879&partnerID=8YFLogxK
U2 - 10.1109/IROS.2011.6048752
DO - 10.1109/IROS.2011.6048752
M3 - Conference contribution
AN - SCOPUS:84455160879
SN - 9781612844541
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 2371
EP - 2376
BT - IROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
T2 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11
Y2 - 25 September 2011 through 30 September 2011
ER -