International Journal of Computer Vision
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
International Journal of Computer Vision
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Data-Derived Models for Segmentation with Application to Surgical Assessment and Training
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Task versus Subtask Surgical Skill Evaluation of Robotic Minimally Invasive Surgery
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Structure in surgical motion
Modeling and segmentation of surgical workflow from laparoscopic video
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
An application-dependent framework for the recognition of high-level surgical tasks in the OR
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
HMM assessment of quality of movement trajectory in laparoscopic surgery
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IEEE Transactions on Signal Processing
Sparse hidden markov models for surgical gesture classification and skill evaluation
IPCAI'12 Proceedings of the Third international conference on Information Processing in Computer-Assisted Interventions
Pattern-based real-time feedback for a temporal bone simulator
Proceedings of the 19th ACM Symposium on Virtual Reality Software and Technology
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Much of the existing work on automatic classification of gestures and skill in robotic surgery is based on kinematic and dynamic cues, such as time to completion, speed, forces, torque, or robot trajectories. In this paper we show that in a typical surgical training setup, video data can be equally discriminative. To that end, we propose and evaluate three approaches to surgical gesture classification from video. In the first one, we model each video clip from each surgical gesture as the output of a linear dynamical system (LDS) and use metrics in the space of LDSs to classify new video clips. In the second one, we use spatio-temporal features extracted from each video clip to learn a dictionary of spatio-temporal words and use a bag-of-features (BoF) approach to classify new video clips. In the third approach, we use multiple kernel learning to combine the LDS and BoF approaches. Our experiments show that methods based on video data perform equally well as the state-of-the-art approaches based on kinematic data.