Content-Based Video Indexing and Retrieval
IEEE MultiMedia
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Affine-invariant anisotropic detector for soft tissue tracking in minimally invasive surgery
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Eye-gaze driven surgical workflow segmentation
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Recovery of surgical workflow without explicit models
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
The MPEG-7 visual standard for content description-an overview
IEEE Transactions on Circuits and Systems for Video Technology
A novel video key-frame-extraction algorithm based on perceived motion energy model
IEEE Transactions on Circuits and Systems for Video Technology
Tissue deformation recovery with gaussian mixture model based structure from motion
AE-CAI'11 Proceedings of the 6th international conference on Augmented Environments for Computer-Assisted Interventions
A polynomial model of surgical gestures for real-time retrieval of surgery videos
MCBR-CDS'12 Proceedings of the Third MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
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Succinct content-based representation of minimally invasive surgery (MIS) video is important for efficient surgical workflow analysis and modeling of instrument-tissue interaction. Current approaches to video representation are not well suited to MIS as they do not fully capture the underlying tissue deformation nor provide reliable feature tracking. The aim of this paper is to propose a novel framework for content-based surgical scene representation, which simultaneously identifies key surgical episodes and encodes motion of tracked salient features. The proposed method does not require pre-segmentation of the scene and can be easily combined with 3D scene reconstruction techniques to provide further scene representation without the need of going back to the raw data.