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Escaping specularity: recovering specular-free video sequences from rank-constrained data.

, , , and . SIGGRAPH Posters, page 78:1-78:2. ACM, (2017)

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Robust Cardiac Motion Estimation using Ultrafast Ultrasound Data: A Low-Rank-Topology-Preserving Approach., , , , and . CoRR, (2016)Sensorless force estimation using a neuro-vision-based approach for robotic-assisted surgery., , , and . NER, page 86-89. IEEE, (2015)Towards estimating cardiac motion using low-rank representation and topology preservation for ultrafast ultrasound data., , , and . EMBC, page 1082-1086. IEEE, (2016)Adaptive segmentation and mask-specific Sobolev inpainting of specular highlights for endoscopic images., , , , and . EMBC, page 1196-1199. IEEE, (2016)Exploring the effects of dimensionality reduction in deep networks for force estimation in robotic-assisted surgery., , , and . Medical Imaging: Image-Guided Procedures, volume 9786 of SPIE Proceedings, page 97861X. SPIE, (2016)Escaping specularity: recovering specular-free video sequences from rank-constrained data., , , and . SIGGRAPH Posters, page 78:1-78:2. ACM, (2017)Towards Retrieving Force Feedback in Robotic-Assisted Surgery: A Supervised Neuro-Recurrent-Vision Approach., , , and . IEEE Trans. Haptics, 10 (3): 431-443 (2017)Unconstrained ℓ1 - regularized minimization with interpolated transformations for heart motion compensation., , and . EMBC, page 5109-5112. IEEE, (2014)Automatic and robust single-camera specular highlight removal in cardiac images., , , , and . EMBC, page 675-678. IEEE, (2015)On Genetic Algorithms Optimization for Heart Motion Compensation., and . ROBOT (1), volume 252 of Advances in Intelligent Systems and Computing, page 237-244. Springer, (2013)