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Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery.

, , , and . EMBC, page 1-4. IEEE, (2015)

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Sight to touch: 3D diffeomorphic deformation recovery with mixture components for perceiving forces in robotic-assisted surgery., , and . IROS, page 160-165. IEEE, (2017)A Deep-Neuro-Fuzzy approach for estimating the interaction forces in Robotic surgery., , , , and . FUZZ-IEEE, page 1113-1119. IEEE, (2016)Force-feedback sensory substitution using supervised recurrent learning for robotic-assisted surgery., , , and . EMBC, page 1-4. IEEE, (2015)V-ANFIS for Dealing with Visual Uncertainty for Force Estimation in Robotic Surgery., , , and . IFSA-EUSFLAT, Atlantis Press, (2015)Peekaboo-Where are the Objects? Structure Adjusting Superpixels., , , , and . ICIP, page 3693-3697. IEEE, (2018)Adaptive segmentation and mask-specific Sobolev inpainting of specular highlights for endoscopic images., , , , and . EMBC, page 1196-1199. IEEE, (2016)Sensorless force estimation using a neuro-vision-based approach for robotic-assisted surgery., , , and . NER, page 86-89. IEEE, (2015)Sensory Substitution for Force Feedback Recovery: A Perception Experimental Study., , , , , , and . ACM Trans. Appl. Percept., 15 (3): 16:1-16:19 (2018)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)