Umberto Castellani — accepted AI-conference papers

2021

Data-Driven Intra-Operative Estimation of Anatomical Attachments for Autonomous Tissue Dissection

Eleonora Tagliabue, Diego Dall'Alba, Micha Pfeiffer, Marco Piccinelli, Riccardo Marin, Umberto Castellani +2

RA-L 2021

The execution of surgical tasks by an Autonomous Robotic System (ARS) requires an up-to-date model of the current surgical environment, which has to be deduced from measurements collected during task execution. In this work, we propose to automate tissue dissection tasks by introducing a convolution

Cited by 18 Source ↗Scholar ↗

2017

Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach

Giorgio Roffo, Simone Melzi, Umberto Castellani, Alessandro Vinciarelli

ICCV 2017poster

Feature selection is playing an increasingly significant role with respect to many computer vision applications spanning from object recognition to visual object tracking. However, most of the recent solutions in feature selection are not robust across different and heterogeneous set of data. In thi…

Cited by 317 PDF ↗Scholar ↗

2017

Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering

Matteo Denitto, Simone Melzi, Manuele Bicego, Umberto Castellani, Alessandro Farinelli, Mario A. T. Figueiredo +2

ICCV 2017poster

Region-based correspondence (RBC) is a highly relevant and non-trivial computer vision problem. Given two 3D shapes, RBC seeks segments/regions on these shapes that can be reliably put in correspondence. The problem thus consists both in finding the regions and determining the correspondences betwee…

Cited by 10 PDF ↗Scholar ↗