Author: Roberto Tron

Consensus algorithms on Riemannian manifolds

Given a group of agents which move in Euclidean space and communicate according to a given communication graph, standard consensus algorithms provide a protocol which, as time passes, brings all the agents to a common location. The key aspect here is that only local communications are used. These algorithms, however, do not apply when the […]

Distributed optimization on Riemannian manifolds

I worked on distributed optimization problems involving variables lying on non-linear spaces (that is, Riemannian manifolds) using extensions of gradient descent algorithms with fixed step size. I developed novel theoretical tools which significantly broadened the state of the art for determining sufficient conditions for global behaviors (algorithm convergence) using only local information. These tools have […]

Motion segmentation

My initial research included the comparison of different algorithms for segmenting multiple moving objects in a monocular video.  For this purpose, I created the Hopkins 155 dataset, which, since its introduction, has been used in over 150 scholarly articles and is a de-facto standard benchmark in this field. The following is a frame from the […]

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