Publications by Roberto Rigamonti

Learning Separable Filters

Pascal Fua, Vincent Lepetit, Roberto Rigamonti, Bugra Tekin, Amos Sironi

Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, mak ...

Ieee Computer Soc2015

On the Relevance of Sparsity for Image Classification

Pascal Fua, Vincent Lepetit, Engin Türetken, Germán González Serrano, Roberto Rigamonti, Matthew Brown, Fethallah Benmansour

In this paper we empirically analyze the importance of sparsifying representations for classification purposes. We focus on those obtained by convolving images with linear filters, which can be either hand designed or learned, and perform extensive experim ...

Elsevier2014

Better features for image categorization and segmentation

Roberto Rigamonti

EPFL2014

Learning Separable Filters

Pascal Fua, Vincent Lepetit, Roberto Rigamonti, Amos Sironi

2013

Supervised Feature Learning for Curvilinear Structure Segmentation

Pascal Fua, Vincent Lepetit, Carlos Joaquin Becker, Roberto Rigamonti

Springer2013

Accurate and Efficient Linear Structure Segmentation by Leveraging Ad Hoc Features with Learned Filters

Vincent Lepetit, Roberto Rigamonti

Extracting linear structures, such as blood vessels or dendrites, from images is crucial in many medical imagery applications, and many handcrafted features have been proposed to solve this problem. However, such features rely on assumptions that are never ...

Springer2012

Are Sparse Representations Really Relevant for Image Classification?

Vincent Lepetit, Roberto Rigamonti, Matthew Brown

Recent years have seen an increasing interest in sparse representations for image classiﬁcation and object recognition, probably motivated by evidence from the analysis of the primate visual cortex. It is still unclear, however, whether or not sparsity hel ...

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