Publications de Roberto Rigamonti

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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 ...

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 ...

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 ...

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 ...

Roberto Rigamonti

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

Are Sparse Representations Really Relevant for Image Classification?

Better features for image categorization and segmentation

Learning Separable Filters

On the Relevance of Sparsity for Image Classification

Supervised Feature Learning for Curvilinear Structure Segmentation

Learning Separable Filters