Federico Lecumberry

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Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is(More)
Sparse data models have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. The learning of sparse models has been mostly concerned with adapting the dictionary to tasks such as classification and reconstruction, optimizing extrinsic properties of the trained(More)
Shape models (SMs), capturing the common features of a set of training shapes, represent a new incoming object based on its projection onto the corresponding model. Given a set of learned SMs representing different objects classes, and an image with a new shape, this work introduces a joint classification-segmentation framework with a twofold goal. First,(More)
Cryo-electron microscopy (cryo-EM) is a powerful technique for 3D structure determination of protein complexes by averaging information from individual molecular images. The resolutions that can be achieved with single-particle cryo-EM are frequently limited by inaccuracies in assigning molecular orientations based solely on 2D projection images.(More)
We developed an EBGM-based algorithm that successfully implements face recognition under constrained conditions. A suitable adaptation of the Gabor filters was found through a power spectral analisys (PSD) of the face images. We outperformed the best-known implementations of the EBGM algorithm in the FERET database. The results are comparable with those of(More)
The “Big Data” era has arisen, driven by the increasing availability of data from multiple sources such as social media, online transactions, network sensors or mobile devices. This is currently a focus of interest among public and private organizations, governments, research institutes and companies operating in diverse fields as health, security,(More)
Current EEG applications imply the need for low-latency, low-power, high-fidelity data transmission and storage algorithms. This work proposes a compression algorithm meeting these requirements through the use of modern information theory and signal processing tools (such as universal coding, universal prediction, and fast online implementations of(More)
Minimal surface regularization has been used in several applications ranging from stereo to image segmentation, sometimes hidden as a graph-cut discrete formulation, or as a strictly convex approximation to TV minimization. In this paper we consider a modified version of minimal surface regularization coupled with a robust data fitting term for(More)
This paper proposes lossless and near-lossless compression algorithms for multichannel biomedical signals. The algorithms are sequential and efficient, which makes them suitable for low-latency and low-power signal transmission applications. We make use of information theory and signal processing tools (such as universal coding, universal prediction, and(More)