Òscar Divorra Escoda

Learn More
In this paper, we introduce a framework that merges classical ideas borrowed from scale-space and multiresolution segmentation with nonlinear partial differential equations. A nonlinear scale-space stack is constructed by means of an appropriate diffusion equation. This stack is analyzed and a tree of coherent segments is constructed based on relationships(More)
Recent results have underlined the importance of incoherence in redundant dictionaries for a good behavior of decomposition algorithms like matching and basis pursuit. However, appropriate dictionaries for a given application may not be able to meet the incoherence condition. In such a case, decomposition algorithms may completely fail in the retrieval of(More)
Frame partitioning is a process of key importance in efficient video coding. Most recent video compression technologies, like H.264/AVC, use tree based frame partition. This reveals to be more efficient than simple uniform block partition, typically used in older video coding standards like MPEG-2 or H.263. However, tree based frame partition still does not(More)
Modern video coding standards such as H.264/AVC use intra prediction for efficient coding of Intra pictures. These usually exploit local directional signal correlations. More recently, intra prediction modes using non-local signal information have been introduced. A very popular approach is the so called template matching prediction (TMP), which uses(More)
This paper presents a novel method to correlate audio and visual data generated by the same physical phenomenon, based on sparse geometric representation of video sequences. The video signal is modeled as a sum of geometric primitives evolving through time, that jointly describe the geometric and motion content of the scene. The displacement through time of(More)
Many modern video coding strategies, such as the H.264/AVC standard, use quadtree-based partition structures for coding intra macroblocks. Such a structure allows the coding algorithm to adapt to the complicated and non-stationary nature of natural images. Despite the adaptation flexibility of quadtree partitions, recent studies have shown that these are(More)
In this work we explore the potentialities of a framework for the representation of audio-visual signals using decompositions on overcomplete dictionaries. Redundant decompositions may describe audio-visual sequences in a concise fashion, preserving good representation properties thanks to the use of redundant, well designed, dictionaries. We expect that(More)
3D technologies are becoming the more and more relevant in recent years. Visual communications, as well as image and video analysis, benefit in great manner from spatial information such as depth for various applications. Highly accurate visual depth estimation often involves complex optimization algorithms in order to fit proper estimation models to data.(More)
— In this work, we explore a framework for the sparse representation of video sequences by means of spatio-temporal functions able to exploit the 2D nature of images and the temporal smoothness associated to object trajectories. Decomposition over redundant dictionaries formed by 2D functions capable to exploit image geometry, has shown to be well adapted(More)
This paper explores a novel approach for ventricular and atrial activities estimation in electrocardiogram (ECG) signals, based on sparse source separation. Sparse decompositions of ECG over signal-adapted multi-component dictionaries can lead to natural separation of its components. In this work, dictionaries of functions adapted to ventricular and atrial(More)