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—In applications such as social, energy, transportation , sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process such signals on graphs. In this tutorial(More)
In the past few years, the problem of distributed consensus has received a lot of attention, particularly in the framework of ad hoc sensor networks. Most methods proposed in the literature address the consensus averaging problem by distributed linear iterative algorithms, with asymptotic convergence of the consensus solution. The convergence rate of such(More)
—The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing. However, a meaningful graph is not always readily available from the data, nor easy to define depending on the application domain. In(More)
W e address the problem of video quality prediction and control for high-resolution video transmitted over lossy packet networks. In packet video, the bitstream ¯ows through several subsystems (coder, network, decoder); each of them can impair the information, either by data loss or by introducing some delay. However, each of these subsystems can be(More)
—This paper proposes a rate-distortion optimal a posteriori quantization scheme for matching pursuit (MP) coefficients. The a posteriori quantization applies to an MP expansion that has been generated offline and cannot benefit of any feedback loop to the encoder in order to compensate for the quantization noise. The redundancy of the MP dictionary provides(More)
—Relationships between entities in datasets are often of multiple types, which can naturally be modeled by a multi-layer graph; a common vertex set represents the entities and the edges on different layers capture different types of relationships between the entities. In this paper, we address the problem of analyzing multi-layer graphs and propose methods(More)
—Unions of graph Fourier multipliers are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators to the high-dimensional signals collected by sensor networks. The proposed method features approximations of the graph Fourier multipliers by shifted(More)
We propose a method to compute scale invariant features in omni-directional images. We present a formulation based on Riemannian geometry for the definition of differential operators on non-Euclidian manifolds that correspond to the particular form of the mirrors in omnidirectional imaging. These operators lead to a scale-space analysis that preserves the(More)