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Abstract: Local Binary Descriptors are becoming more and more popular for image matching tasks, especially when going mobile. While they are extensively studied in this context, their ability to carry enough information in order to infer the original image is seldom addressed.

Abstract: Ductile fracture by nucleation, growth and coalescence of internal voids is the dominant fracture mechanism in metals at ambient temperature. Micromechanics-based models for each elementary mechanism have been developed and enhanced over the past 40 years, allowing microstructure-informed failure predictions essentially assuming homogeneous damage evolution.

Abstract: The Compressive Sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by reducing the sampling rate required to acquire and stably recover sparse signals. Practical ADCs not only sample but also quantize each measurement to a finite number of bits; moreover, there is an inverse relationship between the achievable sampling rate and the bit-depth.

Abstract: This paper studies the problem of reconstructing sparse or compressible signals from compressed sensing measurements that have undergone nonuniform quantization. Previous approaches to this Quantized Compressed Sensing (QCS) problem based on Gaussian models (bounded l2-norm) for the quantization distortion yield results that, while often acceptable, may not be fully consistent: re-measurement and quantization of the reconstructed signal do not necessarily match the initial observations.

Abstract: Physically Unclonable Functions (PUFs) are becoming popular tools for various applications such as anti-counterfeiting schemes. The security of a PUF-based system relies on the properties of its underlying PUF. Usually, evaluating PUF properties is not simple as it involves assessing a physical phenomenon.

More information: This paper is part of the special issue “Advances in Multirate Filter Bank Structures and Multiscale Representations.” Here is a webpage related to this work (on Laurent Duval’s website).

Abstract: In this paper, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present a new class of convex optimization programs, or decoders, coined Basis Pursuit DeQuantizer of moment p (BPDQp), that model the quantization distortion more faithfully than the commonly used Basis Pursuit DeNoise (BPDN) program.

Abstract: This paper addresses the problem of localizing people in low and high density crowds with a network of heterogeneous cameras. The problem is recasted as a linear inverse problem. It relies on deducing the discretized occupancy vector of people on the ground, from the noisy binary silhouettes observed as foreground pixels in each camera.