15 accepted papers
Estimating frequencies of damped sinusoids is the underlying problem of many practical applications. Existing algorithms are often inaccurate for estimating frequencies from damped sinusoids since many of them are designed for undamped signals. In this paper we propose an algorithm to estimate frequ…
The translational equivariant nature of Convolutional Neural Networks (CNNs) is a reason for its great success in computer vision. However, networks do not enjoy more general equivariance properties such as rotation or scaling, ultimately limiting their generalization performance. To address this li…
Signal and image representations that are steerable are essential to capture efficiently directional features. However, those that are successful at achieving directional selectivity usually use too many subbands, resulting in low computational efficiency. In this paper, we propose a two-dimensional…
In this paper, we propose a generalized Blind Source Separation (BSS) method using a novel assumption which we call “weak exclusion” principle. We first give the mathematical definition of the exclusion criterion and propose an iterative algorithm to minimize it. We then test WEP in simulated and re…
We consider physical fields induced by a finite number of instantaneous diffusion sources, which we sample using a mobile sensor, along unknown trajectories composed of multiple linear segments. We address the problem of estimating the sources, as well as the trajectory of the mobile sensor. Within…
In the theory of linear models, the degrees of freedom (DOF) of an estimator play a pivotal role in risk estimation, as it quantifies the complexity of a statistical modeling procedure. Considering the total-variation (TV) regularization, we present a theoretical study of the DOF in Stein’s unbiased…
While sensors have been widely used in various applications, an essential current trend of research consists of collecting and fusing the information that comes from many sensors. In this paper, on the contrary, we would like to concentrate on a unique mobile sensor; our goal is to unveil the multid…
Super-resolution is the art of recovering spikes from their low-pass projections. Over the last decade specifically, several significant advancements linked with mathematical guarantees and recovery algorithms have been made. Most super-resolution algorithms rely on a two-step procedure: deconvoluti…
Recently, total variation (TV)-based regularization has become a standard technique for signal denoising. The reconstruction quality is generally sensitive to the value of regularization parameter. In this work, based on Cham-bolle's algorithm, we develop two data-driven optimization schemes based o…
Estimation of conduction velocity (CV) is an important task in the analysis of surface electromyography (sEMG). The problem can be framed as estimation of a time-varying delay (TVD) between electrode recordings. In this paper we present an algorithm which incorporates information from multiple elect…
The field of signal processing is replete with exemplary problems where the measurements amount to time-delayed and amplitude scaled echoes of some template function or a pulse. When the inter-pulse spacing is favorable, something as primitive as a matched filter serves the purpose of identifying ti…
Sparsity-promoting regularization is often formulated as ℓv-penalized minimization (0 <; v ≤ 1), which can be efficiently solved by iteratively reweighted least squares (IRLS). The reconstruction quality is generally sensitive to the value of regularization parameter. In this work, for accurate reco…
Recently, sampling theory has been broadened to include a class of non-bandlimited signals that possess finite rate of innovation (FRI). In this paper, we consider the problem of determining the minimum rate of innovation (RI) in a noisy setting. First, we adapt a recent model-fitting algorithm for…
We propose a novel edge detection algorithm with sub-pixel accuracy based on annihilation of signals with finite rate of innovation [1, 2]. We show that the Fourier domain annihilation equations can be interpreted as spatial domain multiplications. From this new perspective, we obtain an accurate es…
The optical flow is a velocity field that describes the motion of pixels within a sequence (or set) of images. Its estimation plays an important role in areas such as motion compensation, object tracking and image registration. In this paper, we present a novel framework to estimate the optical flow…