Publications related to Multiresolution Subdivision Snakes

Analytical prediction of the final error due to noise in a ϕ-OTDR system

Luc Thévenaz, Malak Mohamed Hossameldeen Omar Mohamed Galal, Yuting Yang, Li Zhang, Suneetha Sebastian

An analytical expression to estimate the uncertainty on the frequency shift in ϕ-OTDR systems is established and confirmed by actual data. ...

Optica Publishing Group2022

On Linear Interpolation in the Latent Space of Deep Generative Models

Quentin Christian Becker, Mike Yan Michelis

The underlying geometrical structure of the latent space in deep generative models is in most cases not Euclidean, which may lead to biases when comparing interpolation capabilities of two models. Smoothness and plausibility of linear interpolations in lat ...

2021

Functional Tucker Approximation Using Chebyshev Interpolation

Daniel Kressner, Christoph Max Strössner

This work is concerned with approximating a trivariate function defined on a tensor-product domain via function evaluations. Combining tensorized Chebyshev interpolation with a Tucker decomposition of low multilinear rank yields function approximations tha ...

SIAM PUBLICATIONS2021

Examining the Effects of Privacy-Aware Blended Learning Scenarios in Executive Training for Policymakers and Government Officials

Denis Gillet, Isabelle Barbara Marie-Hélène Cardia, Juan Carlos Farah, Maria Gaci

Enabling blended learning scenarios that foster participation and interaction is an important consideration in the design of executive training programs. To investigate this design process, our study focuses on two aspects that are present in such scenario ...

Springer2020

Systematic analysis of wavelet denoising methods for neural signal processing

Silvestro Micera

Objective. Among the different approaches for denoising neural signals, wavelet-based methods are widely used due to their ability to reduce in-band noise. All wavelet denoising algorithms have a common structure, but their effectiveness strongly depends o ...

IOP PUBLISHING LTD2020

Compactly-Supported Smooth Interpolators for Shape Modeling with Varying Resolution

Michaël Unser, Julien René Pierre Fageot, Pablo Garcia-Amorena Garcia, Daniel Andreas Schmitter, Anaïs Laure Marie-Thérèse Badoual

In applications that involve interactive curve and surface modeling, the intuitive manipulation of shapes is crucial. For instance, user interaction is facilitated if a geometrical object can be manipulated through control points that interpolate the shape ...

2017

Sampling and Reconstruction of Shapes with Algebraic Boundaries

Martin Vetterli, Arash Amini, Mitra Fatemi

We present a sampling theory for a class of binary images with finite rate of innovation (FRI). Every image in our model is the restriction of

\mathds{1}_{\{p\leq0\}}

to the image plane, where

\mathds{1}

denotes the indicator function and

p

is some r ...

2016

A Family of Smooth and Interpolatory Basis Functions for Parametric Curve and Surface Representation

Michaël Unser, Daniel Andreas Schmitter, Ricard Delgado Gonzalo

Interpolatory basis functions are helpful to specify parametric curves or surfaces that can be modified by simple user-interaction. Their main advantage is a characterization of the object by a set of control points that lie on the shape itself (i.e., curv ...

2016

Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Fabio Nobile, Giovanni Migliorati

We study the accuracy of the discrete least-squares approximation on a finite-dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. Th ...

2015

MATHICSE Technical Report : Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Fabio Nobile, Giovanni Migliorati

We study the accuracy of the discrete least-squares approximation on a finite dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. Th ...

MATHICSE2015

Multiresolution Subdivision Snakes

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Analytical prediction of the final error due to noise in a ϕ-OTDR system

On Linear Interpolation in the Latent Space of Deep Generative Models

Functional Tucker Approximation Using Chebyshev Interpolation

Examining the Effects of Privacy-Aware Blended Learning Scenarios in Executive Training for Policymakers and Government Officials

Systematic analysis of wavelet denoising methods for neural signal processing

Compactly-Supported Smooth Interpolators for Shape Modeling with Varying Resolution

Sampling and Reconstruction of Shapes with Algebraic Boundaries

A Family of Smooth and Interpolatory Basis Functions for Parametric Curve and Surface Representation

Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

MATHICSE Technical Report : Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Analytical prediction of the final error due to noise in a ϕ-OTDR system

A Family of Smooth and Interpolatory Basis Functions for Parametric Curve and Surface Representation

Functional Tucker Approximation Using Chebyshev Interpolation

Sampling and Reconstruction of Shapes with Algebraic Boundaries

MATHICSE Technical Report : Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Compactly-Supported Smooth Interpolators for Shape Modeling with Varying Resolution

On Linear Interpolation in the Latent Space of Deep Generative Models

Examining the Effects of Privacy-Aware Blended Learning Scenarios in Executive Training for Policymakers and Government Officials

Systematic analysis of wavelet denoising methods for neural signal processing