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Multifractal analysis for signal and image classification23 Mar 2017

Stéphane Jaffard
UPEC

3D SIM and measurements time-reallocation for scanning based systems: Introduction and preliminary results on these two problems14 Mar 2017

Emmanuel Soubies
EPFL STI LIB

Inverse problems and multimodality for biological imaging28 Feb 2017

Denis Fortun
EPFL STI LIB

RKHS to find the Representer Theorem for regularization operators whose null space is not finite dimensional09 Feb 2017

Harshit Gupta
EPFL STI LIB

A unified reconstruction framework for coherent imaging24 Jan 2017

Ferréol Soulez
EPFL STI LIB

BPConvNet for compressed sensing recovery in bioimaging10 Jan 2017

Kyong Jin
EPFL STI LIB

Steerable template detection based on maximum correlation: preliminary results13 Dec 2016

Adrien Depeursinge
EPFL STI LIB

Opportunities in Computational Imaging for Biomicroscopy06 Dec 2016

Prof. Michael Leibling
Idap Research Institute

A multiple scattering approach to diffraction tomography30 Nov 2016

Luc Zeng
EPFL STI LIB

Learning Optimal Shrinkage Splines for ADMM Algorithms22 Nov 2016

Ha Nguyen
EPFL STI LIB

SIGGRAPH ASIA 201601 Nov 2016

Daniel Schmitter
EPFL STI LIB

Lévy's Persian summers18 Oct 2016

Julien Fageot
EPFL STI LIB

High-quality parallel-ray X-ray CT back projection using optimized interpolation11 Oct 2016

Mike McCann
EPFL STI LIB

Algorithmic Aspects of Compressive Sensing03 Oct 2016

Verner Vlacic
Cambridge University

ICIP 201620 Sep 2016

Anaïs Badoual
EPFL STI LIB

Machine Vision forum in Heidelberg17 Aug 2016

Virginie Uhlmann
EPFL STI LIB

Title: Spline-based models for image segmentation Abstract: Splines provide a unifying framework for solving a whole variety of image-processing problems that are best formulated in the continuous domain. In particular, splines can be used to de ne a particular type of active contour algorithm called spline-snakes [1]. Active contours (or snakes) are very popular methods for image segmentation that consist in a curve evolving in the image from an initial position to the boundaries of the object of interest. Many di erent snake algorithms exist, which can usually be grouped into three main categories, namely point-based, level sets and parametric snakes. Spline-snakes are a subcategory of parametric snakes which bene t from a continuous-domain representation, hence involving less parameters and being easy to handle analytically. In addition, spline-snakes are well-suited for semi-automated analysis pipelines and therefore hold a strong potential for user-friendly segmentation frameworks. Spline-snake algorithms rely on two main ingredients. The first one is the de nation of the snake model, which includes the choice of a spline generator that serves as basis function. The snake curve is then continuously-de ned using the spline basis to interpolate between a collection of discrete control points on the image. The second ingredient is the so-called snake energy, an appropriately de ned cost function that, upon minimization, drives the deformation of the snake curve to t object boundaries. The snake energy is generally composed of external and internal forces, which attract the curve towards prominent image features (data delity) or constrain its rigidity (regularization), respectively. Out of these two aspects (snake curve model and energy), a whole zoo of spline-snakes with different properties can be defined. In this way, spline-snakes can yield both multi-purpose segmentation methods as well as approaches speci cally tuned to match the features of particular problems. In this talk, we will present in more details the general spline-snake construction and illustrate its use through a collection of applications to segmentation in 2- and 3-D biomedical images. References [1] R. Delgado-Gonzalo, V. Uhlmann, D. Schmitter, and M. Unser, \Snakes on a Plane: A Perfect Snap for Bioimage Analysis," IEEE Signal Processing Magazine, vol. 32, no. 1, pp. 41--48, January 2015.

K-space interpolation using CV(complex valued)-CNN & sparse and low-rank model of ALOHA09 Aug 2016

Kyong Jin
EPFL STI LIB

Complete Compressed Sensing Framework for STEM Tomography19 Jul 2016

Laurène Donati
EPFL STI LIB

Trainable shrinkage splines: inverse problems meet deep learning28 Jun 2016

Ha Nguyen
EPFL STI LIB

A reconstruction framework for coherent imaging31 May 2016

Ferréol Soulez
EPFL STI LIB

Shape-Constrained Tracking with Active Contours17 May 2016

Virginie Uhlmann
EPFL STI LIB

Steerable Wavelet Machines (SWM): Learning Moving Frames for Texture Classification / Some results on MA-TIRF reconstruction and exact continuous penalties for l2-l0 minimization03 May 2016

Adrien Depeursinge, Emmanuel Soubies
EPFL STI LIB

Learning-Based approach in Single Molecule localization microscopy19 Apr 2016

Silvia Colabrese
Italian Institute of Technology, Genova, Italy

ISBI 201606 Apr 2016

Michael Unser, Denis Fortun

More info ...

ICASSP 201615 Mar 2016

Pedram Pad
EPFL STI LIB

Fast 3D Reconstruction Method for Differential Phase Contrast X-ray CT08 Mar 2016

Mike McCann

More info ...

Sparsity and the optimality of splines for inverse problems: Deterministic vs. statistical justifications23 Feb 2016

Michael Unser
EPFL STI LIB

Decoding Epileptogenesis: A Dynamical System Approach09 Feb 2016

Prof. Francois Meyer
University of Colorado at Boulder

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