Joint image reconstruction and segmentation using the Potts model

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural network ...

Reconstruction of underconstrained tomographic data sets remains a major challenge. Standard analytical techniques frequently lead to unsatisfactory results due to insufficient information. Several iterative algorithms, which can easily integrate a priori ...

We propose an active learning approach to image segmentation that exploits geometric priors to speed up and streamline the annotation process. It can be applied for both background foreground and multi-class segmentation tasks in 2D images and 3D image vol ...

Mumford-Shah and Potts functionals are powerful variational models for regularization which are widely used in signal and image processing; typical applications are edge-preserving denoising and segmentation. Being both non-smooth and non-convex, they are ...

Super-resolution fluorescence microscopy improves spatial resolution, but this comes at a loss of image throughput and presents unique challenges in identifying optimal acquisition parameters. Microscope automation routines can offset these drawbacks, but ...

Ultrafast ultrasound (US) imaging based on plane wave (PW) insonification is a widely used modality nowadays. Two main types of approaches have been proposed for image reconstruction either based on classical delay-and-sum (DAS) or on Fourier reconstructio ...

Optimization is a fundamental tool in modern science. Numerous important tasks in biology, economy, physics and computer science can be cast as optimization problems. Consider the example of machine learning: recent advances have shown that even the most s ...

We propose new regularization models to solve inverse problems encountered in biomedical imaging applications. In formulating mathematical schemes, we base our approach on the sparse signal processing principles that have emerged as a central paradigm in t ...

It is commonly assumed in the optimal auction design literature that valuations of buyers are independently drawn from a unique distribution. In this paper we study auctions under ambiguity, that is, in an environment where valuation distribution is uncert ...

Brain stroke is an age-related illness which has become a major issue in our ageing societies. Early diagnosis and treatment are of high importance for the full recovery of the patient, as reminded in Anglo-Saxon countries by the abbreviation FAST (Face, A ...

Joint image reconstruction and segmentation using the Potts model

Chattez avec Graph Search

Deep neural networks for plasma tomography with applications to JET and COMPASS

A Forward Regridding Method with Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

Geometry in active learning for binary and multi-class image segmentation

Mumford-Shah and Potts Regularization for Manifold-Valued Data

Autonomous illumination control for localization microscopy

Sparse regularization methods in ultrafast ultrasound imaging

New Algorithmic Paradigms for Discrete Problems using Dynamical Systems and Polynomials

Sparsity-Based Data Reconstruction Models for Biomedical Imaging

Robust auction design under multiple priors by linear and integer programming

Microwave Imaging of Brain Stroke : Contributions to Modeling and Inverse Problem Resolution