Publications related to Unsupervised Connectivity-Based Thresholding Segmentation of Midsagittal Brain MR Images

Monogenic Filtering Based Automatic Defect Detection from a Single Fringe Pattern

Pramod Rastogi, Rishikesh Dilip Kulkarni

We propose a fringe analysis algorithm for the automatic detection of defects from a single fringe pattern (FP). Typically, the surface defects exhibit high fringe density areas in the FP. Consequently, high fringe density regions can be utilized as a sign ...

Springer, Cham2019

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

Pascal Fua, Raphael Sznitman, Ksenia Konyushkova

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 ...

2019

Variational Methods for Human Modeling

Timur Bagautdinov

A large part of computer vision research is devoted to building models and algorithms aimed at understanding human appearance and behaviour from images and videos. Ultimately, we want to build automated systems that are at least as capable as people when i ...

EPFL2018

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

Volkan Cevher, Jonathan Mark Scarlett, Ilija Bogunovic, Slobodan Mitrovic

We study the problem of maximizing a monotone submodular function subject to a cardinality constraint

k

, with the added twist that a number of items

\tau

from the returned set may be removed. We focus on the worst-case setting considered in Orlin et al ...

2017

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

EPFL2017

Large-Scale Image Segmentation with Convolutional Networks

Pedro Henrique Oliveira Pinheiro

Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

Sciences et Techniques de l’Ingénieur (STI)2017

Decentralized consensus optimization with asynchrony and delays

Ali H. Sayed, Kun Yuan, Tianyu Wu

We propose an asynchronous, decentralized algorithm for consensus optimization. The algorithm runs over a network of agents, where the agents perform local computation and communicate with neighbors. We design the algorithm so that the agents can compute a ...

IEEE2016

Target-based fiber assignment for large survey spectrographs

Jean-Paul Richard Kneib, Laleh Makarem

Next generation massive spectroscopic survey projects have to process a massive amount of targets. The preparation of subsequent observations should be feasible in a reasonable amount of time. We present a fast algorithm for target assignment that scales a ...

Spie-Int Soc Optical Engineering2016

Efficient greedy algorithms for high-dimensional parameter spaces with applications to empirical interpolation and reduced basis methods

Jan Sickmann Hesthaven, Benjamin Stamm

We propose two new and enhanced algorithms for greedy sampling of high- dimensional functions. While the techniques have a substantial degree of generality, we frame the discussion in the context of methods for empirical interpolation and the devel- opment ...

EDP Sciences2014

Robust image reconstruction from multi-view measurements

Pierre Vandergheynst, Gilles Puy

We propose a novel method to accurately reconstruct a set of images representing a single scene from few linear multi-view measurements. Each observed image is modeled as the sum of a background image and a foreground one. The background image is common to ...

2014

Unsupervised Connectivity-Based Thresholding Segmentation of Midsagittal Brain MR Images

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Monogenic Filtering Based Automatic Defect Detection from a Single Fringe Pattern

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

Variational Methods for Human Modeling

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

Large-Scale Image Segmentation with Convolutional Networks

Large-Scale Image Segmentation with Convolutional Networks

Decentralized consensus optimization with asynchrony and delays

Target-based fiber assignment for large survey spectrographs

Efficient greedy algorithms for high-dimensional parameter spaces with applications to empirical interpolation and reduced basis methods

Robust image reconstruction from multi-view measurements

Variational Methods for Human Modeling

Large-Scale Image Segmentation with Convolutional Networks

Large-Scale Image Segmentation with Convolutional Networks

Monogenic Filtering Based Automatic Defect Detection from a Single Fringe Pattern

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

Robust Submodular Maximization: A Non-Uniform Partitioning Approach

Decentralized consensus optimization with asynchrony and delays

Target-based fiber assignment for large survey spectrographs

Efficient greedy algorithms for high-dimensional parameter spaces with applications to empirical interpolation and reduced basis methods

Robust image reconstruction from multi-view measurements