Publications associées à Connected-component labeling

Region Extraction in Mesh Intersection

Annalisa Buffa, Pablo Antolin Sanchez, Emiliano Cirillo

Region extraction is a very common task in both Computer Science and Engineering with several applications in object recognition and motion analysis, among others. Most of the literature focuses on regions delimited by straight lines, often in the special ...

2023

Weakly supervised joint whole-slide segmentation and classification in prostate cancer

Seyedbehzad Bozorgtabar, Maria Gabrani, Guillaume Jaume, Pushpak Pati, Zeineb Ayadi, Kevin Thandiackal

The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which a ...

ELSEVIER2023

Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations

Devis Tuia, Olga Fink, Florent Evariste Forest, Hugo Laurent Pascal Porta

Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe h ...

Research Publishing2023

AI-based telepresence for broadcast applications

Touradj Ebrahimi, Rayan Daod Nathoo, Laurent Deillon, Henrique Piñeiro Monteagudo

In this paper, we introduce a new solution and underlying architecture that allows remote participants to interact with hosts in a broadcast scenario. To achieve this, background extraction is first applied to video received from remote participants to ext ...

SPIE2022

SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Mohammad Saeed Rad

Despite well-established baselines, learning of scene depth and ego-motion from monocular video remains an ongoing challenge, specifically when handling scaling ambiguity issues and depth inconsistencies in image sequences. Much prior work uses either a su ...

IEEE COMPUTER SOC2019

Neural Scene Decomposition for Multi-Person Motion Capture

Pascal Fua, Mathieu Salzmann, Isinsu Katircioglu, Helge Jochen Rhodin, Victor Constantin

Learning general image representations has proven key to the success of many computer vision tasks. For example, many approaches to image understanding problems rely on deep networks that were initially trained on ImageNet, mostly because the learned featu ...

IEEE2019

Learning Monocular 3D Human Pose Estimation from Multi-view Images

Pascal Fua, Mathieu Salzmann, Frédéric Meyer, Isinsu Katircioglu, Helge Jochen Rhodin, Victor Constantin

Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves open the problem of capturing motions for which no such database exists. Ma ...

IEEE2018

Blob properties in full-turbulence simulations of the TCV scrape-off layer

Ivo Furno, Paolo Ricci, Benoît Labit, Fabio Avino, Federico Nespoli, Federico David Halpern, Fabio Riva

To investigate blob properties in the tokamak scrape-off layer (SOL), we perform dedicated numerical nonlinear simulations of plasma turbulence in the SOL of a TCV discharge using the Global Braginskii Solver code. A blob detection technique is used for th ...

2017

Efficient Transductive Semantic Segmentation

Mathieu Salzmann

Semantically describing the contents of images is one of the classical problems of computer vision. With huge numbers of images being made available daily, there is increasing interest in methods for semantic pixel labelling that exploit large image sets. ...

Ieee2016

Exploiting Large Image Sets for Road Scene Parsing

Mathieu Salzmann

There is an increasing interest in exploiting multiple images for scene understanding, with great progress in areas such as cosegmentation and video segmentation. Jointly analyzing the images in a large set offers the opportunity to exploit a greater sourc ...

IEEE Institute of Electrical and Electronics Engineers2016

Imaging in radio interferometry by iterative subset scanning using a modified amp algorithm

Paul Hurley, Matthieu Martin Jean-André Simeoni, Giovanni Cherubini, Sanaz Kazemi

Imaging techniques in radio interferometry often face a significant challenge posed by the large number of antenna signals received, from which the image information needs to be extracted. Beam- forming is envisaged to reduce the rate required for transpor ...

2016

Multiscale Centerline Extraction Based on Regression and Projection onto the Set of Elongated Structures

Amos Sironi

Automatically extracting linear structures from images is a fundamental low-level vision problem with numerous applications in different domains. Centerline detection and radial estimation are the first crucial steps in most Computer Vision pipelines aimin ...

EPFL2016

Modeling annotator behaviors for crowd labeling

Oya Aran

Machine learning applications can benefit greatly from vast amounts of data, provided that reliable labels are available. Mobilizing crowds to annotate the unlabeled data is a common solution. Although the labels provided by the crowd are subjective and no ...

2015

Three-dimensional simulations of blob dynamics in a simple magnetized torus

Paolo Ricci, Joaquim Loizu Cisquella, Sébastien Jolliet, Federico David Halpern, Annamaria Mosetto, Annalisa Cardellini

The propagation of blobs, structures of localized enhanced plasma pressure, is studied in global three-dimensional simulations of a simple magnetized torus. In particular, we carry out single-seeded blob simulations to explore the dependence of the blob ve ...

American Institute of Physics (AIP)2014

Advances in hyperspectral image classification

Devis Tuia

The technological evolution of optical sensors over the last few decades has provided remote sensing analysts with rich spatial, spectral, and temporal information. In particular, the increase in spectral resolution of hyperspectral images (HSIs) and infra ...

Institute of Electrical and Electronics Engineers2014

Smart Imaging and On-Focal-Plane Image Acquisition and Processing in Standard CMOS Technology

Nikola Katic

In recent years, modern imaging sensors and systems have become increasingly complex following the growing demand for high-quality and high-resolution imaging. Commercially available sensors having 30-40 mega-pixel resolutions are common nowadays, while pr ...

EPFL2014

Weakly Supervised Alignment Of Image Manifolds With Semantic Ties

Devis Tuia

Aligning data distributions that underwent spectral distortions related to acquisition conditions is a key issue to improve the performance of classifiers applied to multi-temporal and multi-angular images. In this paper, we propose a feature extraction me ...

Ieee2014

Non-Linear Domain Adaptation with Boosting

Pascal Fua, Carlos Joaquin Becker, Christos Marios Christoudias

A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can gen ...

2013

Sequential Topic Models for Mining Recurrent Activities and their Relationships : Application to long term video recordings

Jagannadan Varadarajan

In this thesis, we address the analysis of activities from long term data logs with an emphasis on video recordings. Starting from simple words from video, we progressively build methods to infer higher level scene semantics. The main strategies used to ac ...

EPFL2012

Fast human detection from joint appearance and foreground feature subset covariances

Jean-Marc Odobez

We present a fast method to detect humans from stationary surveillance videos. It is based on a cascade of LogitBoost classifiers which use covariance matrices as object descriptors. We have made several contributions. First, our method learns the correlat ...

2011