Concept

Connected-component labeling

Publications associées (39)

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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

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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

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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

QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval

Shaokai Ye, Yuan He, Hang Su, Jinfeng Li

We study the query-based attack against image retrieval to evaluate its robustness against adversarial examples under the black-box setting, where the adversary only has query access to the top-1 ranked unlabeled images from the database. Compared with que ...

IEEE COMPUTER SOC2021

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

Jean-Philippe Thiran, 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

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 ...

2018

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

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

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

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