Publications associées à A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches have shown remarkable success on synthetic datasets, we have observed them to fail in the presence of real-world data. We ...

Ieee Computer Soc2024

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Martin Weigert

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and s ...

Nature Portfolio2024

Wave-momentum shaping for moving objects in heterogeneous and dynamic media

Stefan Rotter, Romain Christophe Rémy Fleury, Bakhtiyar Orazbayev, Matthieu Francis Malléjac

Light and sound waves can move objects through the transfer of linear or angular momentum, which has led to the development of optical and acoustic tweezers, with applications ranging from biomedical engineering to quantum optics. Although impressive manip ...

2024

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro, Xiaolong Lu

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Inverse Problems in Pulse-Echo Ultrasound: Exploiting Coherence for Image Enhancement and Quantitative Imaging

Samuel Denys Beuret

Whereas pulse-echo ultrasound imaging relied on focused acoustic waves since its inception, the last two decades have seen the development of techniques based on unfocused waves, including ultrafast ultrasound imaging. In large part due to the emergence of ...

EPFL2024

Test-time adaptation for 6D pose tracking

Andrea Cavallaro

We propose a test -time adaptation for 6D object pose tracking that learns to adapt a pre -trained model to track the 6D pose of novel objects. We consider the problem of 6D object pose tracking as a 3D keypoint detection and matching task and present a mo ...

Elsevier Sci Ltd2024

Non-contact robotic manipulation of floating objects: exploiting emergent limit cycles

Nana Obayashi, Josephine Anna Eleanor Hughes

The study of non-contact manipulation in water, and the ability to robotically control floating objects has gained recent attention due to wide-ranging potential applications, including the analysis of plastic pollution in the oceans and the optimization o ...

Frontiers Media Sa2023

Electroadhesive gripping system and method for gripping an object

Herbert Shea, Krishna Manaswi Digumarti, Vito Cacucciolo

An electroadhesive gripping system and a method for gripping and manipulating an object are disclosed. The invention provides a novel approach particularly useful to pick fabrics or otherwise flat and flexible objects including a method relying on electroa ...

2023

CLIP the Gap: A Single Domain Generalization Approach for Object Detection

Mathieu Salzmann, Martin Pierre Engilberge, Vidit Vidit

Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remai ...

Ieee Computer Soc2023

Affordance segmentation of hand-occluded containers from exocentric images

Andrea Cavallaro

Visual affordance segmentation identifies the surfaces of an object an agent can interact with. Common challenges for the identification of affordances are the variety of the geometry and physical properties of these surfaces as well as occlusions. In this ...

Ieee Computer Soc2023

SAGTTA: SALIENCY GUIDED TEST TIME AUGMENTATION FOR MEDICAL IMAGE SEGMENTATION ACROSS VENDOR DOMAIN SHIFT

Seyedbehzad Bozorgtabar, Devavrat Tomar

Test time augmentation has been shown to be an effective approach to combat domain shifts in deep learning. Despite their promising performance levels, the interpretability of the underlying used models is however low. Saliency maps have been widely used i ...

IEEE2023

Combination of dielectrophoretic and hydrodynamic traps for the controlled contact between cells or beads and application to adhesion assays

Clémentine Sophie Sarah Lipp

Multicellular organisms require very well organized and finely balanced cell-cell communication, adhesion and coordination to ensure the organisms homeostasis. These functions rely on specialized receptors placed at the cells membrane whose binding to thei ...

EPFL2023

Confidence Matters: Applications to Semantic Segmentation

Prabhu Teja Sivaprasad

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

EPFL2023

Effects of item distinctiveness on the retrieval of objects and object-location bindings from visual working memory

Yury Markov

Visual working memory (VWM) is prone to interference from stored items competing for its limited capacity. Distinctiveness or similarity of the items is acknowledged to affect this competition, such that poor item distinctiveness causes a failure to discri ...

2022

Between digital and territorial turns: A forking path

Elena Cogato Lanza, Chiara Cavalieri

This chapter looks at the intertwined evolution of two different ideas throughout the past century: that of ‘territory’, that has been reshaped and redefined in the field of architecture and urbanism, and that of ‘digital’, that while becoming dominant, ha ...

Routledge2022

Learning to Represent and Reconstruct 3D Deformable Objects

Jan Bednarík

Representing and reconstructing 3D deformable shapes are two tightly linked problems that have long been studied within the computer vision field. Deformable shapes are truly ubiquitous in the real world, whether be it specific object classes such as human ...

EPFL2022

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Beril Besbinar

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

EPFL2022

Opacité et transparence dans le design d'un dispositif de surveillance urbain : le cas de l'IMSI catcher

Félicien Michel Marie Goguey

This thesis assesses the surveillance operated on the mobile phone network by governmental actors (intelligence agencies, police, army) and the relationship between monitored spaces and their users. Indeed, some new surveillance devices used by intelligenc ...

EPFL2022

Catch Me If You Can Designing a Disobedient Object to Protest Against GSM Surveillance

Félicien Michel Marie Goguey

In this article, I discuss the process of designing an object to protest against a specific surveillance device: the IMSI catcher, a controversial object used to monitor GSM networks. Being widely used in protests, I develop a tactical approach based on ob ...

ASSOC COMPUTING MACHINERY2021

Gradient-based Methods for Deep Model Interpretability

Suraj Srinivas

In this dissertation, we propose gradient-based methods for characterizing model behaviour for the purposes of knowledge transfer and post-hoc model interpretation. Broadly, gradients capture the variation of some output feature of the model upon unit vari ...

EPFL2021