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A Variational Framework for the Simultaneous Segmentation and Object Behavior Classification of Image Sequences

Publications associées (36)

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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

Object Priors for Volumetric Image Segmentation

Pamuditha Udaranga Wickramasinghe

Large training datasets have played a vital role in the success of modern deep learning methods in computer vision. But, obtaining sufficient amount of training data is challenging, specially when annotating volumetric images. This is because fully annotat ...

EPFL2022

Horizontal Healing

Anja Fröhlich

A Concideration of Typologies for Housing the Sick as a Spatial Manifestation of knowledge. ...

EPFL Press2022

Lifelong Machine Learning with Data Efficiency and Knowledge Retention

Fei Mi

Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...

EPFL2021

Rethinking data browsing through embodied knowledge

In the age of digital and big data, the interaction with machines is central and emphasizes the sophistication of user interfaces and browsing tools. However, in the domain of digital artwork collections, most platforms propose access to images through key ...

2021

The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models

André Anjos

The segmentation of the retinal vasculature from eye fundus images represents one of the most fundamental tasks in retinal image analysis. Over recent years, increasingly complex approaches based on sophisticated Convolutional Neural Network architectures ...

2020

Learning to Reduce Annotation Load

Ksenia Konyushkova

Modern machine learning methods and their applications in computer vision are known to crave for large amounts of training data to reach their full potential. Because training data is mostly obtained through humans who manually label samples, it induces a ...

EPFL2019

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

Jean-Philippe Thiran, Hazim Kemal Ekenel, Guillaume Jaume

We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely ...

IEEE2019

Using A Priori Knowledge to Improve Scene Understanding

Alexandre Massoud Alahi

Semantic segmentation algorithms that can robustly segment objects across multiple camera viewpoints are crucial for assuring navigation and safety in emerging applications such as autonomous driving. Existing algorithms treat each image in isolation, but ...

IEEE2019

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