50 citations · 102 across the 6 of their papers we have counts for
6 papers
Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation
James P. Horwath, Dmitri N. Zakharov, Remi Megret +1
Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since the…
Egocentric vision IT technologies for Alzheimer disease assessment and studies
Hugo Boujut, Vincent Buso, Guillaume Bourmaud +5
Egocentric vision technology consists in capturing the actions of persons from their own visual point of view using wearable camera sensors. We apply this new paradigm to instrumen…
Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia
Svebor Karaman, Jenny Benois-Pineau, Vladislavs Dovgalecs +5
This paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recor…
Multi-Layer Local Graph Words for Object Recognition
Svebor Karaman, Jenny Benois-Pineau, Rémi Mégret +1
In this paper, we propose a new multi-layer structural approach for the task of object based image retrieval. In our work we tackle the problem of structural organization of local…
Activities of Daily Living Indexing by Hierarchical HMM for Dementia Diagnostics
Svebor Karaman, Jenny Benois-Pineau, Jean-François Dartigues +3
This paper presents a method for indexing human ac- tivities in videos captured from a wearable camera being worn by patients, for studies of progression of the dementia diseases.…
Human Daily Activities Indexing in Videos from Wearable Cameras for Monitoring of Patients with Dementia Diseases
Svebor Karaman, Jenny Benois-Pineau, Rémi Mégret +3
Our research focuses on analysing human activities according to a known behaviorist scenario, in case of noisy and high dimensional collected data. The data come from the monitorin…