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We present in this article a comparative study of well-known supervised evaluation criteria that enable the quantification of the quality of contour detection algorithms. The tested criteria are often used or combined in the literature to create new

Externí odkaz: https://doaj.org/article/1b7b5dbb14854facb8d530a60c838fc7

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Deep features and One-class classification with unsupervised data for weed detection in UAV images

Autor: Raphael Canals, Adel Hafiane, Bruno Emile, M. Dian Bah

Publikováno v: 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)
2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), Nov 2019, Istanbul, Turkey. pp.1-5, ⟨10.1109/IPTA.2019.8936091⟩
IPTA

With the raise of the world population, increasing agricultural productivity has become a necessity for farmers. One way to reduce the cost of chemicals and environmental impact is to allocate the right doses of herbicide to the right place and at th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc58db6278a55baaf074f9592c9c7581
https://hal.archives-ouvertes.fr/hal-03299512

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Action Recognition from Low-Resolution Infrared Sensor for Indoor use: A Comparative Study between Deep Learning and Classical Approaches

Autor: Bruno Emile, Helene Laurent, Félix Polla

Publikováno v: MDM

In recent years, automatic action recognition has attracted a lot of attention in the field of computer vision. In this paper, action recognition in an indoor environment using low-resolution sensor is considered. In this environment, several studies

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::52dd180bcf0e1ad031950d23f88a8cac
https://doi.org/10.1109/mdm.2019.00-11

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Rare Events Detection and Localization In Crowded Scenes Based On Flow Signature

Autor: Bruno Emile, Dieudonné Fabrice Atrevi, Damien Vivet

We introduce in this paper a novel method for rare events detection based on the optical flow signature. It aims to automatically highlight regions in videos where rare events are occurring. This kind of method can be used as an important step for ma

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bed715628e1d32fc31640cc603f36977
https://oatao.univ-toulouse.fr/26956/

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Détection d'événements rares par modélisation de la signature du flot optique

Autor: Dieudonné ATREVI, Damien Vivet, Bruno Emile

Publikováno v: GRETSI
GRETSI, Aug 2019, Lille, France
HAL

International audience; Nous présentons dans cet article une nouvelle méthode de détection d'événements rares basée sur l'analyse des mouvements saillants dans une scène. La méthode vise à localiser automatiquement toutes les régions d'une

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::4846bbb6fd76169fce064a2c9d23cd20
https://hal.archives-ouvertes.fr/hal-02277109

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Bayesian Generative Model Based on Color Histogram of Oriented Phase and Histogram of Oriented Optical Flow for Rare Event Detection in Crowded Scenes

Autor: Dieudonné Fabrice Atrevi, Bruno Emile, Damien Vivet

Publikováno v: ICASSP 2018-2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ICASSP 2018-2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, Canada. pp.3126-3130
ICASSP

In this paper, we propose a new method for rare event detection in crowded scenes using a combination of Color Histogram of Oriented Phases (CHOP) and Histogram of Oriented Optical Flow (HOOF). We propose to detect and filter spatio-temporal interest

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f7681a20a8eb0c7de750631e3aba7563
https://oatao.univ-toulouse.fr/21298/

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