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Robust vehicle detection in aerial images using bag-of-words and orientation aware scanning / Hailing Zhou in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)
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Titre : Robust vehicle detection in aerial images using bag-of-words and orientation aware scanning Type de document : Article/Communication Auteurs : Hailing Zhou, Auteur ; Lei Wei, Auteur ; Chee Peng Lim, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 7074 - 7085 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] accentuation d'image
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image captée par drone
[Termes IGN] méthode robuste
[Termes IGN] modèle sac-de-mots
[Termes IGN] objet mobile
[Termes IGN] PMVS
[Termes IGN] SIFT (algorithme)
[Termes IGN] transformation de Radon
[Termes IGN] véhiculeRésumé : (auteur) This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with “one-window-one-car” results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images. Numéro de notice : A2018-555 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2848243 Date de publication en ligne : 17/07/2018 En ligne : http://dx.doi.org/10.1109/TGRS.2018.2848243 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91654
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 12 (December 2018) . - pp 7074 - 7085[article]Spatio-temporal grid mining applied to image classification and cellular automata analysis / Romain Deville (2018)
Titre : Spatio-temporal grid mining applied to image classification and cellular automata analysis Type de document : Thèse/HDR Auteurs : Romain Deville, Auteur ; Christine Solnon, Directeur de thèse ; Elisa Fromont, Directeur de thèse ; Baptiste Jeudy, Directeur de thèse Editeur : Lyon : Institut National des Sciences Appliquées INSA Lyon Année de publication : 2018 Importance : 145 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université de Lyon opérée au sein de l’INSA de Lyon, Spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] automate cellulaire
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] exploration de données
[Termes IGN] grille
[Termes IGN] modèle sac-de-mots
[Termes IGN] SIFT (algorithme)Index. décimale : THESE Thèses et HDR Résumé : (auteur) During this thesis, we consider the exhaustive graph mining problem for a special kind of graphs : the grids. Theses grids can be used to model objects that present a regular structure. These structures are naturally present in multiple board games (checkers, chess or go for instance) or in ecosystems models using cellular automata. It is also possible to find this structure in a lower level in images, which are 2D grids of pixels, or even in videos, which are 2D+t spatio-temporal grids of pixels. In this thesis, we proposed a new algorithm to find frequent patterns dedicated to spatio-temporal grids, GriMA. Use of regular grids allow our algorithm to reduce the complexity of the isomorphisms test. These tests are often use by generic graph mining algorithm but because of their complexity, they are rarely used on real data. Two applications were proposed to evaluate our algorithm: image classification for 2D grids mining and prediction of cellular automata for 2D+t grids mining. Note de contenu : 1- Introduction
2- Background on graphs
3- Existing graph mining algoritms
4- Definition on grids
5- Description of GriMA
6- Application of GriMA to image classification
7- Application of GriMA to cellular automata analysis
8- ConclusionNuméro de notice : 25959 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Université de Lyon : 2018 Organisme de stage : LIRIS nature-HAL : Thèse DOI : sans En ligne : https://hal.science/tel-01865020 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96466
Titre : Time Series Classification Algorithms with Applications in Remote Sensing Type de document : Thèse/HDR Auteurs : Adeline Bailly, Auteur ; Romain Tavenard, Directeur de thèse Editeur : Rennes : Université Bretagne Loire Année de publication : 2018 Importance : 181 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse Université Rennes 2 pour obtenir le titre de Docteur de l'Université Bretagne Loire, Mention InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image satellite
[Termes IGN] modèle sac-de-mots
[Termes IGN] série temporelle
[Termes IGN] SIFT (algorithme)
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Time Series Classification (TSC) has received an important amount of interest over the past years due to many real-life applications. In this PhD, we create new algorithms for TSC, with a particular emphasis on Remote Sensing (RS) time series data. We first propose the Dense Bag-of-Temporal-SIFT-Words (D-BoTSW) method that uses dense local features based on SIFT features for 1D data. Extensive experiments exhibit that D-BoTSW significantly outperforms nearly all compared standalone baseline classifiers. Then, we propose an enhancement of the Learning Time Series Shapelets (LTS) algorithm called Adversarially-Built Shapelets (ABS) based on the introduction of adversarial time series during the learning process. Adversarial time series provide an additional regularization benefit for the shapelets and experiments show a performance improvement between the baseline and our proposed framework. Due to the lack of available RS time series datasets,we also present and experiment on two remote sensing time series datasets called TiSeLaCand Brazilian-Amazon Note de contenu : Introduction
1- Time series classification: State of the art
2- Time series classification based on local features representation
3- Improving time series shapelets based on adversarial examples
4- Time series classification: Remote sensing applications
Conclusion and perspectivesNuméro de notice : 25809 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Rennes : 2018 nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02139897/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95069