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Urban objects classification by spectral library: Feasibility and applications / Walid Ouerghemmi (2017)
Titre : Urban objects classification by spectral library: Feasibility and applications Type de document : Article/Communication Auteurs : Walid Ouerghemmi , Auteur ; Sébastien Gadal, Auteur ; Gintautas Mozgeris, Auteur ; Donatas Jonikavičius, Auteur ; Christiane Weber, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Conférence : JURSE 2017, Joint urban remote sensing event 06/03/2017 08/03/2017 Lausanne Suisse Proceedings IEEE Importance : pp 1 - 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'objet
[Termes IGN] étude de faisabilité
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] milieu urbain
[Termes IGN] reconnaissance d'objetsRésumé : (auteur) Objects recognition in urban environment using multiband imagery is a difficult process, implying the use of elaborated and complex image processing methods, which are used to enhance the detection efficiency. The urban mosaics are characterized by multiple materials (e.g. manmade, urban vegetation, bare soil, transport infrastructure, etc.), which are combined together to form a complex patchwork. This study aims to take advantage of the multiband imagery, to assess the feasibility degree of the urban objects detection, and to explore some of the applications related to the multiband hyperspectral imagery classification. Numéro de notice : C2017-036 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/URBANISME Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE.2017.7924629 Date de publication en ligne : 11/05/2017 En ligne : https://doi.org/10.1109/JURSE.2017.7924629 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91864 Sparse output coding for scalable visual recognition / Bin Zhao in International journal of computer vision, vol 119 n° 1 (August 2016)
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Titre : Sparse output coding for scalable visual recognition Type de document : Article/Communication Auteurs : Bin Zhao, Auteur ; Eric P. Xing, Auteur Année de publication : 2016 Article en page(s) : pp 60 - 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] codage
[Termes IGN] décodage
[Termes IGN] matrice
[Termes IGN] reconnaissance d'objetsRésumé : (auteur) Many vision tasks require a multi-class classifier to discriminate multiple categories, on the order of hundreds or thousands. In this paper, we propose sparse output coding, a principled way for large-scale multi-class classification, by turning high-cardinality multi-class categorization into a bit-by-bit decoding problem. Specifically, sparse output coding is composed of two steps: efficient coding matrix learning with scalability to thousands of classes, and probabilistic decoding. Empirical results on object recognition and scene classification demonstrate the effectiveness of our proposed approach. Numéro de notice : A2016--152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-015-0839-4 En ligne : https://doi.org/10.1007/s11263-015-0839-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85920
in International journal of computer vision > vol 119 n° 1 (August 2016) . - pp 60 - 75[article]Object classification and recognition from mobile laser scanning point clouds in a road environment / Matti Lehtomäki in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)
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Titre : Object classification and recognition from mobile laser scanning point clouds in a road environment Type de document : Article/Communication Auteurs : Matti Lehtomäki, Auteur ; Anttoni Jaakkola, Auteur ; Juha Hyyppä, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1226 - 1239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] classification automatique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] histogramme
[Termes IGN] reconnaissance d'objets
[Termes IGN] réseau routier
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobile
[Termes IGN] télémétrie laser terrestreRésumé : (Auteur) Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future. Numéro de notice : A2016-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2476502 En ligne : https://doi.org/10.1109/TGRS.2015.2476502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80000
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 1226 - 1239[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2016021 SL Revue Centre de documentation Revues en salle Disponible A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing / Chengjiang Long in International journal of computer vision, vol 116 n° 2 (15th January 2016)
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Titre : A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing Type de document : Article/Communication Auteurs : Chengjiang Long, Auteur ; Gang Hua, Auteur ; Ashish Kapoor, Auteur Année de publication : 2016 Article en page(s) : pp 136 - 160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification bayesienne
[Termes IGN] classification dirigée
[Termes IGN] distribution de Gauss
[Termes IGN] inférence
[Termes IGN] production participative
[Termes IGN] reconnaissance d'objetsRésumé : (auteur) We present a noise resilient probabilistic model for active learning of a Gaussian process classifier from crowds, i.e., a set of noisy labelers. It explicitly models both the overall label noise and the expertise level of each individual labeler with two levels of flip models. Expectation propagation is adopted for efficient approximate Bayesian inference of our probabilistic model for classification, based on which, a generalized EM algorithm is derived to estimate both the global label noise and the expertise of each individual labeler. The probabilistic nature of our model immediately allows the adoption of the prediction entropy for active selection of data samples to be labeled, and active selection of high quality labelers based on their estimated expertise to label the data. We apply the proposed model for four visual recognition tasks, i.e., object category recognition, multi-modal activity recognition, gender recognition, and fine-grained classification, on four datasets with real crowd-sourced labels from the Amazon Mechanical Turk. The experiments clearly demonstrate the efficacy of the proposed model. In addition, we extend the proposed model with the Predictive Active Set Selection Method to speed up the active learning system, whose efficacy is verified by conducting experiments on the first three datasets. The results show our extended model can not only preserve a higher accuracy, but also achieve a higher efficiency. Numéro de notice : A2016--137 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-015-0834-9 En ligne : https://doi.org/10.1007/s11263-015-0834-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85903
in International journal of computer vision > vol 116 n° 2 (15th January 2016) . - pp 136 - 160[article]Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation / J.G. Martins in Machine Vision and Applications, vol 26 n° 2-3 (April 2015)
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Titre : Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation Type de document : Article/Communication Auteurs : J.G. Martins, Auteur ; L.S. Oliveira, Auteur ; A.S. Britto Jr, Auteur ; Robert Sabourin, Auteur Année de publication : 2015 Article en page(s) : pp 279 - 293 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse texturale
[Termes IGN] arbre (flore)
[Termes IGN] base de données d'images
[Termes IGN] classificateur
[Termes IGN] données vectorielles
[Termes IGN] reconnaissance d'objets
[Termes IGN] SIFT (algorithme)Résumé : (auteur) Multiple classifiers on the dissimilarity space are proposed to address the problem of forest species recognition from microscopic images. To that end, classical texture-based features such as Gabor filters, local binary patterns (LBP) and local phase quantization (LPQ), as well as two keypoint-based features, the scale-invariant feature transform (SIFT) and the speeded up robust features (SURF), are used to generate a pool of diverse classifiers on the dissimilarity space. A comprehensive set of experiments on a database composed of 2,240 microscopic images from 112 different forest species was used to evaluate the performance of each individual classifier of the generated pool, the combination of all classifiers, and different dynamic selection of classifiers (DSC) methods. The best result (93.03 %) was observed by incorporating probabilistic information in a DSC method based on multiple classifier behavior. Numéro de notice : A2015--098 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s00138-015-0659-0 Date de publication en ligne : 29/01/2015 En ligne : http://doi.org/10.1007/s00138-015-0659-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85410
in Machine Vision and Applications > vol 26 n° 2-3 (April 2015) . - pp 279 - 293[article]Multi-agent recognition system based on object based image analysis using WorldView-2 / Fatemeh Tabib Mahmoudi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)PermalinkReal-time object detection with sub-pixel accuracy using the level set method / F. Burkert in Photogrammetric record, vol 26 n° 134 (June - August 2011)PermalinkUnsing spatial continuity and discontinuity information to retrieve geographic entities / Z. Xie in Geocarto international, vol 24 n° 1 (February - March 2009)PermalinkCircular road sign extraction from street level images using colour, shape and texture database maps / Aurore Arlicot (2009)PermalinkProceedings of MVA 2009, IAPR Conference on Machine vision Applications, May 20-22, 2009, Keio University, Japan / Hideo Saito (2009)PermalinkSimultaneous Facial Action Tracking and Expression Recognition in the Presence of Head Motion / Fadi Dornaika in International journal of computer vision, vol 76 n°3 (March 2008)PermalinkPermalinkGeneralization of 3D building data based on a scale-space approach / A. Forberg in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 2 (June 2007)PermalinkPermalinkImage Analysis and Recognition, 4th International Conference, ICIAR 2007, Montreal, Canada, August 2007 / Mohamed Kamel (2007)PermalinkAutomatic 3D object recognition and reconstruction based on neuro-fuzzy modelling / F. Samadzadegan in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 5 (August - October 2005)PermalinkPermalinkFrom mobile mapping to telegeoinformatics: paradigm shift in geospatial data acquisition, processing, and management / Dorota A. Grejner-Brzezinska in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 2 (February 2004)PermalinkDetecting building changes from multitemporal aerial stereopairs / Franck Jung in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 3-4 (January - June 2004)PermalinkImage Analysis and Recognition, International Conference, ICIAR 2004, Porto, Portugal, September-October 2004, Part 1. Proceedings / Aurélio Campilho (2004)PermalinkPermalinkA cognitive pyramid for contextual classification of remote sensing images / E. Binaghi in IEEE Transactions on geoscience and remote sensing, vol 41 n° 12 (December 2003)PermalinkVision with non-traditional sensors, 26th workshop of the Austrian Association for Pattern Recognition (ÖAGM/AAPR), Graz, September 10 - 11, 2002 / Franz W. Leberl (2002)PermalinkA robust texture analysis and classification approach for urban land-use and land-cover feature discrimination / S.W. Myint in Geocarto international, vol 16 n° 4 (December 2001 - February 2002)PermalinkReconnaissance d'objets par focalisation et détection de changement / Franck Jung (2001)Permalink