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Hierarchical method of urban building extraction inspired by human perception / Chao Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)
[article]
Titre : Hierarchical method of urban building extraction inspired by human perception Type de document : Article/Communication Auteurs : Chao Tao, Auteur ; Yihua Tan, Auteur ; Zheng-Rong Zou, Auteur Année de publication : 2013 Article en page(s) : pp 1109 - 1119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification bayesienne
[Termes IGN] détection du bâti
[Termes IGN] image à haute résolution
[Termes IGN] morphologie mathématique
[Termes IGN] reconnaissance de formes
[Termes IGN] segmentation d'imageRésumé : (Auteur) In a high-resolution satellite image, buildings can be considered as clustered objects belonging to the same category. Human perception of such objects consists of an initial identification of simple instances followed by recognition of more complicated ones by deduction. Inspired by this observation, a hierarchical building extraction framework is proposed to simulate the process, which includes three major components. First, a total variation-based segmentation algorithm is presented to decompose the given image into object-level elements. Then, shape analysis is applied to extract some common and easily identified rectangular buildings. Finally, the detection of buildings with complex structures is formulated as a deduction problem based on preceding extracted information in terms of maximum a posteriori (MAP) estimation, and a Bayesian based approach is proposed to deal with it. The experimental results demonstrate that the proposed framework is capable of efficiently identifying urban buildings from high-resolution satellite images. Numéro de notice : A2013-689 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.12.1109 En ligne : https://doi.org/10.14358/PERS.79.12.1109 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32825
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 12 (December 2013) . - pp 1109 - 1119[article]Unsichere topologische Beziehungen zwischen ungenauen Flächen / Stephan Winter (1996)
Titre : Unsichere topologische Beziehungen zwischen ungenauen Flächen Titre original : [Relations topologiques incertaines entre surfaces imprécises] Type de document : Thèse/HDR Auteurs : Stephan Winter, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 1996 Collection : DGK - C Sous-collection : Dissertationen num. 465 Importance : 66 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-7696-9506-9 Note générale : Bibliographie Langues : Allemand (ger) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] classification bayesienne
[Termes IGN] incertitude de position
[Termes IGN] incertitude géométrique
[Termes IGN] logique floue
[Termes IGN] modèle mathématique
[Termes IGN] propagation d'erreur
[Termes IGN] raisonnement spatial
[Termes IGN] relation topologique
[Termes IGN] squelettisationRésumé : (Auteur) Uncertainty is an inherent property of observations. Abstracting the real world to conceptional objects is a step of generalization, and the measurement, taking place on abstracted objects, propagates this uncertainty, due to additional systematic, gross or random errors. Each spatial analysis is infected by these sources of uncertainty. It is necessary to introduce propagation of uncertainty in spatial reasoning to allow an assessment of the results. The scope of the thesis is to combine the process of observation with a mathematical model of qualitative spatial relations, modelling the randornness of the observations. A methodology is presented for probability-based decisions about spatial relations.
When determining spatial relations from positional uncertain objects, one has to distinguish between quantita-tive relations, which become imprecise, and qualitative relations, which become uncertain. Topological relations, being of qualitative nature, may or may not be true in presence of positional uncertainty. Assuming the overlay of two independent objects indicates a very small overlap, the question arises whether the two objects could be neighbored in reality. Comparing the degree of overlap to the size of uncertainty will allow to make a decision, and to assess this decision.
Because of the essential importance of considering uncertainty in spatial analysis, a theoretically well-based model of uncertainty is preferred against limitations in validity or meaning. Therefore, in this thesis, positio-nal uncertainty will be described stochastically, and the inference from this description to the uncertainty of derived spatial relations is treated with a statistical classification approach. Probabilities of single relations are determined, and the relation with maximum probability, given the evidence from observation, is chosen.
At the beginning the separation of abstraction and measurement within the observation process is discussed. The complexity of regions is limited by the smoothness of the boundary when related to its uncertainty.
In the following chapter the space of possible relations is reduced to two sub-graphs of the conceptual-neigh-borhood-graph of binary relations between regions. Depending on the sub-graph that the relation between two regions belongs to, different sets of intersection sets between the two objects become uncertain. To describe the uncertainty a morphological distance function is defined, based on the skeleton.
It is shown that it is sufficient to use the minimum and maximum distance, and to classify the relation, depending on the signs of these two values. For the first time the imprecision of measurement and the uncertainty of abstraction are described using probability densities. They are used to determine a vector of probabilities the sign of the distance values have, which is the basis for a Bayesian classification. From these distance classes the decision about the topological relation of the two objects is derived.
In the last chapter examples show the versatility of the proposed methodology.
Other approaches for handling uncertainty are discrete, using error bands, or fuzzy, with the problem of weaker results. With the strong connection to an observation process we hope to give a more valuable decision method, with probabilities as interpretable results, which should be useful! for the assessment and propagation in spatial reasoning processes.Numéro de notice : 28028 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Thèse étrangère Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=63375 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 28028-01 37.10 Livre Centre de documentation Géomatique Disponible 28028-02 37.10 Livre Centre de documentation Géomatique Disponible Nonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou (2020)
Titre : Nonparametric Bayesian learning for collaborative robot multimodal introspection Type de document : Monographie Auteurs : Xuefeng Zhou, Auteur ; Hongmin Wu, Auteur ; Juan Rojas, Auteur ; et al., Auteur Editeur : Springer Nature Année de publication : 2020 Importance : 137 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-981-1562631-- Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] classification bayesienne
[Termes IGN] inférence
[Termes IGN] interface homme-machine
[Termes IGN] modèle de Markov caché
[Termes IGN] modèle mathématique
[Termes IGN] problème de Dirichlet
[Termes IGN] robotiqueRésumé : (éditeur) This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students. Note de contenu : 1- Introduction to robot introspection
2- Nonparametric Bayesian modeling of multimodal time series
3- Incremental learning robot task representation and identification
4- Nonparametric Bayesian method for robot anomaly monitoring
5- Nonparametric Bayesian method for robot anomaly diagnose
6- Learning policy for robot anomaly recovery based on robot introspectionNuméro de notice : 25965 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007%2F978-981-15-6263-1 En ligne : https://link.springer.com/book/10.1007%2F978-981-15-6263-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96557 Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
[article]
Titre : Hyperspectral image denoising via clustering-based latent variable in variational Bayesian framework Type de document : Article/Communication Auteurs : Peyman Azimpour, Auteur ; Tahereh Bahraini, Auteur ; Hadi Sadoghi Yazdi, Auteur Année de publication : 2021 Article en page(s) : pp 3266 - 3276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification bayesienne
[Termes IGN] classification floue
[Termes IGN] distribution de Gauss
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] filtrage du bruit
[Termes IGN] filtre de Gauss
[Termes IGN] image hyperspectrale
[Termes IGN] Matlab
[Termes IGN] processeur graphique
[Termes IGN] qualité des données
[Termes IGN] variableRésumé : (auteur) The hyperspectral-image (HSI) noise-reduction step is a very significant preprocessing phase of data-quality enhancement. It has been attracting immense research attention in the remote sensing and image processing domains. Many methods have been developed for HSI restoration, the goal of which is to remove noise from the whole HSI cube simultaneously without considering the spectral–spatial similarity. When a noise-removal algorithm is used globally to the entire data set, it would not eliminate all levels of noise, effectively. Furthermore, most of the existing methods remove independent and identically distributed (i.i.d.) Gaussian noise. The real scenarios are much more complicated than this assumption. The complexity created by natural noise that has a non-i.i.d. structure leads to inefficient methods containing underestimation and invalid performance. In this article, we calculated the spatial–spectral similarity criteria by defining a set of clustering-based latent variables (CLVs) in a Bayesian framework to improve the robustness. These criteria can be extracted using the clustering operators. Then, by applying the CLV to the variational Bayesian model, we investigated a new low-rank matrix factorization denoising approach based on the proposed clustering-based latent variable (CLV-LRMF) to remove noise with the non-i.i.d. mixture of Gaussian structures. Finally, we switched to the GPU for MATLAB implementation to reduce the runtime. The experimental results show that the performance has been improved by applying the proposed CLV and demonstrate the effectiveness of the proposed CLV-LRMF over other state-of-the-art methods. Numéro de notice : A2021-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939512 Date de publication en ligne : 24/03/2021 En ligne : https://doi.org/10.1109/TGRS.2019.2939512 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97396
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3266 - 3276[article]A framework for automatic and unsupervised detection of multiple changes in multitemporal images / Francesca Bovolo in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)
[article]
Titre : A framework for automatic and unsupervised detection of multiple changes in multitemporal images Type de document : Article/Communication Auteurs : Francesca Bovolo, Auteur ; S. Marchesi, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2012 Article en page(s) : pp 2196 - 2212 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] bande B
[Termes IGN] classification bayesienne
[Termes IGN] détection automatique
[Termes IGN] détection de changement
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] seuillage d'imageRésumé : (Auteur) The detection of multiple changes (i.e., different kinds of change) in multitemporal remote sensing images is a complex problem. When multispectral images having B spectral bands are considered, an effective solution to this problem is to exploit all available spectral channels in the framework of supervised or partially supervised approaches. However, in many real applications, it is difficult/impossible to collect ground truth information for either multitemporal or single-date images. On the opposite, unsupervised methods available in the literature are not effective in handling the full information present in multispectral and multitemporal images. They usually consider a simplified subspace of the original feature space having small dimensionality and, thus, characterized by a possible loss of change information. In this paper, we present a framework for the detection of multiple changes in bitemporal and multispectral remote sensing images that allows one to overcome the limits of standard unsupervised methods. The framework is based on the following: 1) a compressed yet efficient 2-D representation of the change information and 2) a two-step automatic decision strategy. The effectiveness of the proposed approach has been tested on two bitemporal and multispectral data sets having different properties. Results obtained on both data sets confirm the effectiveness of the proposed approach. Numéro de notice : A2012-264 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2171493 Date de publication en ligne : 21/11/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2171493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31710
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 6 (June 2012) . - pp 2196 - 2212[article]Réservation
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