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Titre : Conditional random fields for the classification of LiDAR point clouds Type de document : Article/Communication Auteurs : Joachim Niemeyer, Auteur ; Clément Mallet , Auteur ; Franz Rottensteiner, Auteur ; Uwe Soergel, Auteur
Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2011 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 38/4-W19 Conférence : ISPRS 2011, High-Resolution Earth Imaging for Geospatial Information workshop 14/06/2011 17/06/2011 Hanovre Allemagne OA ISPRS Archives Importance : 6 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forme d'onde pleine
[Termes IGN] prise en compte du contexte
[Termes IGN] semis de points
[Termes IGN] zone urbaine denseRésumé : (auteur) In this paper we propose a probabilistic supervised classification algorithm for LiDAR (Light Detection And Ranging) point clouds. Several object classes (i.e. ground, building and vegetation) can be separated reliably by considering each point's neighbourhood. Based on Conditional Random Fields (CRF) this contextual information can be incorporated into classification process in order to improve results. Since we want to perform a point-wise classification, no primarily segmentation is needed. Therefore, each 3D point is regarded as a graph's node, whereas edges represent links to the nearest neighbours. Both nodes and edges are associated with features and have effect on the classification. We use some features available from full waveform technology such as amplitude, echo width and number of echoes as well as some extracted geometrical features. The aim of the paper is to describe the CRF model set-up for irregular point clouds, present the features used for classification, and to discuss some results. The resulting overall accuracy is about 94 %. Numéro de notice : C2011-069 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication DOI : 10.5194/isprsarchives-XXXVIII-4-W19-209-2011 Date de publication en ligne : 07/09/2012 En ligne : https://doi.org/10.5194/isprsarchives-XXXVIII-4-W19-209-2011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101398 Conditional random fields for urban scene : Classification with full waveform LiDAR Data / Joachim Niemeyer (2011)
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Titre : Conditional random fields for urban scene : Classification with full waveform LiDAR Data Type de document : Article/Communication Auteurs : Joachim Niemeyer, Auteur ; Jan Dirk Wegner, Auteur ; Clément Mallet , Auteur ; Franz Rottensteiner, Auteur ; Uwe Soergel, Auteur
Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2011 Conférence : PIA 2011, ISPRS Conference on Photogrammetric Image Analysis 05/10/2011 07/10/2011 Munich Allemagne OA ISPRS Archives Importance : pp 233 - 244 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forme d'onde pleine
[Termes IGN] prise en compte du contexte
[Termes IGN] semis de points
[Termes IGN] zone urbaine denseRésumé : (auteur) We propose a context-based classification method for point clouds acquired by full waveform airborne laser scanners. As these devices provide a higher point density and additional information like echo width or type of return, an accurate distinction of several object classes is possible. However, especially in dense urban areas correct labelling is a challenging task. Therefore, we incorporate context knowledge by using Conditional Random Fields. Typical object structures are learned in a training step and improve the results of the point-based classification process. We validate our approach with two real-world datasets and by a comparison to Support Vector Machines and Markov Random Fields. Numéro de notice : C2011-033 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication DOI : 10.1007/978-3-642-24393-6_20 En ligne : https://doi.org/10.1007/978-3-642-24393-6_20 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85946 Documents numériques
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Conditional random fields for urban scene - postpublicationAdobe Acrobat PDFEstimating meteorological visibility using cameras: A probabilistic model-driven approach / Nicolas Hautière (2011)
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Titre : Estimating meteorological visibility using cameras: A probabilistic model-driven approach Type de document : Article/Communication Auteurs : Nicolas Hautière, Auteur ; Raouf Babari, Auteur ; Eric Dumont, Auteur ; Roland Brémont, Auteur ; Nicolas Paparoditis , Auteur
Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2011 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 6495 Conférence : ACCV 2010, 10th Asian Conference on Computer Vision 08/11/2010 12/11/2010 Queenstown Nouvelle-Zélande Proceedings Springer Importance : pp 243 - 254 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] données météorologiques
[Termes IGN] éclairement lumineux
[Termes IGN] modèle stochastique
[Termes IGN] projection Lambert
[Termes IGN] visibilitéRésumé : (auteur) Estimating the atmospheric or meteorological visibility distance is very important for air and ground transport safety, as well as for air quality. However, there is no holistic approach to tackle the problem by camera. Most existing methods are data-driven approaches, which perform a linear regression between the contrast in the scene and the visual range estimated by means of reference additional sensors. In this paper, we propose a probabilistic model-based approach which takes into account the distribution of contrasts in the scene. It is robust to illumination variations in the scene by taking into account the Lambertian surfaces. To evaluate our model, meteorological ground truth data were collected, showing very promising results. This works opens new perspectives in the computer vision community dealing with environmental issues. Numéro de notice : C2010-054 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-642-19282-1_20 En ligne : https://doi.org/10.1007/978-3-642-19282-1_20 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101400 A featureless approach to 3D polyhedral building modeling from aerial images / Karim Hammoudi in Sensors, vol 11 n° 1 (January 2011)
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Titre : A featureless approach to 3D polyhedral building modeling from aerial images Type de document : Article/Communication Auteurs : Karim Hammoudi , Auteur ; Fadi Dornaika
, Auteur
Année de publication : 2011 Article en page(s) : pp 228 - 259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image aérienne
[Termes IGN] luminance lumineuse
[Termes IGN] mesure de similitude
[Termes IGN] méthode robuste
[Termes IGN] orthoimage
[Termes IGN] reconstruction 3D du bâtiRésumé : (auteur) This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach. Numéro de notice : A2011-611 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s110100228 Date de publication en ligne : 28/12/2010 En ligne : https://doi.org/10.3390/s110100228 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91711
in Sensors > vol 11 n° 1 (January 2011) . - pp 228 - 259[article]Modelos armonicos no lineales para series temporales geodéticas = Non-linear harmonic models for geodetic time series / P.A. Martinez-Ortiz (2011)
Titre : Modelos armonicos no lineales para series temporales geodéticas = Non-linear harmonic models for geodetic time series Type de document : Thèse/HDR Auteurs : P.A. Martinez-Ortiz, Auteur ; J.M. Fernadiz Leal, Directeur de thèse Editeur : Alicante : Escuella politécnica superior Année de publication : 2011 Importance : 402 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Espagnol (spa) Descripteur : [Vedettes matières IGN] Géodésie
[Termes IGN] analyse harmonique
[Termes IGN] analyse spectrale
[Termes IGN] bruit blanc
[Termes IGN] bruit rose
[Termes IGN] géocentre
[Termes IGN] marée terrestre
[Termes IGN] masse de la Terre
[Termes IGN] Matlab
[Termes IGN] modèle non linéaire
[Termes IGN] positionnement par GPS
[Termes IGN] série temporelle
[Termes IGN] système de référence géodésiqueIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) The dissertation addresses the development of new methods and software for the spectral analysis of scalar or vectorial time series, with emphasis in the applications of geodetic interest. The starting point can be placed in the method introduced by Harada and Fukushima for the non-linear analysis of time series, which allows the recursive detection of frequencies and their associated amplitudes and phases as well as the secular mixed Fourier terms when found in the signal. That method is extended in different ways, allowing the treatment of series affected by auto correlated noise with a power law, either evenly or unevenly spaced. This is made both at the level of frequency detection and non-linear fitting. Reduction of the computational overhead is also obtained. The theoretical work is accompanied by the developing of comprehensive and specialized software for such non-linear harmonic analysis of time series using the MATLAB programming language. Much of the tools we can find today for analyzing these periodic time series are valid only for certain types whereas the programs in the thesis can be applied to oddly spaced series in the presence of combinations of white and flicker noise. The new methods and routines are used for analyzing some interesting series as those ones describing the celestial pole offsets, the geocentre variations due to the redistribution of water mass on the Earth's surface, the excess of the length of day, continental water flux and the positions of GPS stations, among others. We estimate harmonic models that explain each one of these phenomena in the considered time domain and allow us to draw conclusions of their behavior. Note de contenu : Agradecimientos
Resumen
Abstract
I MODELOS ARMÓNICOS NO LINEALES PARA SE RÍES TEMPORALES GEODÉTICAS
1. Introducción
1.1. Objetivos y metodología
1.2. Contenidos
2. El problema de la detección de señales
2.1. Definición de serie temporal
2.1.1. Componentes de una serie temporal
2.1.2. Componente de ruido
2.2. Técnicas para el estudio de series temporales
2.2.1. Análisis clásico
2.2.2. Análisis espectral
2.2.3. Análisis wavelet
2.3. El problema de la detección de señales
2.3.1. Periodograma de Lomb
2.3.2. Dominio de frecuencia
3. Análisis armónico no lineal
3.1. Introducción
3.2. Descripción del método
3.2.1. Función objetivo y funciones base
3.2.2. Solución mínimo cuadrática
3.2.3. Optimización no lineal: Algoritmo BFGS
3.2.4. Extracción de frecuencias
3.3. Incertidumbre
3.4. Generalización
3.5. Tratamiento de los términos periódicos de corta frecuencia
4. Variaciones del polo celeste
4.1. Modelo de precesión-nutación IAU1980
4.1.1. Introducción
4.1.2. Descripción de los datos
4.1.3. Características del análisis y resultados
4.1.4. Conclusiones
4.2. Modelo de precesión-nutación IAU2000
4.2.1. Introducción
4.2.2. Descripción de los datos
4.2.3. Características del análisis y resultados
4.2.4. Conclusiones
4.3. Modelos dinámicos para la predicción a corto plazo de (óV óe)
5. Variaciones geocéntricas causadas por el flujo de agua continental 101
5.1. Introducción
5.2. Descripción de los datos
5.3. Características del análisis y resultados
5.4. Conclusiones
6. Modelos espacio-temporales para el flujo de agua continental
6.1. Introducción
6.2. Descripción de datos
6.3. Características del análisis
6.4. Resultados
6.5. Conclusiones
7. Estudio armónico del exceso en la duración del día
7.1. Introducción
7.2. Descripción de los datos
7.3. Características del análisis y resultados
7.4. Conclusiones
8. Ruido
8.1. Tipología básica
8.2. Matrices de covarianza
8.2.1. Matriz de covarianzas para un ruido blanco
8.2.2. Matriz de covarianzas para un ruido parpadeante
8.2.3. Matriz de covarianzas para un paseo aleatorio
8.3. Relación ruido-periodograma
8.4. Ruido en las observaciones GPS
9. Algoritmo FHAST
9.1. Introducción
9.2. Función objetivo
9.3. Modelo estocástico
9.3.1. Estimación de un índice espectral
9.3.2. Componente residual como combinación de varios ruidos. Es-timación de la frecuencia de transición,
9.4. Modelo funcional
9.5. Estimación de la componente de varianza
9.5.1. Condición de no negatividad
9.6. Extensión del periodograma
9.6.1. Aceleración del cálculo del periodograma
9.7. Incertidumbre
9.7.1. Parámetros lineales y no lineales del modelo funcional
9.7.2. Parámetros del modelo estocástico
9.8. Criterios de parada algorítmica
9.9. Entramado algorítmico
9.10. Simulación
10.Estudio de las series de posiciones de estaciones GPS
10.1. Introducción
10.2. Análisis de las series temporales residuales
10.3. Resultados y discusión
10.4. Conclusiones
11.Conclusiones y perspectivas
11.1. Conclusiones
11.2. Perspectivas
II EXTENDED SUMMARY: NON-LINEAR HARMO NIC MODELS FOR GEODETIC TIME SERIES
S.1. Introduction
S.2. The signal detection problem
S.2.1. Definition of time series
S.2.2. Spectral analysis
S.2.3. The signal detection problem
S.3. Non-linear harmonic analysis
S.4. Celestial Pole Offsets
S.4.1. IAU1980 Pole Offsets
S.4.2. IAU2000 Pole Offsets
S.4.3. Dynamic models for (óV,óe) prediction
S.5. Geocenter variations caused by continental water flux
S.5.1. Introduction
S.5.2. Data
S.5.3. Analysis and results
S.5.4. Conclusions
S.6. Spatio-temporal models for continental water flux
S.6.1. Introduction
S.6.2. Data
S.6.3. Methods
S.6.4. Results
S.6.5. Conclusions
S.7. Harmonio study of the length of day
S.7.1. Introduction
S.7.2. Data
S.7.3. Analysis and results
S.7.4. Conclusions
S.8. Noise
S.8.1. Typology
S.8.2. Covariance matrices
S.8.3. Relationship noise-periodogram
S.8.4. Noise in GPS observations
S.9. FHAST algorithm
S.9.1. Introduction
S.9.2. Stochastic model
S.9.3. Functional model
S.9.4. Component variance estimation
S.9.5. Modification of the periodogram
S.9.6. Stop criteria
S9.7. Algorithm
S.10. Study of the position time series of GPS stations
S.10.1.Introduction
S.10.2. Analysis and results
S.10.S.Conclusions
S.11 Conclusions and outlook
S.11.1.Conclusions
S.11.2. Outlook
III APÉNDICES
A. Ley de propagación del error
B, Acrónimos, abreviaturas y unidades
C. Modelos armónicos no lineales de algunas estaciones GPS
BibliografíaNuméro de notice : 10519 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse française Note de thèse : Bibliographie nature-HAL : Thèse DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=45141 Réservation
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