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Titre : Robust hand pose recognition from stereoscopic capture Type de document : Thèse/HDR Auteurs : Rilwan Remilekun Basaru, Auteur Editeur : Londres : University of London Press Année de publication : 2018 Importance : 200 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted in fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Computer Science, City, University of LondonLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] estimation de pose
[Termes IGN] image RVB
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] régression
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Hand pose is emerging as an important interface for human-computer interaction. The problem of hand pose estimation from passive stereo inputs has received less attention in the literature compared to active depth sensors. This thesis seeks to address this gap by presenting a data-driven method to estimate a hand pose from a stereoscopic camera input, with experimental results comparable to more expensive active depth sensors. The frameworks presented in this thesis are based on a two camera stereo rig capture as it yields a simpler and cheaper set-up and calibration. Three frameworks are presented, describing the sequential steps taken to solve the problem of depth and pose estimation of hands.
The first is a data-driven method to estimate a high quality depth map of a hand from a stereoscopic camera input by introducing a novel regression framework. The method first computes disparity using a robust stereo matching technique. Then, it applies a machine learning technique based on Random Forest to learn the mapping between the estimated disparity and depth given ground truth data. We introduce Eigen Leaf Node Features (ELNFs) that perform feature selection at the leaf nodes in each tree to identify features that are most discriminative for depth regression. The system provides a robust method for generating a depth image with an inexpensive stereo camera.
The second framework improves on the task of hand depth estimation from stereo capture by introducing a novel superpixel-based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, it introduces Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels.
The final framework introduces a stochastic approach to propose potential depth solutions to the observed stereo capture and evaluate these proposals using two convolutional neural networks (CNNs). The first CNN, configured in a Siamese network architecture, evaluates how consistent the proposed depth solution is to the observed stereo capture. The second CNN estimates a hand pose given the proposed depth. Unlike sequential approaches that reconstruct pose from a known depth, this method jointly optimizes the hand pose and depth estimation through Markov-chain Monte Carlo (MCMC) sampling. This way, pose estimation can correct for errors in depth estimation, and vice versa.
Experimental results using an inexpensive stereo camera show that the proposed system measures pose more accurately than competing methods. More importantly, it presents the possibility of pose recovery from stereo capture that is on par with depth based pose recovery.Numéro de notice : 17505 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère En ligne : https://openaccess.city.ac.uk/19938/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90396
Titre : Vehicle dynamic model based navigation for small UAVs Type de document : Thèse/HDR Auteurs : Mehran Khaghani, Auteur ; Jan Skaloud, Directeur de thèse Editeur : Zurich : Schweizerischen Geodatischen Kommission / Commission Géodésique Suisse Année de publication : 2018 Autre Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Collection : Geodätisch-Geophysikalische Arbeiten in der Schweiz, ISSN 0257-1722 num. 101 Importance : 138 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-908440-47-5 Note générale : bibliography
Thèse de Doctorat, EPFL, Lausanne, 2018Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] analyse de sensibilité
[Termes IGN] centrale inertielle
[Termes IGN] démonstration de faisabilité
[Termes IGN] drone
[Termes IGN] filtre de Kalman
[Termes IGN] GPS-INS
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] navigation autonome
[Termes IGN] positionnement par GNSS
[Termes IGN] processus stochastique
[Termes IGN] ventIndex. décimale : 30.70 Navigation et positionnement Résumé : (auteur) The dominant navigation system for small civilian UAVs today is based on integration of inertial navigation system (INS) and global navigation satellite system (GNSS). This strategy works well to navigate the UAV, as long as proper reception of GNSS signal is maintained. However, when GNSS outage occurs, the INS-based navigation solution drifts very quickly, considering the limited quality of IMU(s) employed in INS for small UAVs. In beyond visual line of sight (BVLOS) flights, this poses the serious danger of losing the UAV and its eventual falling down. Limited payload capacity and cost for small UAVs, as well as the need for operating in different conditions, with limited visibility for example, make it challenging to find a solution to reach higher levels of navigation autonomy based on conventional approaches. This thesis aims to improve the accuracy of autonomous navigation for small UAVs by at least one order of magnitude. The proposed novel approach employs vehicle dynamic model (VDM) as process model within navigation system, and treats data from other sensors such as IMU, barometric altimeter, and GNSS receiver, whenever available, as observations within the system. Such improvement comes with extra effort required to determine the VDM parameters for any specific UAV. This work investigates the internal capability of the proposed system for estimating VDM parameters as part of the augmented state vector within an extended Kalman filter (EKF) as the estimator. This reduces the efforts required to setup such navigation system that is platform dependent. Multiple experimental flights using two custom made fixed-wing UAVs are presented together with Monte-Carlo simulations. The results reveal improvements of 1 to 2 orders of magnitude in navigation accuracy during GNSS outages of a few minutes' duration. Computational cost for the proposed VDM-based navigation does not exceed 3~times that of conventional INS-based systems, which establishes its applicability for online application. A global sensitivity analysis is presented, spotting the VDM parameters with higher influence on navigation performance. This provides insight for design of calibration procedures. The proposed VDM-based navigation system can be interesting for professional UAVs from at least two points of view. Firstly, it adds little to no extra hardware and cost to the UAV. Secondly and more importantly, it might be currently the only way to reach such significant improvement in navigation autonomy for small UAVs regardless of visibility conditions and electromagnetic signals reception. Possibly, such environmental condition independence for navigation system may be needed to obtain certifications from legal authorities to expand UAV applications to new types of mission. Note de contenu : 1- Preliminaries
2- VDM-based navigation framework
3- Results and analyses
4- Conclusion remarksNuméro de notice : 21988 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse étrangère Note de thèse : Thèse Doctorat : : EPFL : 2018 nature-HAL : Thèse DOI : 10.5075/epfl-thesis-8494 En ligne : https://www.sgc.ethz.ch/publications.html Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91986 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 21988-01 30.70 Livre Centre de documentation Géodésie Disponible A wavelet decomposition and polynomial fitting-based method for the estimation of time-varying residual motion error in airborne interferometric SAR / Hai Qiang Fu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)
[article]
Titre : A wavelet decomposition and polynomial fitting-based method for the estimation of time-varying residual motion error in airborne interferometric SAR Type de document : Article/Communication Auteurs : Hai Qiang Fu, Auteur ; Jian Jun Zhu, Auteur ; Chang Cheng Wang, Auteur ; Hui Qiang Wang, Auteur ; Rong Zhao, Auteur Année de publication : 2018 Article en page(s) : pp 49 - 59 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] estimation statistique
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] polarisation
[Termes IGN] résidu
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Compensating the residual motion error (RME) is very important in airborne interferometric synthetic aperture radar (InSAR). In this paper, the wavelet decomposition and polynomial fitting-based (WDPF) method is proposed for detecting and correcting the RME. Wavelet decomposition with root-mean-square error (RMSE) change ratio-based decomposition scale identification is used to detect the RME from the differential interferogram. Polynomial fitting in combination with robust estimation-based least squares is used to absorb the incidence-angle-dependent and topography-dependent components of the RME. A simulated experiment was conducted to test the proposed WDPF method. High-precision RME (with an RMSE of 0.0375 rad) was obtained, which can meet the requirements of InSAR. Real-data L- and P-band InSAR experiments were also performed to test the WDPF method. The results confirmed that the WDPF method can effectively correct the RME for the interferogram. The RMSE of the estimated digital elevation model (DEM) was reduced from 8.03 to 3.46 m and 8.18 to 3.10 m for the L- and P-band interferograms, respectively. Finally, the effects of the external DEM error and polarization on the RME calibration were investigated. The results indicated that the global InSAR DEM products can fulfill the requirement of differential interferogram generation for the WDPF method, and the multipolarization interferograms can help to reduce the effect of the topographic error phase on RME estimation. Numéro de notice : A2018-184 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2727076 Date de publication en ligne : 09/11/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2727076 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89841
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 1 (January 2018) . - pp 49 - 59[article]Algebraic method to speed up robust algorithms: example of laser-scanned point clouds / B. Palancz in Survey review, vol 49 n° 357 (December 2017)
[article]
Titre : Algebraic method to speed up robust algorithms: example of laser-scanned point clouds Type de document : Article/Communication Auteurs : B. Palancz, Auteur ; Joseph L. Awange, Auteur ; T. Lovas, Auteur ; R. Lewis, Auteur ; B. Molnar, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 408 - 418 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] bases de Gröbner
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] valeur aberranteRésumé : (auteur) Surface reconstruction from point clouds generated by laser scanning technology has become a fundamental task in many fields of geosciences, such as robotics, computer vision, digital photogrammetry, computational geometry, digital building modelling, forest planning and operational activities. Point clouds produced by laser scanning, however, are limited due to the occurrence of occlusions, multiple reflectance and noise, and off-surface points (outliers), thus necessitating the need for robust fitting techniques. In this contribution, a fast, non-iterative and data invariant algebraic algorithm with constant O(1) complexity that fits planes to point clouds in the total least squares sense using Gaussian-type error distribution is proposed. The maximum likelihood estimator method is used, resulting in a multivariate polynomial system that is solved in an algebraic way. It is shown that for plane fitting when datasets are affected heavily by outliers, the proposed algebraic method can be embedded into the framework of robust methods like the Danish or the RANdom SAmple Consensus methods and computed in parallel to provide rigorous algebraic fitting with significantly reduced running times. Compared to the embedded traditional singular value decomposition and principal component analysis approaches, the performance of the proposed algebraic algorithm demonstrated its efficiency on both synthetic data and real laser-scanned measurements. The evaluation of a symbolic algebraic formula is practically independent of the values of its coefficients; however, the computation of the coefficients depends on the complexity of the data. Since the main advantage of the symbolic solution is its non-requirement of numerical iteration, the data complexity will have weak influence on the speed-up. The novelty of the proposed method is the use of algebraic technique in a robust plane fitting algorithm that could be applied to remote sensing data analysis/delineation/classification. In general, the method could be applied to most plane fitting problems in the geoscience field. Numéro de notice : A2017-755 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/00396265.2016.1183939 En ligne : https://doi.org/10.1080/00396265.2016.1183939 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89109
in Survey review > vol 49 n° 357 (December 2017) . - pp 408 - 418[article]Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery / Jose Alan A. Castillo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)
[article]
Titre : Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery Type de document : Article/Communication Auteurs : Jose Alan A. Castillo, Auteur ; Armando A. Apan, Auteur ; Tek N. Maraseni, Auteur ; Severino G. Salmo, Auteur Année de publication : 2017 Article en page(s) : pp 70 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] carte d'utilisation du sol
[Termes IGN] déboisement
[Termes IGN] estimation statistique
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de végétation
[Termes IGN] mangrove
[Termes IGN] modèle de simulation
[Termes IGN] Philippines
[Termes IGN] régression linéaire
[Termes IGN] rétrodiffusion
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) The recent launch of the Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity for land-based biomass mapping and monitoring especially in the tropics where deforestation is highest. Yet, unlike in agriculture and inland land uses, the use of Sentinel imagery has not been evaluated for biomass retrieval in mangrove forest and the non-forest land uses that replaced mangroves. In this study, we evaluated the ability of Sentinel imagery for the retrieval and predictive mapping of above-ground biomass of mangroves and their replacement land uses. We used Sentinel SAR and multispectral imagery to develop biomass prediction models through the conventional linear regression and novel Machine Learning algorithms. We developed models each from SAR raw polarisation backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall above-ground biomass. In contrast, the model which utilised optical bands had the lowest accuracy. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient to low vegetation cover non-forest replacement land uses such as abandoned aquaculture pond, cleared mangrove and abandoned salt pond. These models had 0.82–0.83 correlation/agreement of observed and predicted value, and root mean square error of 27.8–28.5 Mg ha−1. Among the Sentinel-2 multispectral bands, the red and red edge bands (bands 4, 5 and 7), combined with elevation data, were the best variable set combination for biomass prediction. The red edge-based Inverted Red-Edge Chlorophyll Index had the highest prediction accuracy among the vegetation indices. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of the above-ground biomass of mangroves and the replacement non-forest land uses, especially with the inclusion of elevation data. The study demonstrates encouraging results in biomass mapping of mangroves and other coastal land uses in the tropics using the freely accessible and relatively high-resolution Sentinel imagery. Numéro de notice : A2017-730 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88428
in ISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 70 - 85[article]Réservation
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