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Trajectory-based place-recognition for efficient large scale localization / Simon Lynen in International journal of computer vision, vol 124 n° 1 (August 2017)  

Public [article]  inInternational journal of computer vision > vol 124 n° 1 (August 2017) . - pp 49 – 64 Titre : Trajectory-based place-recognition for efficient large scale localization Type de document : Article/Communication Auteurs : Simon Lynen, Auteur ; Michael Bosse, Auteur ; Roland Siegwart, Auteur Année de publication : 2017 Article en page(s) : pp 49 – 64 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] alignement multiple semi-dirigé [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] appariement automatique [Termes descripteurs IGN] lieu géométrique [Termes descripteurs IGN] précision de localisation [Termes descripteurs IGN] reconnaissance automatique Résumé : (auteur) Place recognition is a core competency for any visual simultaneous localization and mapping system. Identifying previously visited places enables the creation of globally accurate maps, robust relocalization, and multi-user mapping. To match one place to another, most state-of-the-art approaches must decide a priori what constitutes a place, often in terms of how many consecutive views should overlap, or how many consecutive images should be considered together. Unfortunately, such threshold dependencies limit their generality to different types of scenes. In this paper, we present a placeless place recognition algorithm using a novel match-density estimation technique that avoids heuristically discretizing the space. Instead, our approach considers place recognition as a problem of continuous matching between image streams, automatically discovering regions of high match density that represent overlapping trajectory segments. The algorithm uses well-studied statistical tests to identify the relevant matching regions which are subsequently passed to an absolute pose algorithm to recover the geometric alignment. We demonstrate the efficiency and accuracy of our methodology on three outdoor sequences, including a comprehensive evaluation against ground-truth from publicly available datasets that shows our approach outperforms several state-of-the-art algorithms for place recognition. Furthermore we compare our overall algorithm to the currently best performing system for global localization and show how we outperform the approach on challenging indoor and outdoor datasets. Numéro de notice : A2017-399 Thématique : IMAGERIE Nature : Article En ligne : https://doi.org/10.1007/s11263-016-0947-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85941 [article]

Unsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2250 - 2261 Titre : Unsupervised feature learning for land-use scene recognition Type de document : Article/Communication Auteurs : Jiayuan Fan, Auteur ; Tao Chen, Auteur ; Shijian Lu, Auteur Année de publication : 2017 Article en page(s) : pp 2250 - 2261 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] algorithme d'apprentissage [Termes descripteurs IGN] analyse discriminante [Termes descripteurs IGN] codage [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] invariant [Termes descripteurs IGN] pouvoir de résolution géométrique [Termes descripteurs IGN] reconnaissance automatique [Termes descripteurs IGN] Singapour [Termes descripteurs IGN] utilisation du sol Résumé : (Auteur) This paper proposes a novel unsupervised feature learning algorithm for land-use scene recognition on very high resolution remote sensing imagery. The proposed technique utilizes a multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex remote sensing sceneries. Unlike the previous sparse coding and bag-of-visual-words-based techniques that rely on the handcrafted feature descriptors such as scale-invariant feature transform, the proposed technique extracts dense low-level features from the raw data, including the visual (RGB) data and near-infrared (NIR) data, using image patches of varying sizes at different layers. The proposed technique has been evaluated on three data sets, including the 21-category UC Merced landuse RGB data set with a 1-ft spatial resolution, the 9-category ground scene RGB-NIR data set, and the 10-category Singapore land-use RGB-NIR data set with a 0.5-m spatial resolution. The experimental results show that the proposed technique outperforms the state-of-the-art methods. Numéro de notice : A2107-174 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2640186 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84723 [article]

Single Image Super-Resolution based on Neural Networks for text and face recognition / Clément Peyrard (2017)  

Public Titre : Single Image Super-Resolution based on Neural Networks for text and face recognition Type de document : Thèse/HDR Auteurs : Clément Peyrard, Auteur ; Christophe Garcia, Auteur Editeur : Université de Lyon Année de publication : 2017 Autre Editeur : Lyon : Institut National des Sciences Appliquées INSA Lyon Importance : 187 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l'Université de Lyon opérée au sein de INSA de Lyon, discipline : Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] artefact [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de visage [Termes descripteurs IGN] image à basse résolution [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] méthode fondée sur le noyau [Termes descripteurs IGN] reconnaissance automatique [Termes descripteurs IGN] reconnaissance de caractères [Termes descripteurs IGN] reconnaissance de formes Résumé : (auteur) This thesis is focussed on super-resolution (SR) methods for improving automatic recognition system (Optical Character Recognition, face recognition) in realistic contexts. SR methods allow to generate high resolution images from low resolution ones. Unlike upsampling methods such as interpolation, they restore spatial high frequencies and compensate artefacts such as blur or jaggy edges. In particular, example-based approaches learn and model the relationship between low and high resolution spaces via pairs of low and high resolution images. Artificial Neural Networks are among the most efficient systems to address this problem. This work demonstrate the interest of SR methods based on neural networks for improved automatic recognition systems. By adapting the data, it is possible to train such Machine Learning algorithms to produce high-resolution images. Convolutional Neural Networks are especially efficient as they are trained to simultaneously extract relevant non-linear features while learning the mapping between low and high resolution spaces. On document text images, the proposed method improves OCR accuracy by +7.85 points compared with simple interpolation. The creation of an annotated image dataset and the organisation of an international competition (ICDAR2015) highlighted the interest and the relevance of such approaches. Moreover, if a priori knowledge is available, it can be used by a suitable network architecture. For facial images, face features are critical for automatic recognition. A two step method is proposed in which image resolution is first improved, followed by specialised models that focus on the essential features. An off-the-shelf face verification system has its performance improved from +6.91 up to +8.15 points. Finally, to address the variability of real-world low-resolution images, deep neural networks allow to absorb the diversity of the blurring kernels that characterise the low-resolution images. With a single model, high-resolution images are produced with natural image statistics, without any knowledge of the actual observation model of the low-resolution image. Note de contenu : 1- Introduction 2- Definitions and application domains 3- Literature review 4- Text single image super-resolution 5- Face single image super-resolution 6- Blind and robust super-resolution 7- Conclusion Numéro de notice : 25863 Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Discipline : Informatique : Lyon 2017 Organisme de stage : LIRIS En ligne : http://www.theses.fr/2017LYSEI083 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95506

Image based geo-localization in the Alps / Olivier Saurer in International journal of computer vision, vol 116 n° 3 (February 2016)  

Public [article]  inInternational journal of computer vision > vol 116 n° 3 (February 2016) . - pp 213 - 225 Titre : Image based geo-localization in the Alps Type de document : Article/Communication Auteurs : Olivier Saurer, Auteur ; Georges Baatz, Auteur ; Kevin Köser, Auteur Année de publication : 2016 Article en page(s) : pp 213 - 225 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Alpes centrales [Termes descripteurs IGN] analyse visuelle [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] exploration de données géographiques [Termes descripteurs IGN] image 2D [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle numérique de terrain [Termes descripteurs IGN] reconnaissance automatique [Termes descripteurs IGN] Suisse Résumé : (Auteur) Given a picture taken somewhere in the world, automatic geo-localization of such an image is an extremely useful task especially for historical and forensic sciences, documentation purposes, organization of the world’s photographs and intelligence applications. While tremendous progress has been made over the last years in visual location recognition within a single city, localization in natural environments is much more difficult, since vegetation, illumination, seasonal changes make appearance-only approaches impractical. In this work, we target mountainous terrain and use digital elevation models to extract representations for fast visual database lookup. We propose an automated approach for very large scale visual localization that can efficiently exploit visual information (contours) and geometric constraints (consistent orientation) at the same time. We validate the system at the scale of Switzerland (40,000 km2) using over 1000 landscape query images with ground truth GPS position. Numéro de notice : A2016--139 Thématique : IMAGERIE Nature : Article En ligne : https://doi.org/10.1007/s11263-015-0830-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85867 [article]

Fast robust large-scale mapping from video and internet photo collections / J. Frahm in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 6 (November - December 2010)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 65 n° 6 (November - December 2010) . - pp 538 - 549 Titre : Fast robust large-scale mapping from video and internet photo collections Type de document : Article/Communication Auteurs : J. Frahm, Auteur ; Marc Pollefeys, Auteur ; S. Lazebnik, Auteur ; D. Gallup, Auteur ; B. Clipp, Auteur ; et al., Auteur Année de publication : 2010 Article en page(s) : pp 538 - 549 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] grande échelle [Termes descripteurs IGN] image vidéo [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] photographie numérique [Termes descripteurs IGN] reconnaissance automatique [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] reconstruction automatique [Termes descripteurs IGN] séquence d'images Résumé : (Auteur) This paper presents a system approaching fully automatic 3D modeling of large-scale environments. Our system takes as input either a video stream or collection of photographs obtained from Internet photo sharing web-sites such as Flickr. The system achieves high computational performance through algorithmic optimizations for efficient robust estimation, the use of image-based recognition for efficient grouping of similar images, and two-stage stereo estimation for video streams that reduces the computational cost while maintaining competitive modeling results. In addition to algorithmic advances, we achieve a major improvement in computational speed through parallelization and execution on commodity graphics hardware. These improvements lead to real-time video processing and to reconstruction from tens of thousands of images within the span of a day on a single commodity computer. We demonstrate modeling results on a variety of real-world video sequences and photo collections. Numéro de notice : A2010-486 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30679 [article]

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