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Titre : Applications of pattern recognition Type de document : Monographie Auteurs : Carlos M. Travieso-Gonzalez, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 136 p. ISBN/ISSN/EAN : 978-1-78985-561-6 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse de données
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] état de l'art
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] reconnaissance de formes
[Termes IGN] reconstruction 3D
[Termes IGN] structure-from-motionRésumé : (Editeur) Nowadays, technological advances allow the development of many applications in different fields. In this book, two important fields are shown. The first field, data analysis, is a good tool to identify patterns; in particular, it is observed by a stereoscopic calculation model based on fixation eye movement, a visual interactive programming learning system, an approach based on color analysis of Habanero chili pepper, an approach for the visualization and analysis of inconsistent data, and finally, a system for building 3D abstractions with wireframes. On the other hand, automatic systems help to detect or identify different kinds of patterns. It is applying to incomplete data analysis a retinal biometric approach based on crossing and bifurcation, an Arabic handwritten signature identification system, and finally, the use of clustering methods for gene expression data with RNA-seq. Note de contenu : 1. Stereoscopic Calculation Model Based on Fixational Eye Movements / Norio Tagawa
2. Visual Identification of Inconsistency in Pattern / Nwagwu Honour Chika, Ukekwe Emmanuel, Ugwoke Celestine, Ndoumbe Dora and George Okereke
3. Build 3D Abstractions with Wireframes / Roi Santos Mateos, Xose M. Pardo and Xose R. Fdez-Vidal
4. Incomplete Data Analysis / Bo-Wei Chen and Jia-Ching Wang
5. Retina Recognition Using Crossings and Bifurcations / Lukáš Semerád and Martin Drahanský
6. New Attributes Extraction System for Arabic Autograph as Genuine and Forged through a Classification Techniques / Anwar Yahya Ebrahim and Hoshang Kolivand
7. Current State-of-the-Art of Clustering Methods for Gene Expression Data with RNA-Seq / Ismail Jamail and Ahmed MoussaNuméro de notice : 26760 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.80151 Date de publication en ligne : 07/07/2021 En ligne : https://doi.org/10.5772/intechopen.80151 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99781
Titre : Artificial intelligence : Latest advances, new paradigms and novel applications Type de document : Monographie Auteurs : Eneko Osaba, Auteur ; Esther Villar-Rodriguez, Auteur ; Jesus L. Lobo, Auteur ; et al., Auteur Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 158 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-1-83962-389-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage profond
[Termes IGN] exploration de données
[Termes IGN] innovation
[Termes IGN] modèle orienté agent
[Termes IGN] organisme international
[Termes IGN] reconnaissance de formes
[Termes IGN] règlement
[Termes IGN] réseau neuronal artificiel
[Termes IGN] Spark
[Termes IGN] système d'informationRésumé : (éditeur) Artificial Intelligence (AI) is widely known as a knowledge field that aims to make computers, robots, or products that mimic the way humans think. In the current scientific community, AI is an intensively studied area composed of multiple branches. Historically, machine learning and optimization are two of the most studied fronts thanks to the development of novel and challenging research topics such as transfer optimization, swarm robotics, and drift detection and adaptation to evolving conditions in real-time. This book collects radically new theoretical insights, reporting recent developments and evincing innovative applications regarding AI methods in all fields of knowledge. It also presents works focused on new paradigms and novel branches of AI science. Note de contenu : 1- Introductory chapter: Artificial intelligence - Latest advances, new paradigms and novel applications
2- Big data framework using Spark architecture for dose optimization based on deep learning in medical imaging
3- Novelty detection methodology based on self-organizing maps for power quality monitoring
4- AI-powered workforce management and its future in India
5- Agent based load balancing in grid computing
6- A food recommender based on frequent sets of food mining using image recognition
7- The prospects for creating instruments for the coordination of activities of international organizations in the regulation of artificial intelligence
8- Artificial intelligence assisted innovation
9- Quest for I (intelligence) in AI (artificial intelligence): A non-elusive attemptNuméro de notice : 28633 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87770 En ligne : https://doi.org/10.5772/intechopen.87770 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99636 Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles / Abdelhak Loukkal (2021)
Titre : Deep convolutional neural networks for scene understanding and motion planning for self-driving vehicles Type de document : Thèse/HDR Auteurs : Abdelhak Loukkal, Auteur ; Yves Grandvalet, Directeur de thèse Editeur : Compiègne : Université de Technologie de Compiègne UTC Année de publication : 2021 Importance : 129 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse présentée pour l’obtention du grade de Docteur de l’UTC, spécialité InformatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] compréhension de l'image
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données multisource
[Termes IGN] navigation autonome
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal profond
[Termes IGN] segmentation sémantique
[Termes IGN] système de navigation
[Termes IGN] véhicule automobile
[Termes IGN] vision monoculaire
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) During this thesis, some perception approaches for self-driving vehicles were developed using de convolutional neural networks applied to monocular camera images and High-Definition map (HD-ma rasterized images. We focused on camera-only solutions instead of leveraging sensor fusion with rang sensors because cameras are the most cost-effective and discrete sensors. The objective was also to show th camera-based approaches can perform at par with LiDAR-based solutions on certain 3D vision tasks. Rea world data was used for training and evaluation of the developed approaches but simulation was als leveraged when annotated data was lacking or for safety reasons when evaluating driving capabilities. Cameras provide visual information in a projective space where the perspective effect does not preserve th distances homogeneity. Scene understanding tasks such as semantic segmentation are then often operated i the camera-view space and then projected to 3D using a precise depth sensor such as a LiDAR. Having thi scene understanding in the 3D space is useful because the vehicles evolve in the 3D world and the navigatio algorithms reason in this space. Our focus was then to leverage the geometric knowledge about the camer parameters and its position in the 3D world to develop an approach that allows scene understanding in the 3D space using only a monocular image as input. Neural networks have also proven to be useful for more than just perception and are more and more used fo the navigation and planning tasks that build on the perception outputs. Being able to output 3D scen understanding information from a monocular camera has also allowed us to explore the possibility of havin an end-to-end holistic neural network that takes a camera image as input, extracts intermediate semantic information in the 3D space and then lans the vehicle's trajectory. Note de contenu : 1. Introduction
1.1 General context
1.2 Framework and objectives
1.3 Organization and contributions of the thesis
2. Background and related work
2.1 Introduction
2.2 Autonomous driving perception datasets
2.3 Autonomous driving simulators
2.4 Semantic segmentation with CNNs
2.5 Monocular depth estimation with CNNs
2.6 Driving with imitation learning
2.7 Conclusion
3. Semantic segmentation using cartographic and depth maps
3.1 Introduction
3.2 Synthetic dataset
3.3 Proposed methods
3.4 Experiments
3.5 Conclusion
4. Disparity weighted loss for semantic segmentation
4.1 Introduction
4.2 Disparity weighting for semantic segmentation
4.3 Experiments
4.4 Conclusion
5. FlatMobileNet: Bird-Eye-View semantic masks from a monoc?ular camera
5.1 Introduction
5.2 Theoretical framework
5.3 FlatMobile network: footprint segmentation
5.4 Conclusion
6. Driving among flatmobiles
6.1 Introduction
6.2 Encoder-decoder LSTM for trajectory planning
6.3 Experimental evaluation
6.4 Conclusion
7. Conclusion
7.1 Contributions
7.2 PerspectivesNuméro de notice : 26769 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Compiègne : 2021 Organisme de stage : Heuristique et Diagnostic des Systèmes Complexes HeuDiaSyC nature-HAL : Thèse DOI : sans Date de publication en ligne : 25/10/2021 En ligne : https://tel.hal.science/tel-03402541/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99871 Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control / Florence Carton (2021)
Titre : Exploration of reinforcement learning algorithms for autonomous vehicle visual perception and control Titre original : Exploration des algorithmes d'apprentissage par renforcement pour la perception et le controle d'un véhicule autonome par vision Type de document : Thèse/HDR Auteurs : Florence Carton, Auteur ; David Filliat, Directeur de thèse Editeur : Paris : Ecole Nationale Supérieure des Techniques Avancées ENSTA Année de publication : 2021 Importance : 173 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l’Institut Polytechnique de Paris, Spécialité : Informatique, Données, IALangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage par renforcement
[Termes IGN] classification dirigée
[Termes IGN] instrument embarqué
[Termes IGN] navigation autonome
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal profond
[Termes IGN] robot mobile
[Termes IGN] segmentation sémantique
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Reinforcement learning is an approach to solve a sequential decision making problem. In this formalism, an autonomous agent interacts with an environment and receives rewards based on the decisions it makes. The goal of the agent is to maximize the total amount of rewards it receives. In the reinforcement learning paradigm, the agent learns by trial and error the policy (sequence of actions) that yields the best rewards.In this thesis, we focus on its application to the perception and control of an autonomous vehicle. To stay close to human driving, only the onboard camera is used as input sensor. We focus in particular on end-to-end training, i.e. a direct mapping between information from the environment and the action chosen by the agent. However, training end-to-end reinforcement learning for autonomous driving poses some challenges: the large dimensions of the state and action spaces as well as the instability and weakness of the reinforcement learning signal to train deep neural networks.The approaches we implemented are based on the use of semantic information (image segmentation). In particular, this work explores the joint training of semantic information and navigation.We show that these methods are promising and allow to overcome some limitations. On the one hand, combining segmentation supervised learning with navigation reinforcement learning improves the performance of the agent and its ability to generalize to an unknown environment. On the other hand, it enables to train an agent that will be more robust to unexpected events and able to make decisions limiting the risks.Experiments are conducted in simulation, and numerous comparisons with state of the art methods are made. Note de contenu : 1- Introduction
2- Supervised learning and reinforcement learning background
3- State of the art
4- End-to-end autonomous driving on circuit with reinforcement learning
5- From lane following to robust conditional driving
6- Exploration of methods to reduce overfit
7- ConclusionNuméro de notice : 28325 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Informatique, Données, IA : ENSTA : 2021 DOI : sans En ligne : https://tel.hal.science/tel-03273748/ Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98363 Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation / Roholah Yazdan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
[article]
Titre : Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation Type de document : Article/Communication Auteurs : Roholah Yazdan, Auteur ; Masood Varshosaz, Auteur Année de publication : 2021 Article en page(s) : pp 18 - 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'images
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] corrélation à l'aide de traits caractéristiques
[Termes IGN] corrélation croisée normalisée
[Termes IGN] couple stéréoscopique
[Termes IGN] détection automatique
[Termes IGN] modèle stéréoscopique
[Termes IGN] reconnaissance d'objets
[Termes IGN] segmentation d'image
[Termes IGN] SIFT (algorithme)
[Termes IGN] signalisation routière
[Termes IGN] SURF (algorithme)
[Termes IGN] Téhéran
[Termes IGN] transformation de Hough
[Termes IGN] zone urbaineRésumé : (auteur) Automatic detection and recognition of traffic signs have many applications. However, some problems can affect the accuracy of the existing algorithms, such as changes in environmental light conditions, shadows, the presence of objects of the same colour, significant changes in scale and rotation, as well as obstacles in front of the traffic signs. To overcome these difficulties, a reference image database is usually used that includes different modes of appearing the traffic signs in the images. In order to overcome the effects of scale and rotation, in this paper a new method is presented in which only one reference image is needed for each sign to recognise the traffic sign in an image. In the proposed method, imaging is done in stereo. Using the captured image pair, a virtual image is generated which is then used to recognise the sign. As a result, the recognition is carried out with a minimum number of reference images. Experiments show that the proposed algorithm significantly improves recognition results. The traffic signs are recognised with 93.1% accuracy that enjoys a 4.9% improvement over traditional methods. Numéro de notice : A2021-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.003 Date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96304
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