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Angel Sappa; Fadi Dornaika |
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Title |
An Edge-Based Approach to Motion Detection |
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2006 |
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6th International Conference on Computational Science (ICCS´06), LNCS 3991:563–570 |
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Reading (United Kingdom) |
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ADAS |
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ADAS @ adas @ SaD2006 |
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654 |
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Author |
Katerine Diaz; Francesc J. Ferri |
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Title |
Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes |
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2013 |
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Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes |
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Los métodos basados en subespacios son una herramienta muy utilizada en aplicaciones de visión por computador. Aquí se presentan y validan algunos algoritmos que hemos propuesto en este campo de investigación. El primer algoritmo está relacionado con una extensión del método de vectores comunes discriminantes con kernel, que reinterpreta el espacio nulo de la matriz de dispersión intra-clase del conjunto de entrenamiento para obtener las características discriminantes. Dentro de los métodos basados en subespacios existen diferentes tipos de entrenamiento. Uno de los más populares, pero no por ello uno de los más eficientes, es el aprendizaje por lotes. En este tipo de aprendizaje, todas las muestras del conjunto de entrenamiento tienen que estar disponibles desde el inicio. De este modo, cuando nuevas muestras se ponen a disposición del algoritmo, el sistema tiene que ser reentrenado de nuevo desde cero. Una alternativa a este tipo de entrenamiento es el aprendizaje incremental. Aquí se proponen diferentes algoritmos incrementales del método de vectores comunes discriminantes. |
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978-3-639-55339-0 |
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Admin @ si @ DiF2013 |
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2440 |
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Author |
Antonio Lopez; David Vazquez; Gabriel Villalonga |
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Title |
Data for Training Models, Domain Adaptation |
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Book Chapter |
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Year |
2018 |
Publication |
Intelligent Vehicles. Enabling Technologies and Future Developments |
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395–436 |
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Driving simulator; hardware; software; interface; traffic simulation; macroscopic simulation; microscopic simulation; virtual data; training data |
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Simulation can enable several developments in the field of intelligent vehicles. This chapter is divided into three main subsections. The first one deals with driving simulators. The continuous improvement of hardware performance is a well-known fact that is allowing the development of more complex driving simulators. The immersion in the simulation scene is increased by high fidelity feedback to the driver. In the second subsection, traffic simulation is explained as well as how it can be used for intelligent transport systems. Finally, it is rather clear that sensor-based perception and action must be based on data-driven algorithms. Simulation could provide data to train and test algorithms that are afterwards implemented in vehicles. These tools are explained in the third subsection. |
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ADAS; 600.118 |
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Admin @ si @ LVV2018 |
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3047 |
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Author |
Jose M. Armingol; Jorge Alfonso; Nourdine Aliane; Miguel Clavijo; Sergio Campos-Cordobes; Arturo de la Escalera; Javier del Ser; Javier Fernandez; Fernando Garcia; Felipe Jimenez; Antonio Lopez; Mario Mata |
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Title |
Environmental Perception for Intelligent Vehicles |
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2018 |
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Intelligent Vehicles. Enabling Technologies and Future Developments |
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23–101 |
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Computer vision; laser techniques; data fusion; advanced driver assistance systems; traffic monitoring systems; intelligent vehicles |
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Environmental perception represents, because of its complexity, a challenge for Intelligent Transport Systems due to the great variety of situations and different elements that can happen in road environments and that must be faced by these systems. In connection with this, so far there are a variety of solutions as regards sensors and methods, so the results of precision, complexity, cost, or computational load obtained by these works are different. In this chapter some systems based on computer vision and laser techniques are presented. Fusion methods are also introduced in order to provide advanced and reliable perception systems. |
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ADAS; 600.118 |
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Admin @ si @AAA2018 |
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3046 |
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German Ros; Laura Sellart; Gabriel Villalonga; Elias Maidanik; Francisco Molero; Marc Garcia; Adriana Cedeño; Francisco Perez; Didier Ramirez; Eduardo Escobar; Jose Luis Gomez; David Vazquez; Antonio Lopez |
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Title |
Semantic Segmentation of Urban Scenes via Domain Adaptation of SYNTHIA |
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Book Chapter |
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2017 |
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Domain Adaptation in Computer Vision Applications |
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12 |
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227-241 |
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SYNTHIA; Virtual worlds; Autonomous Driving |
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Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. Recent revolutionary results of deep convolutional neural networks (DCNNs) foreshadow the advent of reliable classifiers to perform such visual tasks. However, DCNNs require learning of many parameters from raw images; thus, having a sufficient amount of diverse images with class annotations is needed. These annotations are obtained via cumbersome, human labour which is particularly challenging for semantic segmentation since pixel-level annotations are required. In this chapter, we propose to use a combination of a virtual world to automatically generate realistic synthetic images with pixel-level annotations, and domain adaptation to transfer the models learnt to correctly operate in real scenarios. We address the question of how useful synthetic data can be for semantic segmentation – in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic collection of diverse urban images, named SYNTHIA, with automatically generated class annotations and object identifiers. We use SYNTHIA in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments with DCNNs that show that combining SYNTHIA with simple domain adaptation techniques in the training stage significantly improves performance on semantic segmentation. |
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Springer |
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Gabriela Csurka |
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ADAS; 600.085; 600.082; 600.076; 600.118 |
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ADAS @ adas @ RSV2017 |
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2882 |
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Author |
Antonio Lopez; W. Niessen; Joan Serrat; K. Nikolay; B. Ter Haar Romeny; Juan J. Villanueva; M. Viergerver |
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New improvements in the multiscale analysis of trabecular bone patterns |
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2000 |
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Pattern Recognition and Applications |
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251-260 |
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IOS Press |
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ADAS |
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Admin @ si @ |
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3418 |
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Author |
Aura Hernandez-Sabate; Debora Gil |
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Title |
The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries |
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2012 |
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Intravascular Ultrasound |
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185-206 |
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Intech |
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Yasuhiro Honda |
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English |
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english |
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978-953-307-900-4 |
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IAM; ADAS |
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IAM @ iam @ HeG2012 |
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1684 |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez |
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Title |
Stereo Vision Camera Pose Estimation for On-Board Applications |
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2007 |
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Scene Reconstruction, Pose Estimation and Traking |
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39-50 |
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Rustam Stolking |
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978-3-902613-06-6 |
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ADAS @ adas @ SGD2007 |
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797 |
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David Geronimo; Angel Sappa; Antonio Lopez |
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Title |
Stereo-based Candidate Generation for Pedestrian Protection Systems |
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2010 |
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Binocular Vision: Development, Depth Perception and Disorders |
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189–208 |
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Pedestrian Detection |
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This chapter describes a stereo-based algorithm that provides candidate image windows to a latter 2D classification stage in an on-board pedestrian detection system. The proposed algorithm, which consists of three stages, is based on the use of both stereo imaging and scene prior knowledge (i.e., pedestrians are on the ground) to reduce the candidate searching space. First, a successful road surface fitting algorithm provides estimates on the relative ground-camera pose. This stage directs the search toward the road area thus avoiding irrelevant regions like the sky. Then, three different schemes are used to scan the estimated road surface with pedestrian-sized windows: (a) uniformly distributed through the road surface (3D); (b) uniformly distributed through the image (2D); (c) not uniformly distributed but according to a quadratic function (combined 2D-3D). Finally, the set of candidate windows is reduced by analyzing their 3D content. Experimental results of the proposed algorithm, together with statistics of searching space reduction are provided. |
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NOVA Publishers |
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ADAS @ adas @ GSL2010 |
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1301 |
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Author |
David Geronimo; David Vazquez; Arturo de la Escalera |
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Title |
Vision-Based Advanced Driver Assistance Systems |
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2017 |
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Computer Vision in Vehicle Technology: Land, Sea, and Air |
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ADAS; Autonomous Driving |
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ADAS; 600.118 |
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ADAS @ adas @ GVE2017 |
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2881 |
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