Machine Learning and image processingy
Machine Learning is the branch of artificial intelligence whose objective is the development of techniques that allow computers to learn from data. This research field is undergoing an extraordinary revolution and popularization thanks to a family of techniques called Deep Learning. These are computational models composed of numerous processing layers, and used to learn representations with multiple levels of abstraction from input data. These methods, during the last decade, have drastically improved the results provided by existing techniques in tasks such as speech recognition, machine translation and image classification, and have directly exceeded human capacity in complex activities (such as winning human champions playing Go). Among all the applications of deep learning, there is one that is particularly noteworthy for the large amount of existing work and the amazing results obtained: computer vision and image processing. In fact, one of the most effective forms of deep learning (convolutional neural networks) has largely surpassed pre-existing techniques in object detection and face recognition, just to name two examples.
The main goal of this research line is to create new theoretical and practical developments in the field of image analysis through machine learning techniques. From a practical point of view, fully connected, convolutional, recurrent and generative adversarial networks, among others, are employed to solve complex real-world problems. Application areas include medical diagnosis, forensic identification, metallurgical industry, and human-robot interaction, just to name a few. From the theoretical and methodological point of view, the aim is to study the properties and empirical performance of existing models, as well as to analyze, design, implement and validate new learning approaches that can make a novel and original contribution to the state of the art.
Responsible: Pablo Mesejo Santiago
|Ortíz García, Andrésemail@example.comL6AgDlenrpMma.es||DaSCI Technology Applications Area||PhD|
|Salas González, Diego||dsalas4DaH5wG.dwI_@ugr.es||DaSCI Technology Applications Area||PhD|
|Martínez Murcia, Francisco Jesús||fjesusmartinez@uBRzzSFHfugr.es||DaSCI Technology Applications Area||PhD - Juan de la Cierva|
|Segovia Román, Fermínfirstname.lastname@example.orgJB_hvFYCes||DaSCI Technology Applications Area||PhD|
|Górriz Sáez, Juan Manuel||gorriz@BjAE@Kf3ugr.es||DaSCI Technology Applications Area||PhD|
|Ramírez Pérez de Inestrosa, Javier||javierrp@ugraxnNANrXaJDy.es||DaSCI Technology Applications Area||PhD|
|Mesejo Santiago, Pablo||pablomesejo@gB98UCQ1Phmail.com||DaSCI Technology Applications Area||PhD - Marie Curie|
|Villar Castro, Pedroemail@example.comLOG0puses||Data Science and Big Data Area||PhD|
|Gutiérrez Salcedo, Salvador||salvador.gutierrez@ucabgGxI4lbK.es||Data Science and Big Data Area||PhD|