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This work presents a full generic approach to the detection and
recognition of traffic signs. The approach is based on the latest
computer vision methods for object detection, and on powerful
methods for multiclass classification. The challenge was to
robustly detect a set of different sign classes in real time, and
to classify each detected sign into a large, extensible set of
classes. To address this challenge, several state-of-the-art
methods were developed that can be used for different recognition
problems. Following an introduction to the problems of traffic sign
detection and categorization, the text focuses on the problem of
detection, and presents recent developments in this field. The text
then surveys a specific methodology for the problem of traffic sign
categorization - Error-Correcting Output Codes - and presents
several algorithms, performing experimental validation on a mobile
mapping application. The work ends with a discussion on future
research and continuing challenges.
The problem of dealing with missing or incomplete data in machine
learning and computer vision arises in many applications. Recent
strategies make use of generative models to impute missing or
corrupted data. Advances in computer vision using deep generative
models have found applications in image/video processing, such as
denoising, restoration, super-resolution, or inpainting. Inpainting
and Denoising Challenges comprises recent efforts dealing with
image and video inpainting tasks. This includes winning solutions
to the ChaLearn Looking at People inpainting and denoising
challenges: human pose recovery, video de-captioning and
fingerprint restoration. This volume starts with a wide review on
image denoising, retracing and comparing various methods from the
pioneer signal processing methods, to machine learning approaches
with sparse and low-rank models, and recent deep learning
architectures with autoencoders and variants. The following
chapters present results from the Challenge, including three
competition tasks at WCCI and ECML 2018. The top best approaches
submitted by participants are described, showing interesting
contributions and innovating methods. The last two chapters propose
novel contributions and highlight new applications that benefit
from image/video inpainting.
The problem of dealing with missing or incomplete data in machine
learning and computer vision arises in many applications. Recent
strategies make use of generative models to impute missing or
corrupted data. Advances in computer vision using deep generative
models have found applications in image/video processing, such as
denoising, restoration, super-resolution, or inpainting. Inpainting
and Denoising Challenges comprises recent efforts dealing with
image and video inpainting tasks. This includes winning solutions
to the ChaLearn Looking at People inpainting and denoising
challenges: human pose recovery, video de-captioning and
fingerprint restoration. This volume starts with a wide review on
image denoising, retracing and comparing various methods from the
pioneer signal processing methods, to machine learning approaches
with sparse and low-rank models, and recent deep learning
architectures with autoencoders and variants. The following
chapters present results from the Challenge, including three
competition tasks at WCCI and ECML 2018. The top best approaches
submitted by participants are described, showing interesting
contributions and innovating methods. The last two chapters propose
novel contributions and highlight new applications that benefit
from image/video inpainting.
Documento del ano 2011 en eltema Historia Europa - Otros paises -
Tiempos modernos, absolutismo, industrializacion, Nota: -,
Universitat de Barcelona, Idioma: Espanol, Resumen: Resumen:
Presentamos a continuacion, entrelazando con otros estudios
dedicados a la historiografia barroca catalana y maltesa, una breve
relacion y estudio de los principales autores de la historiografia
maltesa de la Edad Moderna, relacionandolos con otros autores
europeos de la epoca que escribieron sobre Malta y su gente.
Logicamente, sobresale la figura de Gian Francesco Abela
(1582-1655), considerado el padre de la historiografia maltesa,
personaje clave para poder enmarcar su obra en el conjunto de la
historiografia barroca y la cultura historica maltesa y para
estudiar tambien su vision de los catalanes y aragoneses, a los que
dedica varias paginas de su obra. ------ Abstract: We present,
interlacing with other studies dedicated to Catalan and maltese
baroque historiography, a brief relation and study of the main
authors of the maltese historiography in the Early Modern Age,
relating them to other European authors of the time who wrote on
Malta and its people. Logically, it excels the figure of Gian
Francesco Abela (1582-1655), considered the father of the maltese
historiography, a very important figure able to frame its work in
the set of the baroque historiography and the maltese historical
culture and also study its vision of Catalans and the Aragonese
ones, to which it dedicates several pages of its work.
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