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.

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.

This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: * Evaluation and Generalization in Interpretable Machine Learning * Explanation Methods in Deep Learning * Learning Functional Causal Models with Generative Neural Networks * Learning Interpreatable Rules for Multi-Label Classification * Structuring Neural Networks for More Explainable Predictions * Generating Post Hoc Rationales of Deep Visual Classification Decisions * Ensembling Visual Explanations * Explainable Deep Driving by Visualizing Causal Attention * Interdisciplinary Perspective on Algorithmic Job Candidate Search * Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions * Inherent Explainability Pattern Theory-based Video Event Interpretations

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.