Courses


Another formative experience in our group includes courses on Vision Science, Machine Learning and Image Processing for the PhD students in the IPL. These courses are closely related to our research, and the lectures are typically given at our university (Remote sensing Master, and Electrical Engineering Master), as well as PhD and Master Programs in Vision Sciences (IOBA) and Computer Vision Master at Universitat Autònoma de Barcelona. Nevertheless, the courses below have readily evolved as a convenient introduction for PhD students to advanced issues not covered in conventional curricula.

Statistical Signal Processing (60 hours) - G. Camps-Valls
Material for a master course on (statistical) signal processing. I cover the essential background for engineers and physicists interested in signal processing: Probability and random variables, discrete time random processes, spectral estimation, signal decomposition and transforms, and an introduction to information theory.
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Representation of spatial information (30 hours) - J. Malo
Statistical regularities in photograpic images imply that certain representations of spatial information are better than others in terms of coding efficiency. In this course we present the information theory concepts (entropy, multi-information, correlation and negentropy) for unsupervised feature extraction or dictionary learning required in image coding. Redundancy in images and sequences is reviewed, and basic techniques for compact information representation are introduced such as vector quantization, predictive coding and transform coding. Application of these concepts in images are the basis of DCT and Wavelet representations which are the core of JPEG and JPEG2000.
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Color vision and Colorimetry (30 hours) - J. Malo
Color is a 5-dimensional perception that is not only related to the spectrum coming from an object, but also strongly related to its spatio-temporal context. It is a powerful feature that allow humans to make reliable inferences about objects that would be nice to understand and mimic in artificial vision. In this course we derive the linear CIE tristimulus theory from its experimental color matching foundations. We derive the relations between spectrum and tristimulus vectors through the color matching functions, the chromatic coordinates, chromatic purity and luminace. Phenomenology of color discrimination and adaptation reveal the limitations of the linear description and set the foundations of color appearance models. In addition, we link the above perceptual representations of color with the conventional representation of color in computers.
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Texture and motion in the visual cortex (40 hours) - J. Malo
Neurons in V1 and MT cortex play a determinant role in the analysis of the shape of objects, their spatial texture and the estimation of retinal motion. In this course we describe the basic psychophysical and physiological phenomena related to low-level spatio-temporal vision: the contrast sensitivity functions, masking, adaptation and aftereffects. These facts are mediated by the context-dependent nonlinearities of the response of neurons with specific receptive fields. We analyze the geometric properties of the standard model of V1 and their consequences in image discrimination. We introduce the concept of optical flow, its properties, and how this description of motion can be estimated from the 3D wavelet sensors in V1 and the aggregated sensors in MT.
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Kernel methods in machine learning (30 hours) - G. Camps-Valls
Two fundamental operations in Machine Learning such as regression and classification involve drawing nonlinear boundaries or functions through a set of (labled or unlabled) training samples. These boundaries or functions at certain (test) sample can be deduced from the similarities between the test sample and the training samples. These similarities can be encoded in Kernels and the representer theorem can be used to obtain expressions for the functions at any test sample. In this course we will also review the application of the kernelization of scalar products (e.g. as in the covariance matrix) to obtain nonlinear generalizations of classical feature extraction methods.
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Hyperspectral image processing (60 hours) - G. Camps-Valls
We introduce the main concepts of hyperspectral image processing. We start by a soft introduction to hyperspectral image processing, the standard processing chain and the current challenges in the field. Then we analyze the current state of the art in several topics: feature extraction, supervised classificaiton, unmixing and abundance estimation and retrieval of biophysical parameters. All the methods and techniques studied are reviewed both theoretically and through MATLAB exercises.
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Machine learning and signal processing for remote sensing data analysis (IGARSS'14 tutorial) - G. Camps-Valls and D. Tuia
In this tutorial, we will present the remote sensing image processing chain, and take the attendants on a tour of different strategies for feature extraction, classification, unmixing, retrieval, and pattern analysis for data understanding. On the one hand, we will present powerful methodologies for remote sensing data classification: extracting knowledge from data, including interactive approaches via active learning, classifiers that encode prior knowledge and invariances, semisupervised learning that exploit the information of unlabeled data, and domain adaptation to compensate for shifts in the ever-changing data distributions. On the other hand, we will pay attention to recent advances in bio-geophysical parameter estimation that incorporate heteroscedasticity, online adaptation, and problem understanding. From there we will take a leap towards the more challenging step of understanding the geoscience problems from data by reviewing the latest advances in (directed) graphical models, structure learning and empirical causal inference. Beyond theory, we will also present results of recent studies illustrating all the covered issues. Finally, we will provide code to the attendees to try the different methodologies and provide a solid ground for their future experimentations.
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The GLaSS training material builds on the global lakes use cases - Ana B. Ruescas & GLaSS team
The GLaSS training material builds on the global lakes use cases of GLaSS. It allows students and professionals in fields as aquatic ecology, environmental technology, remote sensing and GIS to learn about the possibilities of optical remote sensing of water quality, by using the Sentinel-2 and Sentinel-3 satellites and Landsat 8.
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Google Earth Engine introduction - Emma Izquierdo & Jordi Muñoz-Marí
A short introducttion to Google Earth Engine.
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