GIUV2015-260
The ISP research group, http://isp.uv.es, has a long tradition in statistical analysis of data coming from imaging systems. These measurements depend on the properties of the scenes and the physics of the imaging process, and their relevance depends on the (natural or artificial) observer that will analyze the data. Our distinct approach to signal, image and vision processing combines machine learning theory with the understanding of the underlying physics and biological vision. Applications mainly focus on optical remote sensing and computational visual neuroscience. Empirical statistical inference, also known as machine learning, is a field of computer science interested in making predictions, and models from observations and sensory data. The information processing tools in machine learning are critical to understand the function of natural neural networks involved in biological vision, as well as to make inferences in complex dynamic network systems, such as the Earth biosphere, atmosphere, and ecosystems. Problems in Visual Neuroscience and in Remote Sensing based geosciences require similar mathematical tools. For example, both scientific fields face model inversion and...The ISP research group, http://isp.uv.es, has a long tradition in statistical analysis of data coming from imaging systems. These measurements depend on the properties of the scenes and the physics of the imaging process, and their relevance depends on the (natural or artificial) observer that will analyze the data. Our distinct approach to signal, image and vision processing combines machine learning theory with the understanding of the underlying physics and biological vision. Applications mainly focus on optical remote sensing and computational visual neuroscience. Empirical statistical inference, also known as machine learning, is a field of computer science interested in making predictions, and models from observations and sensory data. The information processing tools in machine learning are critical to understand the function of natural neural networks involved in biological vision, as well as to make inferences in complex dynamic network systems, such as the Earth biosphere, atmosphere, and ecosystems. Problems in Visual Neuroscience and in Remote Sensing based geosciences require similar mathematical tools. For example, both scientific fields face model inversion and model understanding problems. In both cases, one has a complex forward model that is difficult to invert (to extract information from) either because it is not analytically invertible (undetermined) or because the measurements (or responses) are noisy in nature. In Remote Sensing, the forward model is the imaging process given certain state conditions in the surface and atmosphere. In Visual Neuroscience, the forward model includes what is known in the neural pathway from the retina to the different regions of the visual cortex. Inversion of such models is key to make quantitative and meaningful inferences about the underlying system that generated the observed data. Beyond such quantitative assessment, a qualitative interpretation of the proposed models is mandatory as well. Qualitative understanding is more challenging than prediction, and causal inference from empirical data is the common playground both in geoscience and neuroscience. Simultaneous observations and recordings from a phenomenon lead to multidimensional signals that may display strong statistical correlation between the components. However, correlation is not enough to establish cause-effect relationships. This is key when analyzing activation and inhibition in the communication between different brain regions, and it is also of paramount relevance when studying the causes, effects and confounders of essential climate variables for detection and attribution in climate science. Finally, another parallelism is the analysis of big visual data: hyperspectral imagery acquired by current and upcoming satellite sensors pose a big-data information processing problem in similar ways to that in the visual brain. Adaptation, pattern recognition, inference and decision making in the brain may be quite inspiring for remote sensing image analysis. The group is therefore organized into a theoretical research branch (A) and a more applied research branch (B). The theoretical machine learning core tackles model inversion, interpretation, causal inference from empirical data and inclusion of physical constraints and prior knowledge in big visual data. The applied research lines are devoted to apply and adapt the theoretrical developments for remote sensing, geociences and visual neuroscience. For the sake of simplicity, we have grouped together these activities along five conceptual research lines: machine learning, visual neuroscience, image processing, remote sensing and big data processing.
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- Desenvolupament d'algorismes d'aprenentatge automatic
- Desenvolupament de models estadistics de neurociencia visual
- Aplicacions en processament d'imatges
- Aplicacions en teledeteccio i geociencies
- Machine learning.Development of statistical learning techniques: neural networks, graphic models, kernel machines, classification techniques, regression, grouping and visualisation (manifold learning), active, semi-supervised, relational, Bayesian, structured, and causal learning.
- Visual neuroscience.Development of models and techniques for data processing in visual neuroscience: manifold learning, independence, statistical techniques of optimal coding, gaussianisation, learning in varieties, estimation and inversion of models, interpretability and causality.
- Teledección y geociencias.Aplicaciones en tratamiento de señales e imágenes de teledetección: estimación de parámetros biofísicos y variables de flujos, inversión de modelos, segmentación de imágenes, detección de cambios y anomalías, fusión de imágenes y multiresolución, restauración, causalidad y atribución, ranking.
- Remote sensing and geosciences.Applications in signal processing and remote sensing images: estimation of biophysical parameters and flow variables, model inversion, image segmentation, detection of changes and anomalies, image fusion and multi-resolution, restoration, causality and attribution and ranking.
- Big data processing.Processing of large databases and high temporal, spatial and spectral resolution images. Our collaborators (ESA, NASA, EUMETSAT, Google, DigitalGlobe) provide access to large volumes of data to be processed in real time through parallelisation techniques, clusters, and algorithms.
| Name | Nature of participation | Entity | Description |
|---|---|---|---|
| GUSTAU CAMPS VALLS | Director | Universitat de València | |
| Research team | |||
| JAVIER CALPE MARAVILLA | Member | Universitat de València | |
| JESUS MALO LOPEZ | Member | Universitat de València | |
| JORDI MUÑOZ MARI | Member | Universitat de València | |
| LUIS GOMEZ CHOVA | Member | Universitat de València | |
| JULIA CARMEN AMOROS LOPEZ | Member | Universitat de València | |
| ROBERTO FERNANDEZ MORAN | Member | Universitat de València | |
| VALERO LAPARRA PEREZ-MUELAS | Member | Universitat de València | |
| ANA BELEN RUESCAS ORIENT | Member | Universitat de València | |
| ADRIAN PEREZ SUAY | Member | Universitat de València | |
| MARIA PILES GUILLEM | Member | Universitat de València | |
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- ERI Image Processing Laboratory (IPL)
- Aprendizaje automático; clasificación; regresión; agrupamiento; inteligencia artificial; aprendizaje Bayesiano
- V1; codificación óptima: gaussianización; normalización; wavelets; ICA; aprendizaje en variedades; estimación; inversió
- Image distortion metrics; image coding; motion estimation; video coding; image restoration; color representation; visual models; saliency
- Estimación de parámetros; inversión de modelos; segmentación de imágenes; detección de cambios; anomalías; fusión; atribución
- Procesado Paralelo; Hadoop; Spark; Paralelización; Clusters; Procesado Distribuido
