University of Valencia logo Logo Scientific Technological Offer Logo del portal

Research Group on Image and Signal Processing - ISP

Description

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.

Goals CT

 

  • Development of machine learning algorithms.
  • Development of statistical models of visual neuroscience.
  • Image processing applications.
  • Remote sensing and geosciences applications.
Research lines
  • Aprenentatge automàtic

    Desenvolupament de tècniques d'aprenentatge estadístic: xarxes neuronals, models gràfics, màquines kernel, tècniques de classificació, regressió, agrupament i visualització (manifold learning), aprenentatge actiu, semisupervisat, relacional, Bayesià, estructurat, i causal.

  • Neurociència Visual

    Desenvolupament de models i tècniques per al processament de dades en neurociència visual: manifold learning, independització, tècniques estadístiques de codificació òptima, de gaussianització, aprenentatge en varietats, estimació i inversió de models, interpretabilitat i causalitat.

  • Processament d'imatges

    Desenvolupament de models i tècniques per al processament de dades en neurociència visual: manifold learning, independització, tècniques estadístiques de codificació òptima, de gaussianització, aprenentatge en varietats, estimació i inversió de models, interpretabilitat i causalitat.

  • Processament de gran volum de dades

    Processament de grans bases de dades i imatges d'alta resolució temporal, espacial i espectral. Els nostres col·laboradors (ESA, NASA, EUMETSAT, Google, DigitalGlobe) proporcionen accés a grans volums de dades a processar en temps real mitjançant tècniques de paral·lelització, clústers, i algorismes.

  • Teledetecció i geociència

    Aplicacions en tractament de senyals i imatges de teledetecció: estimació de paràmetres biofísics i variables de fluxos, inversió de models, segmentació d'imatges, detecció de canvis i anomalies, fusió d'imatges i multiresolució, restauració, causalitat i atribució, rànquing.

Management
  • CAMPS VALLS, GUSTAU
  • PDI-Catedratic/a d'Universitat
View details
Members
  • AMOROS LOPEZ, JULIA CARMEN
  • Alumn.-Servei de Formacio Permanent
View details
  • CALPE MARAVILLA, JAVIER
  • PDI-Titular d'Universitat
View details
  • GOMEZ CHOVA, LUIS
  • Alumn.-Servei de Formacio Permanent
  • Coordinador/a de Mobilitat
View details
  • MALO LOPEZ, JESUS
  • PDI-Catedratic/a d'Universitat
View details
  • MUÑOZ MARI, JORDI
  • PDI-Titular d'Universitat
View details
  • PILES GUILLEM, MARIA
  • PDI-Contractat/Da Doctor/A
View details
Scientific production by UV researcher
  • CAMPS VALLS, GUSTAU
    PDI-Catedratic/a d'Universitat
    Expandir
  • PILES GUILLEM, MARIA
    PDI-Contractat/Da Doctor/A
    Expandir
Associated structure
Image Processing Laboratory (IPL)
Contact group details
Image and Signal Processing Group (ISP)

Burjassot/Paterna Campus

Science Park
C/ Catedrático José Beltrán n°2

46980 Paterna (Valencia)

+34 963 543 229

Geolocation

isp.uv.es

gustau.camps@uv.es

Contact people
  • CAMPS VALLS, GUSTAU
  • PDI-Catedratic/a d'Universitat
View details