Marie Skłodowska-Curie Actions

MSCA-IF: Joint application at the University of Granada. Department of Optics.

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    01/07/2020 10:00 - Europe/Brussels
    H2020 / Marie Skłodowska-Curie Actions
    Spain, Granada
    International Research Projects Office
    Promotion and Advisory Unit

Professor Juan Luis Nieves Gómez, from the Department of Optics at the University of Granada, welcomes postdoctoral candidates interested in applying for a Marie Skłodowska-Curie Individual Fellowships (MSCA-IF) in 2020 at this University. Please note that applicants must comply with the Mobility Rule (more information: http://sl.ugr.es/0aNV).

Brief description of the institution:

The University of Granada (UGR), founded in 1531, is one of the largest and most important universities in Spain. The UGR has been awarded with the "Human Resources Excellence in Research (HRS4R)", which reflects the UGR’s commitment to continuously improve its human resource policies in line with the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers. The UGR is also a leading institution in research, located in the top 5/10 of Spanish universities by a variety of ranking criteria, such as national R&D projects, fellowships awarded, publications, or international funding.

UGR is one of the few Spanish Universities listed in the Shanghai Top 500 ranking (http://sl.ugr.es/0aw0). The Academic Ranking of World Universities (ARWU) places the UGR in 268th position in the world and as the 4th highest ranked University in Spain, reaffirming its position as an institution at the forefront of national and international research. From the perspective of specialist areas in the ARWU rankings, the UGR is outstanding in Documentation (ranked in the 36th in the world) or Food science technology (ranked 37th in the world), Mathematics and Computer Science (ranked among the top 76-100 in the world).

The UGR has 4 researchers at the top of the Highly Cited Researchers (HCR) list in the Computer Science area. With regard to broader subject fields, the UGR is ranked in 45th position in the universities worldwide in the discipline of Engineering. It is also well recognized for its web presence (http://sl.ugr.es/0a6i) taking 36th place in the top 200 Universities in Europe. Internationally, we bet decidedly by our participation in the calls of the Framework Programme of the European Union. For the duration of the last two Framework Programmes, the UGR has obtained a total of 66 projects, with total funding of 18.02 million euros, and for H2020, 80 projects with total funding around 20.6 million euros.

Brief description of the Centre/Research Group

The Color Imaging Lab belongs to the Optics Department at the University of Granada (ranked among the best 300 universities in the world and the second in Spain, according to the Shanghai ranking 2018).

In this department we have carried out research into both classical Colorimetry (e.g. color differences) and Color Vision (e.g. chromatic discrimination) since the beginning of the 1970s. In the 1990s we became interested in both human and computational color constancy. From its origin in 2000 the Color Imaging Lab has focused on the spectrum based color research and novel methods for spectral data analysis and measurement. Our current research topics are: spectral imaging, spectral estimation algorithms, high dynamic range imaging, de-weathering algorithms, human perception, color vision, visual saliency, polarimetric&spectral imaging, new spectral sensors.

In the research group we have four senior permanent staff and several Ph.D. and Post-doc students. Our laboratory is well-equipped for spectral color research with several spectral cameras (hyperspectral and multispectral) and spectroradiometers as well as eye-trackers and a thermal camera. Our university has also the facility to use color and spectral cameras on drones.

Our group is a consortium member of the and Erasmus+ COSI-master programmes. We are very active in international collaboration (i.e. half of our papers have international co-authorship). In research, we have project funding from the University of Granada, the Regional Government of Andalucia, the Spanish Government and from industrial partners.

Project description

Different linear decision strategies for estimation of relevant colors that describe the color palette of paintings

In a previous research project we heuristically introduced the so-called “relevant colors” in a painting to describe the number of colors that would stand out for an observer when just glancing at a painting. These relevant colors would characterize the color palette of a scene as being obtained on the basis of the discernible colors that were colorimetrically different within the scene. We computationally obtained a number of 18 relevant colors, which were representative on average of the color content of every image. We increased the dimensions of the cubes in which we divide the CIELAB color space, and we established a dimension while also setting as the parameter the minimum threshold of colors in each cube, i.e., the minimum percentage of colors compared to the total that should be within the cube.

The purpose of this work is to apply linear Support Vector Machine (SVM) strategies to estimate the relevant colors describing a painting. SVM is one of the sophisticated supervised machine learning algorithms that can be applied for both classification and regression problems. So different strategies will be analyzed to improve the way we have taken the division of the space into regions in every CIELAB direction. This research project will focus also on how to connect those SVM approaches with a subjective determination of the relevant colors found in different paintings. A public paintings image-database will be used to test and run all experiments.

Research Area

Information Science and Engineering (ENG), Life Sciences (LIFE) and Physics (PHY).

For a correct evaluation of your candidature, please send the documents below to Professor Juan Luis Nieves Gómez (jnieves@ugr.es):

  • CV
  • Letter of recommendation (optional)


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