DataScience@UB designs and prototype a machine that includes an intelligent vision system based on semantic segmentation through convolutional neural networks to automate the classification of leather.
Estimate the aesthetic quality of images in order to predict their impact
Image search engines receive huge volumes of images every day, but very few of these have any impact on the internet. Of course, the aesthetics and technique of images are key to ensuring they have an impact on the internet and are not overlooked. The challenge of this project is therefore to develop tools for analysing and predicting the technical and aesthetic quality of images.
An international company and GCD@UB are working on the design of a system to estimate the technical and aesthetic quality of images. This project is being carried out using deep learning networks and large volumes of data with tags related to aesthetics.
Advertising and marketing companies, as well as individual users, are constantly seeking the best images for their projects. Automatically estimating the quality of an image and predicting its impact can help us make decisions about marketing and the economic value of images.
Thanks to the emerging technologies of e-Learning, the analysis of data offers professionals in the education sector, advantages with the ability to impact and revolutionize in the future the way we analyze and evaluate the learning experience.
Within the framework of the Cartographic Plan of Catalonia, the Cartographic and Geological Institute of Catalonia is working on the elaboration of the technical specifications of the land covers Map of Catalonia. The ICGC has commissioned us a study to explore the viability of the application of deep learning techniques in the generation of maps.