One of the key problems in computer vision is adaptation: models are too rigid to follow the variability of the inputs. The canonical computation that explains adaptation in sensory neuroscience is divisive normalization, and it has appealing effects on image manifolds. In this work we show that including divisive normalization in current deep networks makes them more invariant to non-informative changes in the images. In particular, we illustrate this concept in U-Net architectures for image segmentation.

This project is joint work by Clement Gorin, Andre Groeger, Arogya Koyrala and Hannes Mueller. It builds on previous work of the authors using satellite images and Deep Learning for automated detection of war-related building destruction in Syria. Satellite imagery is becoming ubiquitous and is released with ever higher frequency. Research has demonstrated that Deep Learning applied to satellite imagery holds promise for automated detection of war-related building destruction.

The detection of gravitational waves from the coalescence of compact binaries formed by black holes and/or neutron stars strongly relies on accurate waveform templates to conduct searches based on matched filtering.

In the last decades, our society has become more interdependent and complex than ever before. Local impacts can cause global issues, as the current pandemic clearly shows, affecting the health of millions of human beings. It is also highly dependent on relevant technological structures, such as communications, transport, or power distribution networks, which can be very vulnerable to the effects of Space Weather.

The SICAPTOR 2.0 project intends to redesign the iObserver electronic monitoring (REM) system in order to minimize its size and maximize its performance. This device takes pictures of all fishes passing through the conveyor belt in the fishing park, and identifies in real time the species to which each of the specimens belongs in each of the photographs, making also an estimate of height and weight of each individual and of the total amount of captured biomass. To attain these objectives, we use a set of deep learning algorithms specifically developed.

165 newborns die every day of Bacterial Meningitis (BM), an aggressive infection that leaves severe sequelae among 30% of survivors. Rapid detection, particularly in this age group, is difficult due to the little specificity and overlap of its symptoms with those of more common and less severe diseases. Current strategy to improve prognosis is the prompt antibiotic treatment after an early diagnosis by means of a lumbar puncture (LP), invasive and potentially harmful procedure.

Convolutional Neural Networks (CNN) are currently being implemented in a wide variety of applications. This subdomain of Artificial Intelligence shows a powerful performance in machine vision applications and may be used to categorise and classify objects, amongst other image processing tasks.

In the Artificial Perception Group of the Centre for Automation and Robotics (CAR) we are interested in identifying and classifying weed species within crop fields, which is a very specific problem, as the system will only need to process images of soil and plants.

The ATLAS experiment at the Large Hadron Collider (LHC) is looking beyond the Standard Model of particle physics, searching for signs of unknown new physics. An important aspect to be able to find this new physics is the identification of the interesting events within all the events available. Interesting events are called “signal”, while others are “background”. Individuating these signal events, which are indeed extremely rare, is a really challenging task. The LHC has delivered billions of collisions which have been recorded by the ATLAS detector.

Since the dawn of humanity, our species has tried to decipher the world around us, through art, literature, music or science.  The skills developed and the tools used are different, as different as the audiences targeted.  But the goals are basically the same: to dealing with complexity using the tools at hand.

The objective of the project is to advance in the integration of 3D models of the body in the development of digital products and applications, developing innovative tools that allow their 3D and / or 4D analysis for the clothing, health and wellness, audiovisual and orthopedic sectors, or any other sector that is interested in incorporating digital information from users.