RP-En

In recent years, several deep learning techniques have been applied to the problem of seismic facies segmentation. However, there is a lack of an authoritative protocol for evaluating such models, so that the comparison between results becomes compromised.

This project aims to enhance people observation through robust, discriminative approaches to minimize inaccuracies and address advanced tasks like activity recognition. This enables the development of automatic environment monitoring applications.

DeepEyes aims to develop algorithmic visual computing and machine intelligence solutions for problems related to forensic computing, digital security and electronic surveillance.

DET proposes the development of methods for pedestrian detection with the aim of reducing the computational cost and maintaining the accuracy obtained by detectors that obtain good results but have a high computational cost.

This article addresses the critical challenge of maintaining boundary precision and structural continuity in complex geological settings. By leveraging distance transforms, DTL delivers more geologically coherent results across a range of seismic datasets and deep learning architectures.

Recent advances in AI music (AIM) generation services are currently transforming the music industry. Given these advances, understanding how humans perceive AIM is crucial both to educate users on identifying AIM songs, and, conversely, to improve current models.

GigaFrames main objective is to implement and consolidate an emerging nucleus characterized by the creation of a line of research called Surveillance and Forensic Computing.

The objective of Har-Health project is the research and development of methods and algorithms capable of automatically recognizing human activities related to chronic diseases (diabetes, hypertension, obesity and aging) based on visual information, signals captured by sensors on personal mobile devices and signals captured by sensors installed in environments.

Research and development of solutions within the scope of sensor analytics in order to incorporate automatic methods for processing sensory data collected by trackers installed in motor vehicles.

In this work, a review of the well log data imputation literature was presented, together with the proposal of a benchmark for the evaluation of imputation methods, motivated by a gap in the literature with respect to consistent and standardized protocols for the comparison between different approaches.