On May 7th at 12:00, Room B-221 (ETSIT)

 

Research  

 

GTI Data   

 

Open databases created and software developed by the GTI and supplemental material to papers.  

 

Databases  

SportCLIP (2025): Multi-sport dataset for text-guided video summarization.
Ficosa (2024): The FNTVD dataset has been generated using the Ficosa's recording car.
MATDAT (2023):  More than 90K labeled images of martial arts tricking.
SEAW – DATASET (2022): 3 stereoscopic contents in 4K resolution at 30 fps.
UPM-GTI-Face dataset (2022): 11 different subjects captured in 4K, under 2 scenarios, and 2 face mask conditions.
LaSoDa (2022): 60 annotated images from soccer matches in five stadiums with different characteristics and light conditions.
PIROPO Database (2021):People in Indoor ROoms with Perspective and Omnidirectional cameras.
EVENT-CLASS (2021): High-quality 360-degree videos in the context of tele-education.
Parking Lot Occupancy Database (2020)
Nighttime Vehicle Detection database (NVD) (2019)
Hand gesture dataset (2019): Multi-modal Leap Motion dataset for Hand Gesture Recognition.
ViCoCoS-3D (2016): VideoConference Common Scenes in 3D.
LASIESTA database (2016): More than 20 sequences to test moving object detection and tracking algorithms.
Hand gesture database (2015): Hand-gesture database composed by high-resolution color images acquired with the Senz3D sensor.
HRRFaceD database (2014):Face database composed by high resolution images acquired with Microsoft Kinect 2 (second generation).
Lab database (2012): Set of 6 sequences to test moving object detection strategies.
Vehicle image database (2012): More than 7000 images of vehicles and roads.           

 

Software  

Empowering Computer Vision in Higher Education(2024): A Novel Tool for Enhancing Video Coding Comprehension.
Engaging students in audiovisual coding through interactive MATLAB GUIs (2024)
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering Problem (2023)
Solving Routing Problems for Multiple Cooperative Unmanned Aerial Vehicles using Transformer Networks (2023)
Vision Transformers and Traditional Convolutional Neural Networks for Face Recognition Tasks (2023)
Faster GSAC-DNN (2023): A Deep Learning Approach to Nighttime Vehicle Detection Using a Fast Grid of Spatial Aware Classifiers.
SETForSeQ (2020): Subjective Evaluation Tool for Foreground Segmentation Quality. 
SMV Player for Oculus Rift (2016)
Bag-D3P (2016): Face recognition using depth information. 
TSLAB (2015): Tool for Semiautomatic LABeling.    

   

Supplementary material  

Soccer line mark segmentation and classification with stochastic watershed transform (2022)
A fully automatic method for segmentation of soccer playing fields (2022)
Grass band detection in soccer images for improved image registration (2022)
Evaluating the Influence of the HMD, Usability, and Fatigue in 360VR Video Quality Assessments (2020)
Automatic soccer field of play registration (2020)   
Augmented reality tool for the situational awareness improvement of UAV operators (2017)
Detection of static moving objects using multiple nonparametric background-foreground models on a Finite State Machine (2015)
Real-time nonparametric background subtraction with tracking-based foreground update (2015)  
Camera localization using trajectories and maps (2014)

 

                                                                                                                                                                                                                             
 
                                                                   
 
                                                                                                                                                             
 
      

 

 

Automatic mineral recognition from hyperspectral images of cores using a deep learning framework

On May 7th at 12:00, Room B-221.

New minerals have been discovered recently, such as the rare-earth elements, which awaken great interest in several companies which compete to find new deposits, especially in parts of the world less exploited, because of their application to the development of electronic components and devices: for example, mobile phones, chips, tablets or batteries. Several research lines propose solutions to automatize mineral recognition, considering that this process is usually performed manually, despite being crucial in the value chain of mining companies. Together with technological advances about recently developed sensors and innovative processing capabilities, automatic mineral recognition would make explorations more efficient and sustainable and would reduce considerable costs.

In this line, a deep learning framework for automatic mineral recognition from hyperspectral images of scanned cores was presented in this talk. It has been developing for the European public Project Innolog from EIT Raw Materials to improve downhole geophysical logging tools for real-time identification, evaluation and quantification of mineral deposits and raw materials in the subsurface. Fully convolutional neural networks were explained as one of the most widely used deep learning techniques for their great capability to comprehensive complex scene understanding. Finally, experimental results of mineral recognition obtained with databases of hyperspectral images of scanned cores for different infrared wavelength ranges were analysed.

Andrés Bell received the Bachelor of Engineering in Telecommunication Technologies and Services and the Master in Telecommunication Engineering from the Universidad Politécnica de Madrid (UPM), Madrid, Spain, in 2015 and in 2017 respectively. He is currently pursuing the Ph.D degree at the same University.

Since 2014, he has been a member of the Grupo de Tratamiento de Imágenes (Image Processing Group) at the UPM. His current research interests include computer vision, machine learning, deep learning, video and image analysis and processing.

awardieee