Re-identificación de personas utilizando únicamente información de profundidad

La finalidad de este trabajo es el diseño, implementación y evaluación de un sistema de re-identificación de personas a partir de imágenes de profundidad obtenidas por un sensor de tiempo de vuelo (ToF) ubicado en posición cenital. Este trabajo parte del detector desarrollado por el grupo GEINTRA y añade nuevas funcionalidades al sistema: ejecución en tiempo real y re-identificación de personas. La detección y la re-identificación se realizan con un clasificador basado en la técnica de Análisis de Componentes Principales (PCA). Para la validación del sistema se ha utilizado una base de datos de imágenes de profundidad cumpliendo con éxito los objetivos propuestos.The main objective of this work is the design, implementation and evaluation of a system capable of re-identifying people using only depth images obtained by a Time of Flight (ToF) sensor placed in zenithal position. This work starts uses the detector develoved by the GEINTRA group and adds new funtionalities to the system: real-time ejecution and people re-identification. The detection and reidentification of people are done by a clasificator based on the Principal Component Analysis (PCA) technique. The validation of the systeam has been done using a data base of depth images, achieving the proposed goals.Grado en Ingeniería en Tecnologías de Telecomunicació

Towards Deep People Detection using CNNs Trained on Synthetic Images

Para solicitar sexeni

Elaboración de vídeos y cuestionarios para introducir la clase invertida en diversos laboratorios de Física

Memoria ID-19 Ayudas de la Universidad de Salamanca para la innovación docente, curso 2020-2021

La producción cinematográfica como instrumento para la reconstrucción del pasado. Un eficaz recurso pedagógico para la enseñanza-aprendizaje de la historia y del arte

Memoria ID-0173. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2016-2017

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

Elaboración de píldoras educativas sobre Historia de la Veterinaria

Tras el éxito de la utilización de la ludificación como motivación para el estudio de la Historia de la Veterinaria, nos propusimos crear pequeños vídeos o píldoras de conocimiento sobre hechos o personajes históricos que fueran reusables (se pueden utilizar en diferentes contextos), interoperables (sirven para propósitos diferentes) y accesibles por su formato digital que facilita el almacenaje y su recuperación. En este proyecto se ha grabado más escenas antes del confinamiento y preparados la historioteca con una de las píldoras ya definitivas

Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases

Altres ajuts: Generalitat de Catalunya, Departament de Salut; Generalitat de Catalunya, Departament d'Empresa i Coneixement i CERCA Program; Ministerio de Ciencia e Innovación; Instituto Nacional de Bioinformática; ELIXIR Implementation Studies (CNAG-CRG); Centro de Investigaciones Biomédicas en Red de Enfermedades Raras; Centro de Excelencia Severo Ochoa; European Regional Development Fund (FEDER).Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%)

Tele-insti

Se trata de producir programas de televisión de cierta calidad y en directo durante el tiempo de recreo. Los programas tienen, fundamentalmente, carácter escolar, y su misión es mantener al corriente sobre los acontecimientos del centro. Los objetivos son, la puesta en práctica de las técnicas de producción de programas de televisión, la creación de guiones y su realización audiovisual. La experiencia se evalúa mediante los vídeos grabados de los programas en directo, y a través de la crítica de los alumnos que ven las emisiones.Madrid (Comunidad Autónoma). Consejería de Educación y CulturaMadridMadrid (Comunidad Autónoma). Subdirección General de Formación del Profesorado. CRIF Las Acacias; General Ricardos 179 - 28025 Madrid; Tel. + 34915250893ES

Semi-Supervised Anomaly Detection in Video-Surveillance Scenes in the Wild

Surveillance cameras are being installed in many primary daily living places to maintain public safety. In this video-surveillance context, anomalies occur only for a very short time, and very occasionally. Hence, manual monitoring of such anomalies may be exhaustive and monotonous, resulting in a decrease in reliability and speed in emergency situations due to monitor tiredness. Within this framework, the importance of automatic detection of anomalies is clear, and, therefore, an important amount of research works have been made lately in this topic. According to these earlier studies, supervised approaches perform better than unsupervised ones. However, supervised approaches demand manual annotation, making dependent the system reliability of the different situations used in the training (something difficult to set in anomaly context). In this work, it is proposed an approach for anomaly detection in video-surveillance scenes based on a weakly supervised learning algorithm. Spatio-temporal features are extracted from each surveillance video using a temporal convolutional 3D neural network (T-C3D). Then, a novel ranking loss function increases the distance between the classification scores of anomalous and normal videos, reducing the number of false negatives. The proposal has been evaluated and compared against state-of-art approaches, obtaining competitive performance without fine-tuning, which also validates its generalization capability. In this paper, the proposal design and reliability is presented and analyzed, as well as the aforementioned quantitative and qualitative evaluation in-the-wild scenarios, demonstrating its high sensitivity in anomaly detection in all of them