End of Degree Project (TFG)

 

 

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End of Degree Project (TFG)   

 

2023

Evaluation and modelling of the user experience with immersive technologies

Recent years have witnessed many impressive technological and scientific advances relation to immersive technologies, such as Virtual Reality (VR), free-viewpoint video, 360° video, and Augmented Reality (AR). The availability of these technologies is paving the way to some extremely appealing new applications and services in different domains, such as entertainment, communications, social relations, healthcare, and industry. The users of these new technologies can explore and experience the contents in a more interactive and personalized way than previous technologies, intensifying their sensation of “being there”. These new perceptual dimensions and interaction behaviors provided by immersive technologies (e.g., exploration patterns, perceptual quality, immersiveness, simulator sickness, etc.), together with the new challenges concerning the whole processing chain (i.e., from acquisition to rendering), require an exhaustive study and understanding in order to satisfy the demands and expectations of the users. Thus, the evaluation of the user experience by subjective experiments with test participants and the development of models to predict and estimate it (e.g., based on machine learning techniques) is essential. In this sense, the Grupo de Tratamiento de Imágenes (GTI) has a long experience and is actively researching on subjective assessment tests and model development, and is looking for motivated students to work on these activities.

Contact person: Jesús Gutiérrez, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

*Other topics*

We can discuss about other possible topics (your own proposals are welcome!) related to evaluation/modelling of the user experience, eye tracking and visual attention models, virtual reality and image/video quality for health applications.

Contact person: Jesús Gutiérrez, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Deep learning for Semantic Segmentation and Depth Estimation

Deep learning has revolutionized Computer Vision with its remarkable performance. It can be applied to tasks such as Semantic Segmentation and Depth Estimation, which are closely related to immersive video technologies, volumetric capture systems and metaverse related applications.

The aim of this bachelor/master's thesis is to generate and/or annotate a dataset and to use it to train state-of-the-art deep learning models for semantic segmentation, depth estimation, and even combining both tasks.

We are looking for motivated students with a basic background in Deep Learning, Python, and Computer Vision.

Contact people:

Julián Cabrera: This email address is being protected from spambots. You need JavaScript enabled to view it.

Javier Usón: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Video transmission for Cloud deployment of Deep Learning and Computer Vision applications

Deep Learning applications require heavy processing which must be performed by powerful machines. This is a huge problem for portable video setups as these machines are often too difficult to transport and cannot be deployed anywhere.

The aim of this bachelor/master's thesis is to develop a real-time video capture + transmission system that allows to separate the capture hardware from the heavy processing. This way, the heavy applications can be deployed in a cloud scenario and the complexity of the capture system is greatly reduced.

This project would require students who enjoy programming and have some background in video encoding. C++, Python and Linux will be extensively used.

Contact people:

Julián Cabrera: This email address is being protected from spambots. You need JavaScript enabled to view it.

Javier Usón: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Real-time Video transmission for immersive applications

Immersive video technologies often require heavy processing, so they are restricted to powerful devices. This can be avoided by letting an external server perform the processing and transmitting the results and a regular 2D video. In this scenario, any device capable of playing video would be able to display the immersive experience.

The aim of this bachelor/master's thesis is to develop web and/or Unity applications that can communicate with an immersive video system so the experience can be controlled from different devices such as laptops, phones or head mounted displays.

We are looking for motivated students who enjoy programming and have some background in video encoding and transmission.

Contact people:

Julián Cabrera: This email address is being protected from spambots. You need JavaScript enabled to view it.

Javier Usón: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Point Cloud Compression

Point clouds are used to represent volumetric visual data. A point cloud is a set of individual 3D points that contain some other attributes such as color, surface normal, etc.

The objective of this Bachelor/Master Thesis is to analyze the state of the art of Video-based point cloud compression (V-PCC) to identify the most suitable compression approaches for volumetric video. To assess the selected approaches a dataset of point clouds  captured with Microsoft Azure Kinect cameras will be also generated.

We are looking for motivated students with knowledge of video processing and encoding, C++ and Linux.

Julián Cabrera: This email address is being protected from spambots. You need JavaScript enabled to view it.

NeRFs for FVV

Neural Radiance Fields are a very promising approach to virtual view rendering using Neural Networks. They generate an implicit representation of a volumetric scene using a relatively small set of pictures of said scene, taken from different angles. This Bachelor/Masters Thesis involves researching the latest advancements in this field, apply them to a multi-camera setup and comparing its quality and speed to the ones obtained by a FVV system based on traditional rendering techniques.

We are looking for motivated students with knowledge of Deep Learning, Python and basic Computer Vision.

Julián Cabrera: This email address is being protected from spambots. You need JavaScript enabled to view it.

2022

Depth Image Generation and Compression for FVV

Free viewpoint video (FVV) is an application that allows the user to visualize a scene freely, choosing any arbitrary point of view they desire. This Bachelor/Master Thesis involves working with an implementation of FVV based around the use of depth images. It will involve researching ways of generating such depth images and then compress them in order to assess the impact these processes have on the final views rendered by the FVV system.

For Masters students, we propose a more in-depth dive into the depth image generation using Neural Networks for both mono and stereo imaging approaches.

This project would require students who enjoy programming and have some background in video encoding. C++, Python and Linux will be extensively used.

Contact person: Julián Cabrera, This email address is being protected from spambots. You need JavaScript enabled to view it.  

 

Acquisition of facial movements for Social XR applications

Extended reality technologies are offering the possibility to the users to interact and share social experiences as if they were together. In order to offer them the best immersive experience boosting their sensation of “being there” and the quality of the interactions with the other users there are several perceptual and technical factors to improve. One of them is the capture of facial and mouth movements when wearing Head Mounted Displays, which can be useful in several applications (e.g. animating virtual avatars) and in several studies of user behavior (e.g. detection of emotions). Therefore, the Grupo de Tratamiento de Imágenes (GTI) is looking for motivated students to integrate a facial tracker in an HMD in order to capture facial movements for social XR applications.

Contact person: Jesús Gutiérrez, This email address is being protected from spambots. You need JavaScript enabled to view it.

  

2021

Design and implementation of a classification system for neuropathology microscopy images based on deep learning techniques (Ref: Neuro).

Microscopy images of the nervous system tissue are used to assist the diagnosis of disease. This Master Thesis addresses the design and development of a solution based on deep learning techniques to detect the presence of disease in these microscopy images. To that extend, an annotated database with images provided by the Hospital de Almería will be also created.

We are looking for students with experience with Python and Deep Learning Tools ( Keras, Tensorflow, PyTorch, …)

Contact person: Julián Cabrera, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Design and implementation of an annotation tool for echocardiograms to assist Kawasaki disease identification (Ref: Kawasaki).

The Kawasaki disease is the most common heart condition affecting young children, usually under five years old, in developed countries. The disease is responsible for the damages of blood vessels all over the body and results in vasculitis, myocarditis and coronary dilation causing long term heart complications. Therefore, it is essential to be able to detect the disease at an early state.

One of the methods used to detect Kawasaki disease is by the analysis of the echocardiograms of the heart. In the Grupo de Tratamiento de Imágenes we have already developed a platform to assist Kawasaki disease diagnosis. This Mater Thesis addresses the design and implementation of an annotation tool for the echocardiograms, and its integration with the platform.

This Master Thesis will be done in collaboration with Hospital 12 de Octubre.

We are looking for students with experience with Python. Experience with Deep Learning Tools ( Keras, Tensorflow, PyTorch, …) is a plus.

Contact person: Julián Cabrera, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Design and implementation of a multiview capture system (Ref: Multiview).

Free Viewpoint Video is an immersive multimedia system that provides the user the capability to move freely on a scene that is captured by a multiview set up of cameras. The objective of this Master Thesis is to design and develop a multiview capture system using IDS industrial cameras and their SW libraries.

We are looking for students with experience in C++ and Linux.

Contact person: Julián Cabrera, This email address is being protected from spambots. You need JavaScript enabled to view it.

 

2018

Análisis de estrategias de codificación para imágenes lightfield.

Análisis de las soluciones actuales para la codificación de imágenes lightfield/plenópticas, y posible adaptación de esquemas de compresión existentes a las características particulares de las imágenes capturadas por cámaras lightfield/plenópticas.

Herramientas / Entorno de programación: Matlab, software de codificadores existentes.

Para obtener más información contactar con Pablo Carballeira López 

   

Desarrollo de una herramienta con interfaz gráfica para el diseño de configuraciones de cámaras para vídeo Free Viewpoint.

Desarrollo de una herramienta software con interfaz gráfica basada en un modelo de percepción subjetiva de vídeo Free Viewpoint. El objetivo de esta herramienta es guiar el diseño de estructuras de cámaras para vídeo Super MultiView (SMV) y Free-Navigation (FN), mediante la aplicación de un modelo de percepción subjetiva de este tipo de vídeos.

Herramientas / Entorno de programación: C++, OpenCV, Qt.

Para obtener más información contactar con Pablo Carballeira López