Thesis

DOCTORAL THESIS: 3D Underwater SLAM Using Sonar and Laser Sensors

By Albert Palomer Vila Supervised by Dr. Pere Ridao, Dr. Josep Forest i Dr. David Ribas   Abstract The 3D perception and mapping problem are very closely related with the robot localization and has not been yet solved up to a degree that allow AUVs to interact with complex environments safely. By providing AUVs with better 3D perception and improving their localization the usage of AUVs in tasks such as…

DOCTORAL THESIS: Motion Annotation in complex video datasets

By Mahmood Muhammad Habib Supervised by Dr. Arnau Oliver Malagelada   Abstract An in-depth analysis of computer vision methodologies is greatly dependent on the benchmarks they are tested upon. Any dataset is as good as the diversity of the true nature of the problem enclosed in it. Motion segmentation is a preprocessing step in computer vision whose publicly available datasets have certain limitations. Some databases are not up-to-date with modern…

DOCTORAL THESIS: Online Acoustic Localization Methods for Autonomous Underwater Vehicles

By Guillem Vallicrosa Massaguer Supervised by Dr. Pere Ridao   Abstract Autonomous Underwater Vehicles (AUVs) true autonomy capabilities in complex poten-tially unknown environments, have not yet been fully achieved because of the lack of online algorithms that can solve fundamental problems such as localization, mapping and path-planning on-board the AUV and consequently react according to their outputs. These algorithms can empower them for new capabilities such as long-term deployments, au-tonomous…

DOCTORAL THESIS: Underwater Navigation and Mapping with an Omnidirectional Optical Sensor

Underwater Navigation and Mapping with an Omnidirectional Optical Sensor By Josep Bosch Alay Supervised by Dr. Nuno Gràcias / Dr. Pere Ridao Abstract Omnidirectional vision has received increasing interest during the last decade from the computer vision community. A large number of camera models have reached the market to meet the increasing demand for panoramic imagery. However, the use of omnidirectional cameras underwater is still very limited. In this thesis...

DOCTORAL THESIS: Glandular Tissue Pattern Analysis Through Multimodal MRI-Mammography Registration

Glandular Tissue Pattern Analysis Through Multimodal MRI-Mammography Registration By Eloy García Marcos Supervised by Dr. Joan Martí Bonmatí / Dr. Arnau Oliver Malagelada Abstract Breast cancer is the most common cancer in women worldwide. Current statistics show that one in eight women will develop this disease over the course of her lifetime. While X-ray mammography is the gold standard image modality for screening and diagnosis of breast cancer, it presents...

Online Path Planning for Autonomous Underwater Vehicles under Motion Constraints

Doctoral thesis “Online Path Planning for Autonomous Underwater Vehicles under Motion Constraints” By Juan David Hernández Vega Supervised by Dr. Marc Carreras   Abstract Thesis submitted to the University of Girona in fulfillment of the requirements for the degree of Doctor of PhilosophySince their beginning in the late 1950s, the capabilities and applications of autonomous underwater vehicles (AUVs) have continuously evolved. Their most common applications include imaging and inspecting different…

Robot Learning applied to Autonomous Underwater Vehicles for intervention tasks

Doctoral thesis “Robot Learning applied to Autonomous Underwater Vehicles for intervention tasks” By Arnau Carrera Viñas Supervised by Marc Carreras, Narcís Palomeras and Petar Kormushev   Abstract The interest in performing underwater tasks using AUVs has been growing over the past few decades. The initial focus was on the exploration of underwater areas, performing survey trajectories harvesting data to build bathymetric maps or photo-mosaics. Later the focus of research switched…

Case-level detection of mammographic masses

Doctoral thesis “Case-level detection of mammographic masses” By Meritxell Tortajada Giménez Supervised by Jordi Freixenet and Robert Martí   Abstract This thesis is focused on the automatic detection of masses in FFDM images by using case-level information which includes bilateral, temporal and/or ipsilateral information. As a first step, FFDM images are preprocessed to improve image quality before working on the proper detection framework. A novel enhancement method is applied to…