Phd in COMPUTER SCIENCE

Thesis title: Exploiting local features in modern Computer Vision

Computer Vision is an interdisciplinary field which refers to replicating the human vision system and enabling machines to gain high-level understanding from digital images or videos. It plays an important role in many applications, which have become commonplace today such as face recognition, anomaly detection, behaviour recognition, re-identification, image segmentation, and so on. During the last years, the widespread of new visual sensors (i.e., Pan Tilt Zoom camera, depth cameras, acoustic sensors, etc.) have enlarged the number of possible computer vision applications. Particular attention has been focused on those applications in which unconstrained still images and videos have to be processed. This has raised at the same time new research challenges. Among the most interesting topics one which is receiving a growing interest is the identification and extraction of distinct features. The local features are one of the most used features in the current literature. They allow to detect and extract features, which are robust to several types of transformation or distortion (i.e., occlusion, rotation, scale). However, this type of features has been mainly used in RGB camera-based applications. The goal of this thesis is presenting and demonstrating the efficacy of novel computer vision research contributions to different application scenarios as background modelling, RGB-D data fusion for event detection and underwater image compression and transmission. In these applications, we will show how local features can be used on unconstrained environment and with image and videos acquired from different visual sensors.