A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

Incorporating global positioning data in real time P2P audio/video streams for mobile devices

We propose an original method to geoposition an audio/video stream with multiple emitters that are at the same time receivers of the mixed signal. The achieved method is suitable for those comes where a list of positions within a designated area is encoded with a degree of precision adjusted to the visualization capabilities; and is also easily extensible to support new requirements. This method extends a previously proposed protocol, without incurring in any performance penalty.

P2P audio/video protocol with global positioning data in real time for mobile devices

In this paper, we propose an original method to geoposition an audio/video stream with multiple emitters that are at the same time receivers of the mixed signal. The obtained method is suitable when a list of positions within a known area is encoded with precision tailored to the visualization capabilities of the target device. Nevertheless, it is easily adaptable to new precision requirements, as well as parameterized data precision. This method extends a previously proposed protocol, without incurring in any performance penalty.