Ambient Intelligence (AmI) is a muldisciplinary area which refers to environments that are sensitive and responsive to the presence of people and objects. The rapid progress of technology and simultaneous reduction of hardware costs characterizing the recent years have enlarged the number of possible AmI applications, thus raising at the same time new research challenges. In particular, one important requirement in AmI is providing a proactive support to people in their everyday working and free-time activities. To this aim, Computer Vision represents a core research track since only through suitable vision devices and techniques it is possible to detect elements of interest and understand the occurring events. The goal of this thesis is presenting and demonstrating efficacy of novel machine vision research contributes for different AmI scenarios: object keypoints analysis for Augmented Reality purpose, segmentation of natural images for plant species recognition and heterogeneous people identification in unconstrained environments.

Computer Vision Techniques for Ambient Intelligence Applications

2016

Abstract

Ambient Intelligence (AmI) is a muldisciplinary area which refers to environments that are sensitive and responsive to the presence of people and objects. The rapid progress of technology and simultaneous reduction of hardware costs characterizing the recent years have enlarged the number of possible AmI applications, thus raising at the same time new research challenges. In particular, one important requirement in AmI is providing a proactive support to people in their everyday working and free-time activities. To this aim, Computer Vision represents a core research track since only through suitable vision devices and techniques it is possible to detect elements of interest and understand the occurring events. The goal of this thesis is presenting and demonstrating efficacy of novel machine vision research contributes for different AmI scenarios: object keypoints analysis for Augmented Reality purpose, segmentation of natural images for plant species recognition and heterogeneous people identification in unconstrained environments.
2016
it
File in questo prodotto:
File Dimensione Formato  
buoncompagni_simone_tesi.pdf

accesso solo da BNCF e BNCR

Tipologia: Altro materiale allegato
Licenza: Tutti i diritti riservati
Dimensione 21.75 MB
Formato Adobe PDF
21.75 MB Adobe PDF

I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/331427
Il codice NBN di questa tesi è URN:NBN:IT:BNCF-331427