Video interaction using pen-based technology

Dissertação para obtenção do Grau de Doutor em InformáticaVideo can be considered one of the most complete and complex media and its manipulating is still a difficult and tedious task. This research applies pen-based technology to video manipulation, with the goal to improve this interaction. Even though the human familiarity with pen-based devices, how they can be used on video interaction, in order to improve it, making it more natural and at the same time fostering the user’s creativity is an open question. Two types of interaction with video were considered in this work: video annotation and video editing. Each interaction type allows the study of one of the interaction modes of using pen-based technology: indirectly, through digital ink, or directly, trough pen gestures or pressure. This research contributes with two approaches for pen-based video interaction: pen-based video annotations and video as ink. The first uses pen-based annotations combined with motion tracking algorithms, in order to augment video content with sketches or handwritten notes. It aims to study how pen-based technology can be used to annotate a moving objects and how to maintain the association between a pen-based annotations and the annotated moving object The second concept replaces digital ink by video content, studding how pen gestures and pressure can be used on video editing and what kind of changes are needed in the interface, in order to provide a more familiar and creative interaction in this usage context.This work was partially funded by the UTAustin-Portugal, Digital Media, Program (Ph.D. grant: SFRH/BD/42662/2007 - FCT/MCTES); by the HP Technology for Teaching Grant Initiative 2006; by the project "TKB - A Transmedia Knowledge Base for contemporary dance" (PTDC/EAT/AVP/098220/2008 funded by FCT/MCTES); and by CITI/DI/FCT/UNL (PEst-OE/EEI/UI0527/2011

Egocentric Hand Detection Via Dynamic Region Growing

Egocentric videos, which mainly record the activities carried out by the users of the wearable cameras, have drawn much research attentions in recent years. Due to its lengthy content, a large number of ego-related applications have been developed to abstract the captured videos. As the users are accustomed to interacting with the target objects using their own hands while their hands usually appear within their visual fields during the interaction, an egocentric hand detection step is involved in tasks like gesture recognition, action recognition and social interaction understanding. In this work, we propose a dynamic region growing approach for hand region detection in egocentric videos, by jointly considering hand-related motion and egocentric cues. We first determine seed regions that most likely belong to the hand, by analyzing the motion patterns across successive frames. The hand regions can then be located by extending from the seed regions, according to the scores computed for the adjacent superpixels. These scores are derived from four egocentric cues: contrast, location, position consistency and appearance continuity. We discuss how to apply the proposed method in real-life scenarios, where multiple hands irregularly appear and disappear from the videos. Experimental results on public datasets show that the proposed method achieves superior performance compared with the state-of-the-art methods, especially in complicated scenarios

Augmented reality device for first response scenarios

A prototype of a wearable computer system is proposed and implemented using commercial off-shelf components. The system is designed to allow the user to access location-specific information about an environment, and to provide capability for user tracking. Areas of applicability include primarily first response scenarios, with possible applications in maintenance or construction of buildings and other structures. Necessary preparation of the target environment prior to system\u27s deployment is limited to noninvasive labeling using optical fiducial markers. The system relies on computational vision methods for registration of labels and user position. With the system the user has access to on-demand information relevant to a particular real-world location. Team collaboration is assisted by user tracking and real-time visualizations of team member positions within the environment. The user interface and display methods are inspired by Augmented Reality1 (AR) techniques, incorporating a video-see-through Head Mounted Display (HMD) and fingerbending sensor glove.*. 1Augmented reality (AR) is a field of computer research which deals with the combination of real world and computer generated data. At present, most AR research is concerned with the use of live video imagery which is digitally processed and augmented by the addition of computer generated graphics. Advanced research includes the use of motion tracking data, fiducial marker recognition using machine vision, and the construction of controlled environments containing any number of sensors and actuators. (Source: Wikipedia) *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Adobe Acrobat; Microsoft Office; Windows MediaPlayer or RealPlayer