Gesture based interface for image annotation

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Informática pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Algorithms for information extraction and signal annotation on long-term biosignals using clustering techniques

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

People and object tracking for video annotation

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Interfaces for television content sharing and annotation

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Automated image tagging through tag propagation

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial Para obtenção do grau de Mestre em Engenharia Informática

Touchless autopsy report

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Video interaction using pen-based technology

Dissertação para obtenção do Grau de Doutor em Informática

Deciphering the genome of Desulfovibrio gigas: the role of the hydrogenases

The work presented in this thesis describes the functional characterization of hydrogenases in the overall energy metabolism of the sulfate reducing bacterium Desulfovibrio gigas. With the complete annotation of the D. gigas genome, we were able to verify that only the two previously described hydrogenases are present in this organism, the periplasmic [NiFe] HynAB and the cytoplasmic membrane-bound [NiFe] Ech.(...)

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work.

Learning facial-expression models with crowdsourcing

The computational power is increasing day by day. Despite that, there are some tasks that are still difficult or even impossible for a computer to perform. For example, while identifying a facial expression is easy for a human, for a computer it is an area in development. To tackle this and similar issues, crowdsourcing has grown as a way to use human computation in a large scale. Crowdsourcing is a novel approach to collect labels in a fast and cheap manner, by sourcing the labels from the crowds. However, these labels lack reliability since annotators are not guaranteed to have any expertise in the field. This fact has led to a new research area where we must create or adapt annotation models to handle these weaklylabeled data. Current techniques explore the annotators’ expertise and the task difficulty as variables that influences labels’ correction. Other specific aspects are also considered by noisy-labels analysis techniques. The main contribution of this thesis is the process to collect reliable crowdsourcing labels for a facial expressions dataset. This process consists in two steps: first, we design our crowdsourcing tasks to collect annotators labels; next, we infer the true label from the collected labels by applying state-of-art crowdsourcing algorithms. At the same time, a facial expression dataset is created, containing 40.000 images and respective labels. At the end, we publish the resulting dataset.

Humor e ficção na produção de conteúdos audiovisuais

O presente relatório de estágio reflete sobre a produção de conteúdos audiovisuais no Canal Q, com base no Humor e na Ficção. A ele adjacente, encontramos uma reflexão teórica sobre o poder do Humor, enquanto instrumento de comunicação, ao longo da história. Este trabalho pondera a relação partilhada entre o riso, Humor e, em última análise, a Comédia. Sendo o primeiro o reflexo físico do segundo e o terceiro, a sua aplicação dramática, tentaremos compreender em que medida podemos caraterizar esse conceito amplo e vago que é o Humor. Mais que um estado de espírito, mais que um género literário ou cinematográfico, o Humor é uma linguagem muito específica, com traços estilísticos próprios e com um poder sociopolítico muito peculiar. Para além da sua faceta lúdica, que permite um enorme poder de agregação de audiência, muito evidenciada na indústria do entretenimento, o Humor revela-­‐se uma verdadeira arma de poder político. Com este entendimento sobre o humor, é mais fácil compreender a importância da produção de conteúdos humorísticos nas plataformas audiovisuais e de que maneira essa produção poderá afetar a sociedade e o Homem.

Video metadata extraction in a videoMail system

Currently the world swiftly adapts to visual communication. Online services like YouTube and Vine show that video is no longer the domain of broadcast television only. Video is used for different purposes like entertainment, information, education or communication. The rapid growth of today’s video archives with sparsely available editorial data creates a big problem of its retrieval. The humans see a video like a complex interplay of cognitive concepts. As a result there is a need to build a bridge between numeric values and semantic concepts. This establishes a connection that will facilitate videos’ retrieval by humans. The critical aspect of this bridge is video annotation. The process could be done manually or automatically. Manual annotation is very tedious, subjective and expensive. Therefore automatic annotation is being actively studied. In this thesis we focus on the multimedia content automatic annotation. Namely the use of analysis techniques for information retrieval allowing to automatically extract metadata from video in a videomail system. Furthermore the identification of text, people, actions, spaces, objects, including animals and plants. Hence it will be possible to align multimedia content with the text presented in the email message and the creation of applications for semantic video database indexing and retrieving.

Genome-scale metabolic network reconstruction of Polaromonas sp. strain JS666: analysis of cDCE degradation rates and design of experiments for bioremediation improvement

Release of chloroethene compounds into the environment often results in groundwater contamination, which puts people at risk of exposure by drinking contaminated water. cDCE (cis-1,2-dichloroethene) accumulation on subsurface environments is a common environmental problem due to stagnation and partial degradation of other precursor chloroethene species. Polaromonas sp. strain JS666 apparently requires no exotic growth factors to be used as a bioaugmentation agent for aerobic cDCE degradation. Although being the only suitable microorganism found capable of such, further studies are needed for improving the intrinsic bioremediation rates and fully comprehend the metabolic processes involved. In order to do so, a metabolic model, iJS666, was reconstructed from genome annotation and available bibliographic data. FVA (Flux Variability Analysis) and FBA (Flux Balance Analysis) techniques were used to satisfactory validate the predictive capabilities of the iJS666 model. The iJS666 model was able to predict biomass growth for different previously tested conditions, allowed to design key experiments which should be done for further model improvement and, also, produced viable predictions for the use of biostimulant metabolites in the cDCE biodegradation.