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About

I am a Coordinator Professor at the Polytechnic of Porto and a Researcher at INESC TEC, where I lead the Multimedia Communications Technology Area. I  obtained my PhD from University of Porto in the area of multimedia content management. I have been responsible for the participation of INESC TEC in several national and European projects, involving universities and media industries. Author of several publications, I am also an active reviewer for journals and conferences and engaged in the organization of workshops and program committees in the area of Multimedia. Recently I co-chaired the Immersive Media Experiences workshop series (2013-2015) at ACM MM. Additionally I am also often engaged in the evaluation of European and Portuguese research proposals and projects. My main research activities and interests are in the field of networked audiovisual systems, including digital television and new services, content management, personalization and recomendation, new media formats and immersive and interactive media.

Interest
Topics
Details

Details

013
Publications

2022

Photo2Video: Semantic-Aware Deep Learning-Based Video Generation from Still Content

Authors
Viana, P; Andrade, MT; Carvalho, P; Vilaca, L; Teixeira, IN; Costa, T; Jonker, P;

Publication
Journal of Imaging

Abstract
Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.

2021

SmoothMV

Authors
da Costa, TS; Andrade, MT; Viana, P;

Publication
Proceedings of the International Workshop on Immersive Mixed and Virtual Environment Systems (MMVE '21)

Abstract

2021

Inferring Contextual Data from Real-World Photography

Authors
Costa, TS; Andrade, MT; Viana, P;

Publication
Advances in Intelligent Systems and Computing - Intelligent Systems Design and Applications

Abstract

2021

Emotion Identification in Movies through Facial Expression Recognition

Authors
Almeida, J; Vilaca, L; Teixeira, IN; Viana, P;

Publication
Applied Sciences

Abstract
Understanding how acting bridges the emotional bond between spectators and films is essential to depict how humans interact with this rapidly growing digital medium. In recent decades, the research community made promising progress in developing facial expression recognition (FER) methods. However, no emphasis has been put in cinematographic content, which is complex by nature due to the visual techniques used to convey the desired emotions. Our work represents a step towards emotion identification in cinema through facial expressions’ analysis. We presented a comprehensive overview of the most relevant datasets used for FER, highlighting problems caused by their heterogeneity and to the inexistence of a universal model of emotions. Built upon this understanding, we evaluated these datasets with a standard image classification models to analyze the feasibility of using facial expressions to determine the emotional charge of a film. To cope with the problem of lack of datasets for the scope under analysis, we demonstrated the feasibility of using a generic dataset for the training process and propose a new way to look at emotions by creating clusters of emotions based on the evidence obtained in the experiments.

2021

Automatic TV Logo Identification for Advertisement Detection without Prior Data

Authors
Carvalho, P; Pereira, A; Viana, P;

Publication
Applied Sciences

Abstract
Advertisements are often inserted in multimedia content, and this is particularly relevant in TV broadcasting as they have a key financial role. In this context, the flexible and efficient processing of TV content to identify advertisement segments is highly desirable as it can benefit different actors, including the broadcaster, the contracting company, and the end user. In this context, detecting the presence of the channel logo has been seen in the state-of-the-art as a good indicator. However, the difficulty of this challenging process increases as less prior data is available to help reduce uncertainty. As a result, the literature proposals that achieve the best results typically rely on prior knowledge or pre-existent databases. This paper proposes a flexible method for processing TV broadcasting content aiming at detecting channel logos, and consequently advertising segments, without using prior data about the channel or content. The final goal is to enable stream segmentation identifying advertisement slices. The proposed method was assessed over available state-of-the-art datasets as well as additional and more challenging stream captures. Results show that the proposed method surpasses the state-of-the-art.

Supervised
thesis

2021

Cinemetrics: Creative Approaches to Visualize and Analyze Movie Data

Author
ANDRÉ FILIPE CUNHA E SILVA

Institution
IPP-ISEP

2021

Anotação em conteúdos audiovisuais em contexto educativo

Author
João Miguel Calisto Marçal

Institution
UP-FEUP

2021

Desenvolvimento de um Parser de ficheiros ARXML

Author
NUNO MIGUEL SILVA PACHECO

Institution
IPP-ISEP

2020

Towards a Scalable Dataset Construction for Facial Recognition: A guided data selection approach for diversity stimulation

Author
Luís Miguel Salgado Nunes Vilaça

Institution
IPP-ISEP

2020

Context-Based Cultural Visits

Author
Mariana Figueiredo Moutinho Resende Assis

Institution
UP-FEUP