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About

I was born in the district of Porto. I got a degree in Eletric and Computer Engeneering in 2001, a Master degre in Networks and Communication Services in 2004 and the PhD degree in Eletric and COmputer Engeneering in 2012, all from the Faculty of Engeneering of the University of Porto. I've been a collaborator of INESC TEC since 2001 and I'm currently a Senior Researcher at the Center of Telecommunications and Multimedia. I'm also an Invited Adjunct Professor at the School f Engeneering of the Polythecnic Institute of Porto. My current reseach interests include image and video processing, multimedia systems and computer vision. 

Interest
Topics
Details

Details

012
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.

2022

Enhancing Photography Management Through Automatically Extracted Metadata

Authors
Carvalho, P; Freitas, D; Machado, T; Viana, P;

Publication
INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, ISDA 2021

Abstract
The tremendous increase in photographs that are captured each day by common users has been favoured by the availability of high quality devices at accessible costs, such as smartphones and digital cameras. However, the quantity of captured photos raises new challenges regarding the access and management of image repositories. This paper describes a lightweight distributed framework intended to help overcome these problems. It uses image metadata in EXIF format, already widely added to images by digital acquisition devices, and automatic facial recognition to provide management and search functionalities. Moreover, a visualization functionality using a graph-based strategy was integrated, enabling an enhanced and more interactive navigation through search results and the corresponding relations.

2021

Automatic TV Logo Identification for Advertisement Detection without Prior Data

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

Publication
APPLIED SCIENCES-BASEL

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.

2020

Efficient CIEDE2000-based Color Similarity Decision for Computer Vision

Authors
Pereira, A; Carvalho, P; Coelho, G; Corte Real, L;

Publication
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY

Abstract

2020

Texture collinearity foreground segmentation for night videos

Authors
Martins, I; Carvalho, P; Corte Real, L; Luis Alba Castro, JL;

Publication
COMPUTER VISION AND IMAGE UNDERSTANDING

Abstract
One of the most difficult scenarios for unsupervised segmentation of moving objects is found in nighttime videos where the main challenges are the poor illumination conditions resulting in low-visibility of objects, very strong lights, surface-reflected light, a great variance of light intensity, sudden illumination changes, hard shadows, camouflaged objects, and noise. This paper proposes a novel method, coined COLBMOG (COLlinearity Boosted MOG), devised specifically for the foreground segmentation in nighttime videos, that shows the ability to overcome some of the limitations of state-of-the-art methods and still perform well in daytime scenarios. It is a texture-based classification method, using local texture modeling, complemented by a color-based classification method. The local texture at the pixel neighborhood is modeled as an N-dimensional vector. For a given pixel, the classification is based on the collinearity between this feature in the input frame and the reference background frame. For this purpose, a multimodal temporal model of the collinearity between texture vectors of background pixels is maintained. COLBMOG was objectively evaluated using the ChangeDetection.net (CDnet) 2014, Night Videos category, benchmark. COLBMOG ranks first among all the unsupervised methods. A detailed analysis of the results revealed the superior performance of the proposed method compared to the best performing state-of-the-art methods in this category, particularly evident in the presence of the most complex situations where all the algorithms tend to fail. © 2020 Elsevier Inc.

Supervised
thesis

2021

Utilização de técnicas de Business Intelligence e Analytics na avaliação da importância do Cross-Selling e dos empregados de Front-Office como alavancas para uma melhor dinâmica comercial

Author
Tiago Francisco Fernandes da Silva

Institution
UP-FEP

2020

Definition and adaptation of 3D templates for synthesizing human activity

Author
Ricardo Miguel Oliveira Rodrigues de Carvalho

Institution
UP-FEUP

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

Deteção de publicidade em conteúdos de televisão sem informação a priori

Author
Guilherme Dias Castro

Institution
UP-FEUP

2020

Flexible and Interactive Navigation in Synthesized Environment

Author
Vítor Magalhães

Institution
UP-FEUP