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Sobre

Sobre

Sou Professora Coordenadora no Politécnico do Porto e Investigadora no INESC TEC, no Centro de Telecomunicações e Multimédia, onde lidero a área de Tecnologias de Comunicação Multimédia. Tenho um Doutoramento em Engenharia Electrotécnica e de Computadores pela Universidade do Porto, com um foco na àrea da Gestão de Conteúdos Audiovisuais. Enquanto investigadora do INESC TEC, tenho sido responsável por diversos projectos Europeus e Nacionais, envolvendo parceiros da área da indústria, media e academia. Autora de diversas publicações, sou também revisora activa de artigos submetidos a conferências e revistas, membro de comissões científicas e de organização de conferências. Recentemente, organizei a série de Workshops com o tema "Immersive Media Experiences" (2013-2015) na maior conferência na área de multimédia (ACM Multimedia). Participo frequentemente como perita da Comissão Europeia ou de organismos nacionais na avaliação de propostas de investigação. Os meus interesses de investigação centram-na na área dos sistema de comunicação multimedia, incluindo televisão e novos serviços, gestão de conteúdos, personalização e recomendação, novos formatos e conteúdos imersivos e interactivos.

Tópicos
de interesse
Detalhes

Detalhes

017
Publicações

2023

A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition

Autores
Guimaraes, V; Nascimento, J; Viana, P; Carvalho, P;

Publicação
APPLIED SCIENCES-BASEL

Abstract
When compared with traditional local shops where the customer has a personalised service, in large retail departments, the client has to make his purchase decisions independently, mostly supported by the information available in the package. Additionally, people are becoming more aware of the importance of the food ingredients and demanding about the type of products they buy and the information provided in the package, despite it often being hard to interpret. Big shops such as supermarkets have also introduced important challenges for the retailer due to the large number of different products in the store, heterogeneous affluence and the daily needs of item repositioning. In this scenario, the automatic detection and recognition of products on the shelves or off the shelves has gained increased interest as the application of these technologies may improve the shopping experience through self-assisted shopping apps and autonomous shopping, or even benefit stock management with real-time inventory, automatic shelf monitoring and product tracking. These solutions can also have an important impact on customers with visual impairments. Despite recent developments in computer vision, automatic grocery product recognition is still very challenging, with most works focusing on the detection or recognition of a small number of products, often under controlled conditions. This paper discusses the challenges related to this problem and presents a review of proposed methods for retail product label processing, with a special focus on assisted analysis for customer support, including for the visually impaired. Moreover, it details the public datasets used in this topic and identifies their limitations, and discusses future research directions of related fields.

2023

A Dataset for User Visual Behaviour with Multi-View Video Content

Autores
Soares Da Costa, T; Andrade, MT; Viana, P; Silva, NC;

Publicação
MMSys 2023 - Proceedings of the 14th ACM Multimedia Systems Conference

Abstract

2023

From a Visual Scene to a Virtual Representation: A Cross-Domain Review

Autores
Pereira, A; Carvalho, P; Pereira, N; Viana, P; Corte-Real, L;

Publicação
IEEE ACCESS

Abstract
The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an end-to-end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.

2023

Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection

Autores
Mosiichuk, V; Sampaio, A; Viana, P; Oliveira, T; Rosado, L;

Publicação
APPLIED SCIENCES-BASEL

Abstract
Liquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model's detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the & mu;SmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.

2023

Deep Learning Approach for Seamless Navigation in Multi-View Streaming Applications

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

Publicação
IEEE ACCESS

Abstract
Quality of Experience (QoE) in multi-view streaming systems is known to be severely affected by the latency associated with view-switching procedures. Anticipating the navigation intentions of the viewer on the multi-view scene could provide the means to greatly reduce such latency. The research work presented in this article builds on this premise by proposing a new predictive view-selection mechanism. A VGG16-inspired Convolutional Neural Network (CNN) is used to identify the viewer's focus of attention and determine which views would be most suited to be presented in the brief term, i.e., the near-term viewing intentions. This way, those views can be locally buffered before they are actually needed. To this aim, two datasets were used to evaluate the prediction performance and impact on latency, in particular when compared to the solution implemented in the previous version of our multi-view streaming system. Results obtained with this work translate into a generalized improvement in perceived QoE. A significant reduction in latency during view-switching procedures was effectively achieved. Moreover, results also demonstrated that the prediction of the user's visual interest was achieved with a high level of accuracy. An experimental platform was also established on which future predictive models can be integrated and compared with previously implemented models.

Teses
supervisionadas

2022

Semantic-aware Audio-Visual Representations for Multimedia Assets

Autor
Luís Miguel Salgado Nunes Vilaça

Instituição
IPP-ISEP

2022

Image Processing of Grocery Labels for Assisted Analysis

Autor
Jéssica Mireie Fernandes do Nascimento

Instituição
UP-FEUP

2022

Deep Learning for Automated Adequacy Assessment of Cervical Cytology Samples

Autor
VLADYSLAV MOSIICHUK

Instituição
IPP-ISEP

2022

Automatic Analysis of Grocery Product Labels

Autor
Vânia Cristina da Silva Ribeiro Guimarães

Instituição
UP-FCUP

2022

Comparação de Optimizadores de Deep Learning em Reconhecimento de Expressões Faciais

Autor
MIGUEL ANTÓNIO MADUREIRA FONTOURA ALVES

Instituição
IPP-ISEP