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About

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

  • Name

    Paula Viana
  • Role

    Area Manager
  • Since

    01st January 1993
018
Publications

2024

A Machine Learning App for Monitoring Physical Therapy at Home

Authors
Pereira, B; Cunha, B; Viana, P; Lopes, M; Melo, ASC; Sousa, ASP;

Publication
SENSORS

Abstract
Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient's travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient's body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.

2023

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

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

Publication
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

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

Publication
PROCEEDINGS OF THE 2023 PROCEEDINGS OF THE 14TH ACM MULTIMEDIA SYSTEMS CONFERENCE, MMSYS 2023

Abstract
Immersive video applications impose unpractical bandwidth requirements for best-effort networks. With Multi-View(MV) streaming, these can be minimized by resorting to view prediction techniques. SmoothMV is a multi-view system that uses a non-intrusive head tracking mechanism to detect the viewer's interest and select appropriate views. By coupling Neural Networks (NNs) to anticipate the viewer's interest, a reduction of view-switching latency is likely to be obtained. The objective of this paper is twofold: 1) Present a solution for acquisition of gaze data from users when viewing MV content; 2) Describe a dataset, collected with a large-scale testbed, capable of being used to train NNs to predict the user's viewing interest. Tracking data from head movements was obtained from 45 participants using an Intel Realsense F200 camera, with 7 video playlists, each being viewed a minimum of 17 times. This dataset is publicly available to the research community and constitutes an important contribution to reducing the current scarcity of such data. Tools to obtain saliency/heat maps and generate complementary plots are also provided as an open-source software package.

2023

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

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

Publication
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

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

Publication
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.

Supervised
thesis

2022

Telco Voice Traffic: Database Construction and Visualization

Author
Inês Alexandra Cunha Ferreira

Institution
UP-FEP

2022

Identification and extraction of floor planes for 3D representation

Author
Carlos Miguel Guerra Soeiro

Institution
UP-FEUP

2022

Predicting Portuguese Local Elections Outcome: A Machine Learning approach

Author
Diogo João Teixeira Pinto da Costa

Institution
UP-FEP

2021

Improving quality and agility of safety-critical software development using domain-specific languages

Author
João Ricardo Faria Mendes Almeida Reis

Institution
UP-FEUP

2021

Utilização da câmara smartphone para monitorizar a aderência à terapia inalatória

Author
SOFIA ALEXANDRA GONÇALVES FERRAZ

Institution