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Sobre

Sobre

Hélder P. Oliveira Hélder P. Oliveira was born in Porto, Portugal, in 1980. He graduated in Electrical and Computer Engineering in 2004, received the M.Sc. degree in Automation, Instrumentation and Control in 2008 and the Ph.D. degree in Electrical and Computer Engineering in 2013 at the Faculty of Engineering of the University of Porto (FEUP), Portugal. He is currently working as Senior Researcher at INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, a R&D institute affiliated to the University of Porto. He is the Leader of the Visual Computing and Machine Intelligence Area, member of the coordination council of the Centre for Telecommunications and Multimedia, and takes part of the Breast Research Group. He is also one of the coordinators of the Data Science Hub at INESC TEC. He is also working at the Computer Science Department of the Faculty of Sciences of the University of Porto as an Invited Assistant Professor. Between 2014 and 2016 he was contracted as Invited Assistant Professor at Informatics Engineering Department of FEUP. Previously between 2008 and 2011 was working as Invited Assistant in the same Faculty and Department. Hélder Oliveira is the principal investigator in 2 funded research projects (LuCaS, MICOS), project member in 4 projects (S-MODE, HEMOSwimmers, LEGEM and TAMI). In the past was also project member in 5 other funded projects (one European and 4 National) and 3 other as research assistant. He was also responsible at INESC TEC for other 2 projects related with technological transfer with industry, the project Evo3DModel with Adapttech - Adaptation Technologies and the project FollicleCounter with Saúde Viável. He was the founder member and coordinator (between 2010 and 2013) of the Bio-related Image Processing and Analysis Student’s Group (BioStar) at FEUP. Since 2007 I have co-authored 20 peer-reviewed papers and 8 journal abstracts. I have 1 patent conceded (Europe, China, Japan), 3 book chapters and also 64 works in international conferences, 40 articles in national refereed conferences and participated in the creation of 3 public datasets. In total, these publications have attracted 748 citations, with h-index of 14 according to Harzing’s Publish or Perish application on March 30, 2021. He was one of the mentors and belonged to the organizer committee of the VISion Understanding and Machine Intelligence (VISUM) summer school in 6 editions of the event. He also participated in the organization of other 12 events and was invited as keynote speaker in 3 international events. Hélder Oliveira is currently supervising 6 PhD Students, and has 1 Phd Student concluded as supervisor in 2018. During his career supervised (or co-supervised) 56 MSc students. Currently supervises 4 research fellows in projects at INESC TEC. Hélder Oliveira participated as principal jury in 2 PhD and 15 MSc defences as principal examiner. Hélder Oliveira is member of Portuguese Association of Pattern Recognition (APRP) and was been elected for president of the fiscal council in 2017. His research interests include medical image analysis, bio-image analysis, computer vision, image and video processing, machine learning, data science, computer science, programming, and 3D modelling.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Hélder Filipe Oliveira
  • Cargo

    Investigador Auxiliar
  • Desde

    01 novembro 2008
021
Publicações

2024

Systematic review on weapon detection in surveillance footage through deep learning

Autores
Santos, T; Oliveira, H; Cunha, A;

Publicação
COMPUTER SCIENCE REVIEW

Abstract
In recent years, the number of crimes with weapons has grown on a large scale worldwide, mainly in locations where enforcement is lacking or possessing weapons is legal. It is necessary to combat this type of criminal activity to identify criminal behavior early and allow police and law enforcement agencies immediate action.Despite the human visual structure being highly evolved and able to process images quickly and accurately if an individual watches something very similar for a long time, there is a possibility of slowness and lack of attention. In addition, large surveillance systems with numerous equipment require a surveillance team, which increases the cost of operation. There are several solutions for automatic weapon detection based on computer vision; however, these have limited performance in challenging contexts.A systematic review of the current literature on deep learning-based weapon detection was conducted to identify the methods used, the main characteristics of the existing datasets, and the main problems in the area of automatic weapon detection. The most used models were the Faster R-CNN and the YOLO architecture. The use of realistic images and synthetic data showed improved performance. Several challenges were identified in weapon detection, such as poor lighting conditions and the difficulty of small weapon detection, the last being the most prominent. Finally, some future directions are outlined with a special focus on small weapon detection.

2024

Comparative Study Between Object Detection Models, for Olive Fruit Fly Identification

Autores
Victoriano, M; Oliveira, L; Oliveira, HP;

Publicação
Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2024, Volume 2: VISAPP, Rome, Italy, February 27-29, 2024.

Abstract
Climate change is causing the emergence of new pest species and diseases, threatening economies, public health, and food security. In Europe, olive groves are crucial for producing olive oil and table olives; however, the presence of the olive fruit fly (Bactrocera Oleae) poses a significant threat, causing crop losses and financial hardship. Early disease and pest detection methods are crucial for addressing this issue. This work presents a pioneering comparative performance study between two state-of-the-art object detection models, YOLOv5 and YOLOv8, for the detection of the olive fruit fly from trap images, marking the first-ever application of these models in this context. The dataset was obtained by merging two existing datasets: the DIRT dataset, collected in Greece, and the CIMO-IPB dataset, collected in Portugal. To increase its diversity and size, the dataset was augmented, and then both models were fine-tuned. A set of metrics were calculated, to assess both models performance. Early detection techniques like these can be incorporated in electronic traps, to effectively safeguard crops from the adverse impacts caused by climate change, ultimately ensuring food security and sustainable agriculture. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

2024

Radiological Medical Imaging Annotation and Visualization Tool

Autores
Teiga, I; Sousa, J; Silva, F; Pereira, T; Oliveira, HP;

Publicação
Universal Access in Human-Computer Interaction - 18th International Conference, UAHCI 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Washington, DC, USA, June 29 - July 4, 2024, Proceedings, Part III

Abstract
Significant medical image visualization and annotation tools, tailored for clinical users, play a crucial role in disease diagnosis and treatment. Developing algorithms for annotation assistance, particularly machine learning (ML)-based ones, can be intricate, emphasizing the need for a user-friendly graphical interface for developers. Many software tools are available to meet these requirements, but there is still room for improvement, making the research for new tools highly compelling. The envisioned tool focuses on navigating sequences of DICOM images from diverse modalities, including Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, Ultrasound (US), and X-rays. Specific requirements involve implementing manual annotation features such as freehand drawing, copying, pasting, and modifying annotations. A scripting plugin interface is essential for running Artificial Intelligence (AI)-based models and adjusting results. Additionally, adaptable surveys complement graphical annotations with textual notes, enhancing information provision. The user evaluation results pinpointed areas for improvement, including incorporating some useful functionalities, as well as enhancements to the user interface for a more intuitive and convenient experience. Despite these suggestions, participants praised the application’s simplicity and consistency, highlighting its suitability for the proposed tasks. The ability to revisit annotations ensures flexibility and ease of use in this context. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

2023

Special Issue on Novel Applications of Artificial Intelligence in Medicine and Health

Autores
Pereira, T; Cunha, A; Oliveira, HP;

Publicação
APPLIED SCIENCES-BASEL

Abstract
Artificial Intelligence (AI) is one of the big hopes for the future of a positive revolution in the use of medical data to improve clinical routine and personalized medicine [...]

2023

Learning Models for Bone Marrow Edema Detection in Magnetic Resonance Imaging

Autores
Ribeiro, G; Pereira, T; Silva, F; Sousa, J; Carvalho, DC; Dias, SC; Oliveira, HP;

Publicação
APPLIED SCIENCES-BASEL

Abstract
Bone marrow edema (BME) is the term given to the abnormal fluid signal seen within the bone marrow on magnetic resonance imaging (MRI). It usually indicates the presence of underlying pathology and is associated with a myriad of conditions/causes. However, it can be misleading, as in some cases, it may be associated with normal changes in the bone, especially during the growth period of childhood, and objective methods for assessment are lacking. In this work, learning models for BME detection were developed. Transfer learning was used to overcome the size limitations of the dataset, and two different regions of interest (ROI) were defined and compared to evaluate their impact on the performance of the model: bone segmention and intensity mask. The best model was obtained for the high intensity masking technique, which achieved a balanced accuracy of 0.792 +/- 0.034. This study represents a comparison of different models and data regularization techniques for BME detection and showed promising results, even in the most difficult range of ages: children and adolescents. The application of machine learning methods will help to decrease the dependence on the clinicians, providing an initial stratification of the patients based on the probability of edema presence and supporting their decisions on the diagnosis.

Teses
supervisionadas

2023

Contributions for the Olive Fruit Fly detection using Deep Learning approaches

Autor
Ana Margarida Mendes Antunes Martins Victoriano

Instituição
INESCTEC

2023

Vision-based AI Approach for the Real-Time Detection of Compensatory Movements during Upper Limb Rehabilitation

Autor
Cláudia Daniela Costa Rocha

Instituição
INESCTEC

2023

A transfer learning approach for predicting breast cancer hormonal receptor status with MRI data

Autor
Ana Isabel Pontes Silva

Instituição
INESCTEC

2023

Design and Development of a clinical annotation and visualization tool

Autor
Inês Castro Teiga

Instituição
INESCTEC

2023

Enhancing Image Consistency in CT Scans: A PIX2PIX- Based Framework for Cross-Modality Transformation

Autor
Bruno Alberto Carvalho Malta

Instituição
INESCTEC