Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Sobre

Sobre

Ricardo Cruz trabalhou em vários de tópicos de machine learning, com particular ênfase em aspectos teóricos de deep learning e visão computacional – com mais de 20 publicações e mais de 100 citações em tópicos como: • adaptação de modelos de ranking para class imbalance; • tornar as redes neurais convolucionais invariantes ao background; • torná-las mais rápidos ajustando o esforço computacional a cada imagem; • losses para regressão ordinal. É investigador pós-doutorado em condução autónoma na Faculdade de Engenharia da Universidade do Porto e investigador no INESC TEC desde 2015, onde a sua investigação lhe valeu o doutoramento em ciências da computação em 2021. É licenciado em informática. ciências e um mestrado em matemática aplicada. É frequentemente convidado para lecionar na Faculdade de Engenharia da Universidade do Porto, onde obteve um prémio pedagógico.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Ricardo Pereira Cruz
  • Cargo

    Investigador Auxiliar
  • Desde

    01 outubro 2013
  • Nacionalidade

    Portugal
  • Contactos

    +351222094299
    ricardo.p.cruz@inesctec.pt
001
Publicações

2024

Condition Invariance for Autonomous Driving by Adversarial Learning

Autores
e Silva, DT; Cruz, PM;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Object detection is a crucial task in autonomous driving, where domain shift between the training and the test set is one of the main reasons behind the poor performance of a detector when deployed. Some erroneous priors may be learned from the training set, therefore a model must be invariant to conditions that might promote such priors. To tackle this problem, we propose an adversarial learning framework consisting of an encoder, an object-detector, and a condition-classifier. The encoder is trained to deceive the condition-classifier and aid the object-detector as much as possible throughout the learning stage, in order to obtain highly discriminative features. Experiments showed that this framework is not very competitive regarding the trade-off between precision and recall, but it does improve the ability of the model to detect smaller objects and some object classes. © 2024, Springer Nature Switzerland AG.

2023

Rethinking low-cost microscopy workflow: Image enhancement using deep based Extended Depth of Field methods

Autores
Albuquerque, T; Rosado, L; Cruz, RPM; Vasconcelos, MJM; Oliveira, T; Cardoso, JS;

Publicação
Intell. Syst. Appl.

Abstract
Microscopic techniques in low-to-middle income countries are constrained by the lack of adequate equipment and trained operators. Since light microscopy delivers crucial methods for the diagnosis and screening of numerous diseases, several efforts have been made by the scientific community to develop low-cost devices such as 3D-printed portable microscopes. Nevertheless, these devices present some drawbacks that directly affect image quality: the capture of the samples is done via mobile phones; more affordable lenses are usually used, leading to poorer physical properties and images with lower depth of field; misalignments in the microscopic set-up regarding optical, mechanical, and illumination components are frequent, causing image distortions such as chromatic aberrations. This work investigates several pre-processing methods to tackle the presented issues and proposed a new workflow for low-cost microscopy. Additionally, two new deep learning models based on Convolutional Neural Networks are also proposed (EDoF-CNN-Fast and EDoF-CNN-Pairwise) to generate Extended Depth of Field (EDoF) images, and compared against state-of-the-art approaches. The models were tested using two different datasets of cytology microscopic images: public Cervix93 and a new dataset that has been made publicly available containing images captured with µSmartScope. Experimental results demonstrate that the proposed workflow can achieve state-of-the-art performance when generating EDoF images from low-cost microscopes. © 2022 The Author(s)

2023

Two-Stage Framework for Faster Semantic Segmentation

Autores
Cruz, R; Silva, DTE; Goncalves, T; Carneiro, D; Cardoso, JS;

Publicação
SENSORS

Abstract
Semantic segmentation consists of classifying each pixel according to a set of classes. Conventional models spend as much effort classifying easy-to-segment pixels as they do classifying hard-to-segment pixels. This is inefficient, especially when deploying to situations with computational constraints. In this work, we propose a framework wherein the model first produces a rough segmentation of the image, and then patches of the image estimated as hard to segment are refined. The framework is evaluated in four datasets (autonomous driving and biomedical), across four state-of-the-art architectures. Our method accelerates inference time by four, with additional gains for training time, at the cost of some output quality.

2023

Two-Stage Semantic Segmentation in Neural Networks

Autores
Silva, DTE; Cruz, R; Goncalves, T; Carneiro, D;

Publicação
FIFTEENTH INTERNATIONAL CONFERENCE ON MACHINE VISION, ICMV 2022

Abstract
Semantic segmentation consists of classifying each pixel according to a set of classes. This process is particularly slow for high-resolution images, which are present in many applications, ranging from biomedicine to the automotive industry. In this work, we propose an algorithm targeted to segment high-resolution images based on two stages. During stage 1, a lower-resolution interpolation of the image is the input of a first neural network, whose low-resolution output is resized to the original resolution. Then, in stage 2, the probabilities resulting from stage 1 are divided into contiguous patches, with less confident ones being collected and refined by a second neural network. The main novelty of this algorithm is the aggregation of the low-resolution result from stage 1 with the high-resolution patches from stage 2. We propose the U-Net architecture segmentation, evaluated in six databases. Our method shows similar results to the baseline regarding the Dice coefficient, with fewer arithmetic operations.

2023

Interpretability-Guided Human Feedback During Neural Network Training

Autores
Serrano e Silva, P; Cruz, R; Shihavuddin, ASM; Gonçalves, T;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract