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Details

  • Name

    Tânia Fernandes Melo
  • Role

    Research Assistant
  • Since

    01st July 2017
  • Nationality

    Portugal
  • Contacts

    +351 22 209 4106
    tania.f.melo@inesctec.pt
001
Publications

2021

Efficient reactive obstacle avoidance using spirals for escape

Authors
Azevedo, F; Cardoso, JS; Ferreira, A; Fernandes, T; Moreira, M; Campos, L;

Publication
Drones

Abstract
The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

2020

IDRiD: Diabetic Retinopathy – Segmentation and Grading Challenge

Authors
Porwal, P; Pachade, S; Kokare, M; Deshmukh, G; Son, J; Bae, W; Liu, LH; Wang, J; Liu, XH; Gao, LX; Wu, TB; Xiao, J; Wang, FY; Yin, BC; Wang, YZ; Danala, G; He, LS; Choi, YH; Lee, YC; Jung, SH; Li, ZY; Sui, XD; Wu, JY; Li, XL; Zhou, T; Toth, J; Bara, A; Kori, A; Chennamsetty, SS; Safwan, M; Alex, V; Lyu, XZ; Cheng, L; Chu, QH; Li, PC; Ji, X; Zhang, SY; Shen, YX; Dai, L; Saha, O; Sathish, R; Melo, T; Araujo, T; Harangi, B; Sheng, B; Fang, RG; Sheet, D; Hajdu, A; Zheng, YJ; Mendonca, AM; Zhang, ST; Campilho, A; Zheng, B; Shen, D; Giancardo, L; Quellec, G; Meriaudeau, F;

Publication
Medical Image Analysis

Abstract

2020

Optic Disc and Fovea Detection in Color Eye Fundus Images

Authors
Mendonça, AM; Melo, T; Araújo, T; Campilho, A;

Publication
Lecture Notes in Computer Science - Image Analysis and Recognition

Abstract

2020

Microaneurysm detection in color eye fundus images for diabetic retinopathy screening

Authors
Melo, T; Mendonca, AM; Campilho, A;

Publication
Computers in Biology and Medicine

Abstract

2018

Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2

Authors
Ferreira, CA; Melo, T; Sousa, P; Meyer, MI; Shakibapour, E; Costa, P; Campilho, A;

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

Abstract
Breast cancer is one of the leading causes of female death worldwide. The histological analysis of breast tissue allows for the differentiation of the tissue suspected to be abnormal into four classes: normal tissue, benign tumor, in situ carcinoma and invasive carcinoma. Automatic diagnostic systems can help in that task. In this sense, this work propose a deep neural network approach using transfer learning to classify breast cancer histology images. First, the added top layers are trained and a second fine-tunning is done on some feature extraction layers that are frozen previously. The used network is an Inception Resnet V2. In order to overcome the lack of data, data augmentation is performed too. This work is a suggested solution for the ICIAR 2018 BACH-Challenge and the accuracy is 0.76 in the blind test set. © 2018, Springer International Publishing AG, part of Springer Nature.