Dr. Sergio Netto
Federal University of Rio de Janeiro
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Academic Career Length
35Years (1992-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 July 2020
Conference Paper
This work explores and compares the plethora of metrics for the performance evaluation of object-detection algorithms. Average precision (AP),for instance, is a popular metric for evaluating the accuracy of object detectors by estimating the area under the curve (AUC) of the precision × recall relationship. Depending on the point interpolation used in the plot, two different AP variants can be defined and, therefore, different results are generated. AP has six additional variants increasing the possibilities of benchmarking. The lack of consensus in different works and AP implementations is a problem faced by the academic and scientific communities. Metric implementations written in different computational languages and platforms are usually distributed with corresponding datasets sharing a given bounding-box description. Such projects indeed help the community with evaluation tools, but demand extra work to be adapted for other datasets and bounding-box formats. This work reviews the most used metrics for object detection detaching their differences, applications, and main concepts. It also proposes a standard implementation that can be used as a benchmark among different datasets with minimum adaptation on the annotation files.
Journal Article
Rafael Padilla
Wesley L Passos
Thadeu Luiz Barbosa DiasSergio NettoESEduardo A B da Silva
Eduardo A B da SilvaRecent outstanding results of supervised object detection in competitions and challenges are often associated with specific metrics and datasets. The evaluation of such methods applied in different contexts have increased the demand for annotated datasets. Annotation tools represent the location and size of objects in distinct formats, leading to a lack of consensus on the representation. Such a scenario often complicates the comparison of object detection methods. This work alleviates this problem along the following lines: (i) It provides an overview of the most relevant evaluation methods used in object detection competitions, highlighting their peculiarities, differences, and advantages; (ii) it examines the most used annotation formats, showing how different implementations may influence the assessment results; and (iii) it provides a novel open-source toolkit supporting different annotation formats and 15 performance metrics, making it easy for researchers to evaluate the performance of their detection algorithms in most known datasets. In addition, this work proposes a new metric, also included in the toolkit, for evaluating object detection in videos that is based on the spatio-temporal overlap between the ground-truth and detected bounding boxes.
1 January 2009
Book
PDPaulo S. R. Diniz
Paulo S. R. DinizESEduardo A B da Silva
Eduardo A B da SilvaSergio NettoThis new, fully-revised edition covers all the major topics of digital signal processing (DSP) design and analysis in a single, all-inclusive volume, interweaving theory with real-world examples and design trade-offs. Building on the success of the original, this edition includes new material on random signal processing, a new chapter on spectral estimation, greatly expanded coverage of filter banks and wavelets, and new material on the solution of difference equations. Additional steps in mathematical derivations make them easier to follow, and an important new feature is the do-it-yourself section at the end of each chapter, where readers get hands-on experience of solving practical signal processing problems in a range of MATLAB experiments. With 120 worked examples, 20 case studies, and almost 400 homework exercises, the book is essential reading for anyone taking DSP courses. Its unique blend of theory and real-world practical examples also makes it an ideal reference for practitioners.
1 March 2018 in Journal of the Franklin Institute
Journal Article
MMMatheus A. Marins
Matheus A. Marins
FRFelipe M.L. Ribeiro
Felipe M.L. RibeiroSergio NettoESEduardo A B da Silva
Eduardo A B da Silva1 February 2021 in Journal of Petroleum Science and Engineering
Journal Article
MMMatheus A. Marins
Matheus A. Marins
BBBettina D. Barros
Bettina D. Barros
ISIsmael H. Santos
Ismael H. Santos
DBDaniel C. Barrionuevo
Daniel C. Barrionuevo
RVRicardo E.V. Vargas
Ricardo E.V. Vargas
Sergio Netto +4
1 January 1995 in IEEE Transactions on Education
Journal Article
About Me
Sergio L. Netto was born in Rio de Janeiro, RJ, Brazil in 1967. He received the Electronics Engineer degree from the Federal University of Rio de Janeiro (UFRJ) in 1991; the M.Sc. degree in Electrical Engineering from COPPE/UFRJ in 1992, and the Ph.D. degree in Electrical Engineering from University of Victoria, BC, Canada, in 1996.
Since 1997, he has been with the undergraduate Department of Electronics and Computer Engineering at the UFRJ, and since 1998 he has also been with the graduate Program of Electrical Engineering at COPPE/UFRJ.
He is the co-author (with Paulo S. R. Diniz and Eduardo A. B. da Silva) of Digital Signal Processing: System Analysis and Design by Cambridge University Press, Cambridge, UK, 2nd edition, 2010, ISBN 978-0-521-88775-5.
In 2002/2003 he was the IEEE CASS Vice-President ellect for Region 9 (Latin America). In 2005 he was the Academic Chair at the PEE/COPPE/UFRJ. He is the co-recipient (with Miguel B. Furtado Jr., Paulo S. R. Diniz and Tapio Saramäki) of the Guillemin-Cauer Award from the IEEE Circuits and Systems Society (CASS) for the best paper published in the IEEE Transactions on Circuits and Systems, Part I (regular papers) in the year of 2005.
His teaching and research interests are in the fields of digital signal processing, adaptive filter, speech coding and synthesis, information theory and noise cancellation.