About the Conference
The 27th International Conference on Systems, Signals and Image Processing, IWSSIP 2020, Niterói, RJ, Brazil, initially scheduled for June 3 to 5 was held from July 1 to 3, 2020 to accommodate both in person as well as online presentations due to restrictions on travel and mobility related to the new coronavirus (COVID-19).
Protecting the health, safety, and well-being of attendees is paramount. For this reason, the conference was held in the Buildings of the Institute of Computing - IC, inside the campus of the Fluminense Federal University – UFF following the recommendations of maximum persons per each room and all other health orientations of the Niterói city and Brazilian Federal government. The Auditorium, video conference rooms, laboratories, internet access, and computer facilities of the IC/UFF was available to attendees that can and desire be there during the event. Remote attendance and presentations by the internet are also an available options in case of traveling restriction due to coronavirus.
It is essential to highlight that if the Brazilian government imposes more restrictive health orientations in the following weeks, organizers had to transform the in-person component of IWSSIP 2020 into an all-digital conference experience.
IWSSIP is an international conference that brings together researchers and developers from both academia and industry reporting the latest scientific and theoretical advances to discuss and debate major issues and state-of-the-art aspects on systems, signals and image processing. The IWSSIP in Niterói, this year, followed the success of previous occurrences in Bratislava, Bucharest, Budapest, Chalkida, Dubrovnik, London, Manchester, Maribor, Osijek, Poznan, Prague, Rio de Janeiro, Sarajevo, Vienna, and Zagreb.
The program included invited lectures, tutorials, video or online presentations of peer-reviewed accepted papers of the conference or the Smart Cities special session.
About Our LOGO
The logo of IWSSIP 2020 is based on the landscape of the region (as the IWSSIP 2010 logo). It represents the view of the Museum of Contemporary Art - MAC from “Arrows Beach” (“Praia das Flechas”, in Portuguese) in Niterói. The main symbol of the Rio de Janeiro state appears in the background: it is the “Corcovado” mountain where there is the Christ the Redeemer Statue on the top.
The logo of IWSSIP 2020 was created by M. Monteiro when we decide to host the event in Niterói. The initial plan was that it was in the Hotel H that is located precisely in this place. But with the spread of the Corona Virus, all have had to change the previous agenda all over the world.
Designed by Brazilian architect Oscar Niemeyer, the MAC has become the symbol of the city of Niterói. The “Corcovado” mountain is on the other side of the Guanabara Bay in the neighboring city of Rio de Janeiro (same name of this Brazilian state).
Rio de Janeiro was the venue of the IWSSIP, 10 years ago when it took place in a hotel in Flamengo Beach. That time the IWSSIP logo was the view of the same mountain but from a boat in front of Flamengo Beach. The 2010 logo and IWSSIP 2010 proceedings can still be seen at http://www.ic.uff.br/iwssip2010/
Khalifa University of Science and Technology, UAE
Robotics is breaking free from constrained environments to physically interact with a variety of objects and humans. This advanced level of interaction can be supported by multi-sensor systems capable of perceiving the objects around them, changes to the environment, and the actions of human users. Machine touch, in particular, can play a key role within the context of a multi-sensor object recognition system, enabling robustness to illumination changes and occlusions, extraction of object properties, etc. The majority of tactile recognition systems are based on grasping, however, recently, object contact through a continuous tactile sensor, i.e., artificial skin, has emerged as a powerful alternative.
In this lecture, we will discuss artificial skin sensing using Electrical Impedance Tomography (EIT). For a set of typical EIT measurements, one or more rings of electrodes are placed around the medium that an object is positioned. A small current is injected into an electrode pair and the voltages between the other pairs of electrodes are recorded. EIT is capable of simultaneously detecting multiple objects, while providing a richer, i.e., image-based, description of their properties. This offers a number of advantages, e.g., low cost, wire-free, good temporal resolution, and, in the context of tactile sensing, a straightforward interpretation of pressure information. Specifically, when pressure is applied to a pressure-sensitive artificial skin, the surface of the skin deforms, resulting to changes in its conductivity distribution, which can be represented as an image through EIT.
To empower EIT for tactile sensing, machine learning is used to solve the complex problem of image reconstruction through patch-based decomposition. Determining the location of the tactile stimulus is posed as an image segmentation process, which is addressed using transfer learning. Finally, features extracted from the segmented objects are used for classification with state-of-the-art machine learning methods.
Dr. Panos Liatsis is a Professor in the Department of Electrical Engineering and Computer Science at Khalifa University of Science and Technology. Prior to joining Khalifa University, he was Professor of Image Processing and Head of Department of Electrical and Electronic Engineering at City, University of London.
He received the Diploma degree in Electrical Engineering from the University of Thrace in Greece and the Ph.D. degree in Electrical Engineering & Electronics from the University of Manchester, UK.
Dr. Liatsis' research interests include machine learning, pattern recognition, and computer vision, with applications in biomedical image and signal processing, security, and intelligent transportation systems.
He published over 200 research contributions in high-impact factor journals, books and international conference proceedings. He is on the editorial boards of international journals and a member of the International Program Committees of international conferences in the fields of image processing, machine learning and intelligent systems.
Google Inc. CA, USA
Over the last half decade, the computer vision field has made huge strides on core image understanding tasks such as object recognition, detection, segmentation and keypoint estimation -- today these are technologies that are ubiquitously available to everyday consumers. However, video understanding technology is relatively less pervasive as computer vision models must deal with more difficult appearance, less labeled data, and computational challenges. In this talk, I will cover some of what I consider to be the core challenges of computer vision for video understanding, discussing these in the context of several recent research projects by the Perception team at Google.
Jonathan Huang is a senior research scientist at Google and currently works on deep learning for machine perception. He received his M.Sc degree and Ph.D from the School of Computer Science at Carnegie Mellon University in 2008 and 2011 respectively. From 2011 to 2014 he was an NSF Computing Innovation (CI) postdoctoral fellow at the geometric computing group at Stanford University where he also received his B.S. degree in Mathematics in 2005. His research interests lie primarily in deep learning, and probabilistic reasoning with combinatorially structured data with applications in computer vision and online education. To see a list of publications and projects, visit http://www.jonathan-huang.orga>.
Javier Felip Leon
Intel Labs, OR, USA
Visual scene understanding involves estimating quantities of interest (such as object category, location, etc.) from observed images and videos. Bayesian approaches to such problems are gaining importance as these provide uncertainty estimates of the variables being estimated. However, these tend to be expensive and unfeasible for real-time implementation. We present a framework for efficiently solving such problems in real-time using probabilistic analysis-by-synthesis (Approximate Bayesian Computations). The underlying generative models needed for ABC are built from realistic off-the-shelf simulation software but additionally include a simple Bayesian error model for the gap between simulation outputs and real data. We achieve real-time performance by introducing two novel approaches: neural emulators and tree pyramidal importance sampling.
Dr. Javier Felip Leon is a Research Scientist at Intel Labs since 2016. Javier completed his Ph.D. on Computer Science at Universitat Jaume I (Spain) in early 2016, where his research focused on Robotic manipulation under uncertainty (some robot videos here). Currently, his research focuses on analysis-by-synthesis, applied probabilistic methods, sampling methods and intuitive physics. Javier has authored more than 20 peer-reviewed paper publications in conferences and journals such as ICRA, IROS, HUMANOIDS and RAS.
Aristófanes Corrêa Silva
Federal University of Maranhão, UFMA, Brazil
Luis Fernando Marin Sepulveda
Pontifical Catholic University of Rio de Janeiro, PUC-Rio, Brazil
In recent decades, advances in capture devices and the increase of available digital image data have stimulated the creation of methodologies for data processing that produce various forms of valuable models, such as descriptors, classifiers, approximations and visualizations. These models are often developed in the field of machine learning, which is characterized by a large number of available algorithms. These algorithms often do not have guidelines to identify the most appropriate one based on specific data to which they will be applied and the nature of the problem under analysis.
There is a knowledge that allows to relate the features of the algorithms and data that present a good performance to fulfill a specific task, known as Meta-Knowledge, which can include information on algorithms, evaluation metrics to calculate similarity of datasets or relation of tasks. Being Meta-Learning the study of methods based on principles that explore the Meta-Knowledge to obtain efficient models and solutions, adapting the processes of Machine Learning and Data Mining.
The research carried out in this work analyzes the applications and advantages offered by Meta-Learning in the field of digital image processing. To carry out this task, different types of images, characterizers, and feature analysis techniques are used.
The results obtained show that methodology based on Meta-Learning is efficient when applied in the processing of digital images for identification and storage of experience generated by developing methodologies for classification of different types of images, obtaining a high performance with respect to an evaluation metrics. This statement means that Meta-Learning allows recommending the most appropriate methodology to perform the processing of a specific type of image based on features of the dataset under analysis and the type of specific task to be performed.
Aristófanes Corrêa Silva received a Ph.D. degree in Informatics from Pontifical Catholic University of Rio de Janeiro – Brazil in 2004. Currently, he is a Full Professor at the Federal University of Maranhão (UFMA), Brazil. He teaches image processing, pattern recognition and programming language. His research interests include image processing, image understanding, medical image processing, machine vision, artificial intelligence, pattern recognition and, machine learning. Prof. Aristófanes has authored more than 80 peer-reviewed paper publications in scientific journals.
Luis Fernando Marin was a Ph.D. student in computer science at the Pontifical Catholic University of Rio de Janeiro (PUC-RIO). He received his engineering degree from the University of Antioquia in Colombia and his master's degree from the Federal University of Maranhão, both with an emphasis in computer science. His research interest includes image processing, image understanding, medical image processing, machine vision, artificial intelligence, pattern recognition and, machine learning.
André Borges Cavalcante
Federal University of Maranhão, UFMA, Brazil
Interpretability of Machine Learning Models: Application for Lawsuits Prediction in the Energy Sector +
Machine learning can be regarded as computational techniques for learning probability distributions from data. Interpretable machine learning refers, however, to methodologies that make the learned information understandable to humans. In this tutorial, It will be overviewed the interpretability concepts and methods and their application to lawsuit prediction in the energy sector.
André B. Cavalcante is professor at Federal University of Maranhão, Brazil. He received his Doctorate degree in Information Science in 2015 from Nagoya University, Japan. His research interest includes unsupervised generative learning, and its applications to vision.
Faculty of Electrical Engineering and Computing
University of Zagreb, Croatia
Aura Conci, UFF
Leandro A. F. Fernandes, UFF
Anselmo C. Paiva, UFMA
Erick S. Delvizio, IEEE RJ Section
Geraldo Braz Jr., UFMA
João Dallyson S. Almeida, UFMA
Leandro A. F. Fernandes, UFF
Renato Manuel Natal Jorge, FEUP
International Program Committee
- Aggelos Katsaggelo, USA
- Ángel Sánchez Calle, Spain
- Aura Conci, Brazil
- Boris Šimak, Czech Republic
- Branka Zovko-Cihlar, Croatia
- Dimitrios Karras, Greece
- Dušan Gleich, Slovenia
- Ebroul Izquierdo, UK
- Erich Leitgeb, Austria
- Fabiana Leta, Brazil
- Galya Marinova, Bulgaria
- Gregor Rozinaj, Slovakia
- Jan Cornelis, Belgium
- Ján Turán, Slovakia
- Krzysztof Wajda, Poland
- Marek Domański, Poland
- Markus Rupp, Austria
- Marta Mrak, UK
- Martin Slanina, Czech Republic
- Mislav Grgić, Croatia
- Narcis Behlilovic, Bosnia and Herzegovina
- Panos Liatsis, UAE
- Pavol Podhradsky, Slovakia
- Peter Planinšič, Slovenia
- Ratislav Lukac, Canada
- Rodica Tuduce, Romania
- Ryszard Stasiński, Poland
- Snježana Rimac-Drlje, Croatia
- Sonja Grgić, Croatia
- Stamatis Voliotis, Greece
- Theodore Zahariadis, Greece
- Tomasz Grajek, Poland
- Touradj Ebrahimi, Switzerland
- Yo-Sung Ho, Korea
- Žarko Čučej, Slovenia
Local Program Committee
- Adriel dos Santos Araújo, UFF
- Alan Kubrusly, PUC-Rio
- Anna De Falco, PUC-Rio
- Anselmo Cardoso de Paiva, UFMA
- Antônio J. Silva Neto, UERJ
- Artur L. Andrade, UFF
- Célio V. Neves de Albuquerque, UFF
- Creto Vidal, UFC
- Débora Saade, UFF
- Diego Brandão, CEFET/RJ
- Djenane Pamplona, PUC-Rio
- Eldman O. Nunes, UNIFACS
- Esteban Clua, UFF
- Evandro Ottoni Salles, UFES
- Fátima Nunes, USP
- Flávia Cristine H. Pastura, INT/MCTIC
- Flávio Luiz Seixas, UFF
- Geraldo Braz Jr., UFMA
- Gilson Giraldi, LNCC
- João Dallyson S. Almeida, UFMA
- José Ramon, UFF
- José Raphael Bokehi, UFF
- Leandro A. F. Fernandes, UFF
- Luiz Marcos G. Gonçalves, UFRN
- Maira B. M. Hernandes, UFF
- Maria C. P. L. Zamberlan, INT/MCTIC
- Mateus Monteiro, UFF
- Natalia Castro Fernandes, UFF
- Oumar Diene, UFRJ/IEEE RJ Section
- Raul Queiroz Feitosa, PUC-Rio
- Roger Resmini, UFMT
- Simone Vasconcelos, IFF
- Vania Vidal, UFC
- Yona Lopes, UFF
- Nadia Nedjah, UERJ, Brazil
- Snježana Rimac-Drlje, Josip Juraj Strossmayer University of Osijek, Croatia
Divulgation and Communication Committee
- Adriel Santos, UFF
- Anna De Falco, PUC-Rio
- Dorimar Cardoso Tirre, UFF
- Mateus Monteiro, UFF