academic background

Universitat Autonoma de Barcelona (Centro de Visión por Computador)

Universidade Federal de São Carlos, Brazil

Universidade Federal de São Carlos, Brazil

professional experience

Xerox Research Centre Europe, France

DaitanGroup, Brazil

Universidade Federal de São Carlos, Brazil

Daitan Labs, Brazil

Universidade Federal de São Carlos, Brazil

skills

Machine learning and artificial intelligence; sequence classifiers (Markov models, Conditional Random Fields and their latent-variable variants); generative and discriminative models, neural networks (first order and second order optimization methods, first-generation deep learning [based on restricted Boltzmann machines]), support vector machines (multi-class and multi-label strategies, DDAGs, statistical learning theory with main expertise on Kernel Support vector Machines); general scientific computing (research and experimental workflow, parallel processing) and specialized data structures (KD-Trees);

Computer Vision and Image processing; feature extraction (HOGs, FREAK, SURF, Haralick, Local Binary Patterns); corners detection (Harris, FAST, Susan); feature matching (kNN, correlation matching), homography estimation; image moments, bag of visual words, RANSAC, computer vision (color and template based object trackers, object detectors, filters, boosted classifiers).

selected publications

In this paper, published in the International Journal of Cardiology by Elsevier, we described a study on the use of artificial neural networks to predict cardiovascular (CV) death in patients with heart failure (HF). We compared the applicability of relatively modern techniques in the medical field, in comparison with the standard Cox’s proportional hazard models and common regression techniques mostly found in the medical literature.

CR Souza, EB Pizzolato, Brazilian Sign Language Recognition with Support Vector Machines and Hidden Conditional Random Fields Machine Learning and Data Mining in Pattern Recognition – MLDM 2013. Lecture Notes in Computer Science Volume 7988, 2013, pp 84-98, Springer Berlin, 2013.

In this study, we had shown how to recognize freely articulated sign words using Support Vector Machines and Hidden Conditional Random Fields in the Brazilian Sign Language (Libras). We performed the classification of natural signed words in unconstrained backgrounds without the aid of gloves or wearable tracking devices. We have demonstrated that feature vectors extracted from depth information and based on linguistic investigations could be effective for this task. This paper has been selected as one of the three best papers published in the conference.

CR Souza, EB Pizzolato, MS Anjo, Fingerspelling Recognition with Support Vector Machines and Hidden Conditional Random Fields Advances in Artificial Intelligence – IBERAMIA 2012. Lecture Notes in Computer Science Volume 7637, 2012, pp 561-570, Springer Berlin, 2012.

In this paper, we investigated how to use Hidden Conditional Random Fields and Support Vector Machines in the problem of fingerspelling recognition in Libras. We achieved much faster evaluation rates using large-margin Directed Acyclic Graphs to perform multi-class classification in SVMs. We provided comparisons against more common approaches based on Artificial Neural Networks and Hidden Markov Models.

J Myers, CR Souza, A Borghi-Silva et al., A Neural Network Approach to Predicting Outcomes in Heart Failure using Cardiopulmonary Exercise Testing (abstract) Journal of the American College of Cardiology, v. 61, p. E1415, 2013. March 12, 2013

Short version of the first publication, for the Journal of the American College of Cardiology.

S Feuerstack, JH Colnago, CR Souza et al., Designing and Executing Multimodal Interfaces for the Web based on State Chart XML Proceedings of The 3rd W3C Brazil Web Conference, Rio de Janeiro, Brazil

In this paper, we had shown how to create and specify multi-modal interfaces using the state-chart XML language specification. In this paper, we created and tested a computer vision based module to control an interface for automatically turning musical sheets using visual gestures.

CR Souza, EB Pizzolato, R Mendes et al., Artificial Neural Networks Prognostic Evaluation of Post-Surgery Complications in Patients Underwent To Coronary Artery Bypass Graft Surgery 18th International Conference on Machine Learning and Applications (ICMLA’09), 2009, Miami Beach, FL

In this study, we evaluated the use of artificial neural networks in the prediction of post-surgery complications in CABG, such as the need for patient re-intubation, exposure to prolonged mechanical ventilation times and mortality risk increase.

 honors and awards

The CodeProject, 2013Prizewinner article in CodeProject’s Android article competition of November 2013. We describe how to use the Accord.NET Framework to build applications for Android, enabling researchers and developers to apply machine learning and image processing techniques in embedded systems.

Haar-feature Object Detection in C#. The CodeProject, 2012

Prizewinner article in CodeProject’s C# article competition of August 2012. A reimplementation of the Haar cascade recognition system from Viola & Jones in .NET, compatible with OpenCV’s XML definition files, employing a few techniques for faster recognition and featuring a multi-threaded based implementation.

Handwriting Recognition using Kernel Discriminant Analysis. The Code Project, 2010

Double prizewinner article in CodeProject’s C# article competition and best overall article of May 2010. Describes how to use the Kernelized version of Linear Discrimination Analysis to obtain a Non-linear discriminant analysis applicable to handwriting recognition.

patents

US application US 14/630,227. February 24th, 2015

Method and system for simulating users in the context of a parking lot based on the automatic learning of a user-choice decision function from historical data considering multiple user behavior profiles.

work relevance and impact

1. S. Ruffieux, D. Lalanne, E. Mugellini, O. A. Khaled. Gesture recognition corpora and tools: A scripted ground truthing method. Computer Vision and Image Understanding, Volume 131, Pages 72-87, ISSN 1077-3142, 2015.
2. C. Schneider, A. Barker, and S. Dobson. Autonomous Fault Detection in Self-Healing Systems: Comparing Hidden Markov Models and Artificial Neural Networks. In Proceedings of International Workshop on Adaptive Self-tuning Computing Systems (ADAPT ’14). ACM, New York, NY, USA, 2014.
3. H. Josiński, A. Świtoński, A. Michalczuk, D. Kostrzewa, K. Wojciechowski. Human Identification Based on Gait Video Sequences, In: Proceedings of the International Conference on Computer Science and Engineering, p. 312-317, 2013
4. H. Josiński, D. Kostrzewa, A. Michalczuk, A. Świtoński, K. Wojciechowski. Feature Extraction and HMM-based Classification of Gait Video Sequences for the Purpose of Human Identification. In: Vision Based Systems for UAV Applications. Studies in Computational Intelligence Volume 481, pp 233-245, 2013.
5. L. Angelini, F. Carrino, S. Carrino, M. Caon, D. Lalanne, O. A. Khaled, and E. Mugellini. Opportunistic synergy: a classifier fusion engine for micro-gesture recognition. Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI), Pages 30-37, ACM New York, NY, USA, 2013.
6. F. Roulland, M. Niemaz, L. Ulloa, C. Loiodice, and J. Kingsley. City Dashboard: A framework for spatiotemporal analytics of transportation data, In: The 7th International Visualization in Transportation Symposium, Irvine, California, October 23-25, 2013.
7. B. Blamey, T. Crick, and G. Oatley. The First Day of Summer: Parsing Temporal Expressions with Distributed Semantics. Research and Development in Intelligent Systems XXX, pp 389-402, 2013.
8. Y. Alosefer. Analysing Web-based Malware Behaviour through Client Honeypots. PhD Thesis. Cardiff University, School of Computer Science & Informatics, 2012.
9. M. Wright, C.J. Lin, E. O’Neill, D. Cosker, and P. Johnson. 3D Gesture recognition: An evaluation of user and system performance. In: Pervasive Computing – 9th International Conference, Pervasive 2011, Proceedings. Heidelberg: Springer Verlag, pp. 294-313.
10. A. Z. Hassani, Touch versus in-air Hand Gestures: Evaluating the acceptance by seniors of Human-Robot Interaction using Microsoft Kinect, Master Thesis, University of Twente, Enschede, Netherlands, 2011.
11. Virtual Beach 3 – Virtual Beach 3 is a decision support tool created by the United States Environmental Protection Agency to construct site-specific statistical models to predict bacteria (FIB) concentrations at recreational beaches. The framework is listed as one of the technologies employed in its development. http://www2.epa.gov/exposure-assessment-models/virtual-beach-vb

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