Portuguese Conference on Pattern Recognition.
Conferência nacional que pretende promover a colaboração entre a comunidade científica Portuguesa nas áreas de Reconhecimento de Padrões, Análise e Processamento de Imagem, Computação e áreas relacionadas.
É co-organizada por uma entidade diferente e em local diferente todos os anos, com o propósito de potenciar estas áreas de investigação em diversas regiões do país com relevância científica.
Os trabalhos são submetidos como Extended Abstracts no formato final de artigos completos de duas páginas e avaliados pelo comité técnico da organização. Os artigos são compilados e distribuídos em formato electrónico apenas.
27th edition of the Portuguese Conference On Pattern Recognition27th Portuguese Conference on Pattern Recognition RECPAD is the annual Portuguese Conference on Pattern Recognition, sponsored by APRP (Portuguese Association for Pattern Recognition). It is a one-day conference with an invited keynote speaker and poster sessions along the day. RECPAD 2021 was organized by University of Évora and held at Colégio do Espírito Santo (the main building of the University) on November 5th 2021. https://recpad2021.uevora.pt/
- Teresa Gonçalves, Universidade de Évora
- Luís Rato, Universidade de Évora
- Pedro Salgueiro, Universidade de Évora
- Francisco Coelho, Universidade de Évora
- Miguel Barão, Universidade de Évora
- Eduardo Medeiros, Universidade de Évora
- Leonel Corado, Universidade de Évora
- Rute Veladas, Universidade de Évora
Professor Keshav Dahal is a Professor of intelligent systems and the leader of the Artificial Intelligence, Visual Communications and Network (AVCN) Research Centre, University of the West of Scotland, Paisley, U.K. He received his Masters and PhD degrees from the University of Strathclyde, UK. He also worked at the University of Bradford and the University of Strathclyde. His research interests lie in the areas of applied AI, trust and security modelling in distributed systems, Blockchain technology and scheduling/optimization problems. He has been principal investigator or co-investigator on more than 25 externally funded projects and supervised over 30 PhD and postdoctoral researchers. He has published over 170 papers in his research fields with award-winning publications and has sat on organizing/program committees of over 60 international conferences. He is a Senior Member of IEEE.
Multi-objective search with evolving fitness functions for solving scheduling problems
This talk will present some of the development in multi-objective approaches for solving complex scheduling problems. The first part of the talk will investigate multi-objective and weighted single objective approaches to a real-world workforce scheduling problem. The computational experiments show that multi-objective genetic algorithms can create solutions whose fitness is close to that of the solution created by the genetic algorithms using weighted sum objectives even though the multi-objective approaches know nothing of the weights. In the second part of the talk will discuss the variable fitness function approach to enhance the metaheuristic approaches by evolving weights for each of the multiple objectives. The results show that the variable fitness function approach improves the performance of constructive and variable neighbourhood search approaches on workforce scheduling problem instances.
26th edition of the Portuguese Conference On Pattern Recognition26th Portuguese Conference on Pattern Recognition RECPAD is the annual Portuguese Conference on Pattern Recognition, sponsored by APRP (Portuguese Association for Pattern Recognition). It is a one-day conference with an invited keynote speaker and poster sessions along the day. RECPAD 2020 is organized by University of Évora and was held online on October 30th, 2020 https://recpad2020.uevora.pt/
- Teresa Gonçalves, Universidade de Évora
- Luís Rato, Universidade de Évora
- Pedro Salgueiro, Universidade de Évora
- Miguel Barão, Universidade de Évora
- Eduardo Medeiros, Universidade de Évora
Dr Hubert P. H. Shum is an Associate Professor in Computer Science at Durham University. Before this, he worked as the Director of Research/Associate Professor/Senior Lecturer at Northumbria University, a Postdoctoral Researcher at RIKEN Japan, and a Research Assistant at the City University of Hong Kong. He received his PhD degree from the University of Edinburgh, his Master and Bachelor degrees from the City University of Hong Kong. He led funded research projects as the Principal Investigator awarded by EPSRC, the Ministry of Defence (DASA) and the Royal Society. This facilitated him to develop his research team and to collaborate with international researchers from the UK, China, France, Japan and India. To engage the academic and industry networks, he led his team hosting important conferences such as BMVC and ACM SIGGRAPH Conference on MIG. Contributing to the research community, he has served as an Associate Editor for Computer Graphics Forum, a Guest Editor for International Journal of Computer Vision, and a Program Committee member in 15 conferences such as CVPR, Eurographics, Pacific Graphics. He has published over 100 research papers in the fields of computer graphics, computer vision, motion analysis and machine learning, particularly focusing on the modelling of human-related data. More information can be found on http://hubertshum.com.
Machine Learning for Human Data Modelling and Analysis
Taking advantage of the recent advancement in machine learning and the availability of big data, computers have become smarter than ever in understanding complicated data. In this talk, I will focus on the modelling and analysis of human data, which can be represented in formats such as images, video, 3D movement and surfaces. Such data is core to a wide spectrum of research fields including computer vision (e.g. action recognition, pose estimation, 3D reconstruction), computer graphics (e.g. character animation, crowd simulations) and biomedical engineering (e.g. diseases diagnosis, motion analysis). Modelling human data effectively is a challenging problem as it is high dimensional in nature and diverse in representations. I will talk about how machine learning techniques can be used to take on the challenge to come up with novel models that enable robust applications. In particular, I will discuss how state-of-the-art deep learning provides a powerful framework for large-scale human data modelling and analysis. Finally, I will share some insights into future research opportunities and interesting research directions in this area.
25th edition of the Portuguese Conference On Pattern RecognitionRECPAD is the annual Portuguese Conference on Pattern Recognition, sponsored by APRP (Portuguese Association for Pattern Recognition). It is a one-day conference to promote the collaboration between the Portuguese scientific community in the fields of Pattern Recognition, Image Analysis and Processing, Soft Computing, and related areas. This year, RECPAD2019 will be held at FCUP (Faculdade Ciências da Universidade do Porto), on October 31st, 2019.
Organizing Committee of RECPAD 2019
Hélder Oliveira (INESC TEC / FCUP)
Miguel Coimbra (FCUP / IT)
Ana Rebelo (INESC TEC / UPT)
Francesco Renna (IT / FCUP)
Jorge Oliveira (IT)
Carlos Ferreira (INESC TEC)
September 6 – Submission Deadline
September 24 – Accept/Reject Notification
October 1 – Camera-Ready Deadline
Carmen Poon is currently an Assistant Professor at the Department of Surgery, The Chinese University of Hong Kong (CUHK). She received the B.A.Sc. in Engineering Science, M.A.Sc. in Biomaterials and Biomedical Engineering & Electrical and Computer Engineering at University of Toronto, and Ph.D. in Electronic Engineering at CUHK. Her research interests are in wearable sensing and endoscopic informatics that have potential to change surgical practices. Several pieces of her works have been rated Highly Cited Papers by ISI Web of Science (2015-2018) and collectively, her works have been cited >2300 times in Scopus (as of 25 Dec. 2018).
Carmen is serving as Conference Chair of the 16th International Conference on Wearable and Implantable Body Sensor Networks, which will be held in Chicago, USA, 2019. She has served as IEEE Engineering in Medicine and Biology Society (EMBS) Administrative Committee member (2014-2016), and Chair for EMBS Technical Committee on Wearable Biomedical Sensors and Systems (2016-2017). She was Managing Editor for IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2009-2016), during which the Journal’s impact factor has doubled from 1.6 to 3.4. She now serves on the Editorial Boards for several international peer-reviewed journals published by IEEE, IOP, Elsevier and ACM.
Carmen is a Senior Member of IEEE. She received the IFMBE/IAMBE Early Career Award awarded by International Federation of Medical and Biological Engineering / The International Academy of Medical and Biological Engineering in 2015, and the IEEE-EMBS Academic Early Career Achievement Award “for contributions to wearable sensing and endoscopic surgery” in 2018.
AI-doscopist: A Deep Learning-based Model for Real-time Cancer Diagnosis during Therapeutic Endoscopy.
24th edition of the Portuguese Conference On Pattern Recognition
RECPAD is the annual Portuguese Conference on Pattern Recognition, sponsored by APRP (Portuguese Association for Pattern Recognition). It is a one-day conference with an invited keynote speaker and poster sessions along the day. This year, RECPAD2018 will be held at the Centro Cultural D. Dinis, in the heart of the UNESCO World Heritage site of the University of Coimbra , on October 26th, 2018. Please feel extremelly welcome!
- Bernardete Ribeiro
- Hélder Araújo
- Catarina Silva
- César Teixeira
- Joel Arrais
- Joana Costa
- Francisco Antunes
Vincenzo Piuri has received his Ph.D. in computer engineering at Politecnico di Milano, Italy (1989). He is Full Professor in computer engineering at the Università degli Studi di Milano, Italy (since 2000). He has been Associate Professor at Politecnico di Milano, Italy and Visiting Professor at the University of Texas at Austin and at George Mason University, USA.
His main research interests are: intelligent systems, signal and image processing, machine learning, pattern analysis and recognition, theory and industrial applications of neural networks, biometrics, intelligent measurement systems, industrial applications, fault tolerance, digital processing architectures, and cloud computing infrastructures. Original results have been published in more than 400 papers in international journals, proceedings of international conferences, books, and book chapters.
He is Fellow of the IEEE, Distinguished Scientist of ACM, and Senior Member of INNS. He has been IEEE Vice President for Technical Activities (2015), IEEE Director, President of the IEEE Computational Intelligence Society, Vice President for Education of the IEEE Biometrics Council, Vice President for Publications of the IEEE Instrumentation and Measurement Society and the IEEE Systems Council, and Vice President for Membership of the IEEE Computational Intelligence Society. He is Editor-in-Chief of the IEEE Systems Journal (2013-19), and Associate Editor of the IEEE Transactions on Computers and the IEEE Transactions on Cloud Computing, and has been Associate Editor of the IEEE Transactions on Neural Networks and the IEEE Transactions on Instrumentation and Measurement.
He received the IEEE Instrumentation and Measurement Society Technical Award (2002). He is Honorary Professor at Obuda University, Budapest, Hungary, Guangdong University of Petrochemical Technology, China, Muroran Institute of Technology, Japan, and the Amity University, India.
Computational Intelligence for Biometric Technologies and Applications
23rd edition of the Portuguese Conference On Pattern RecognitionEsta edição do Recpad foi organizada por um comité da Academia Militar e decorreu na Academia Militar.
- Jose Silvestre Serra Silva, Chairman, (Academia Militar);
- Jorge Paulo Alves Torres, (Academia Militar);
- Pedro Nuno Mendonça dos Santos, (Academia Militar);
- Thomas Peter Gasche, (Academia Militar).
Paul Scheunders received the Ph.D. degree in physics, with work in the field of statistical mechanics, from the University of Antwerp, Antwerp, Belgium, in 1990. In 1992, he became a research associate with the Vision Lab, Department of Physics, University of Antwerp, where he is currently a professor. His current research interest includes remote sensing and in particular hyperspectral image processing. He has published over 150 papers in international journals and proceedings in the field of image processing, pattern recognition and remote sensing.
Paul Scheunders is associate editor of the IEEE Transactions in Geoscience and Remote Sensing, and has served as program committee member in numerous international conferences. He is senior member of the IEEE Geoscience and Remote Sensing Society.
Machine Learning for remote sensing image analysis
In this talk, he will describe the state of the art on the development and application of machine learning methodologies in the remote sensing domain. He will also describe the specific remote sensing analysis problems that are typically handled by machine learning. On important type of sensor is the hyperspectral image sensor. Hyperspectral image sensors have been important tools for the characterization of materials based on their light reflectance mainly in remote sensing but in other domains as well. Hyperspectral images contain many spectral bands, each revealing the earth surface reflected light at a particular wavelength. These hyperspectral images require specific image processing and analysis methodologies. In this talk, an overview will be given of recent developments of machine learning in hyperspectral image analysis. Some of the strategies that are elaborated on are kernel methods, neural network methods, manifold learning methods, structured output methods, ensemble learning methods and sparse learning methods.
22nd edition of the Portuguese Conference On Pattern RecognitionEsta edição do Recpad foi organizada por um comité da Universidade de Aveiro e decorreu na Universidade de Aveiro.
- Armando J. Pinho (UA)
- Diogo Pratas (UA)
- Raquel Sebastião (UA)
- Samuel Silva (UA)
- Sónia Gouveia (UA)
- Susana Brás (UA)
Joan Serra-Sagristà (IEEE Senior Member 2011) received the Ph.D. degree in computer science from Universitat Autònoma de Barcelona (UAB), Spain, in 1999. He is currently an Associate Professor at Department of Information and Communications Engineering, UAB. He holds the Accreditation as Full Professor from both Spanish ANECA and Catalan AQU Catalunya. From September 1997 to December 1998, he was at University of Bonn, Germany, funded by DAAD. His current research interests focus on data compression, with special attention to image coding for remote sensing and telemedicine applications. He serves as Associate Editor of IEEE Trans. on Image Processing and as Program Committee co-chair for IEEE Data Compression Conference. He has co-authored over one hundred publications. He was the recipient of the Spanish Intensification Young Investigator Award in 2006.
Remote sensing data compression
This talk describes recent developments in several areas of remote sensing data compression. The first part of the talk will introduce the current status of Earth Observation missions and the need for efficient data transmission, where data compression plays a significant role. The second part of the talk will be dedicated to onboard compression of remote sensing data, in particular to recent and ongoing work developed by the main space agencies. The third part of the talk will introduce some of our own recent developments in this field.
21st edition of the Portuguese Conference On Pattern RecognitionEsta edição do Recpad foi organizada por um comité da Universidade do Algarve e decorreu no Instituto Superior de Engenharia da Universidade do Algarve.
- João Rodrigues – General Chair (UAlg)
- Pedro Cardoso – (UAlg)
- Robert Lam – (UAlg)
- Mauro Figueiredo – (UAlg)
PatrocinadoresFoi patrocinado pela SPIC e pelo Hotel EVA.
Maersk MC-Kinney Moller Institute for Production Technology,
Technical Faculty at the University of Southern Denmark.
Norbert Krüger has been employed at the University of Southern Denmark since 2006 (first as an Associate Professor and then as a full Professor (MSO) since 2008). He is one of the two leaders of the Cognitive and Applied Robotics Group (CARO, caro.sdu.dk) in which currently 12 PhD students, two Assistant and two Associate Professor as well as 8 master students are working. Norbert Krüger’s research focuses on Cognitive Vision, in particular vision based manipulation and learning. He has published 45 papers in journals and more than 80 papers at conferences covering the topics computer vision, robotics, neuroscience as well as cognitive systems. His H-index is 24. His group has developed the C++-software CoViS (Cognitive Vision Software) which is now used by a number of groups in national as well as European projects. He is currently involved in 2 European projects as well as 4 Danish projects.
Deep Hierarchies in Human and Computer Vision
Computer vision – although being still a rather young scientific discipline – in the last decades was able to provide some impressive examples of artificial vision systems that outperform humans. However, the human visual system is still superior to any artificial vision system in visual tasks requiring generalization and reasoning (often also called ‘cognitive vision’) such as extraction of visual based affordances or visual tasks in the context of tool use and dexterous manipulation of unknown objects.
Two decades ago, there has been a strong connection between the two communities dealing with human vision research and computer vision. This link however has been somehow lost recently and computer vision has been more and more developed into a sub-field of machine learning. In this talk, I argue that the reason for the superiority of human vision for ‘cognitive vision tasks’ is connected to the deep hierarchical architecture of the primate’s visual system.
The talk is divided into two parts: First, I will give an overview about today’s knowledge about the primate’s (and by that the human’s) visual system primarily based on neurophysiological research. This part is based on the paper (Kruger et al. 2013, IEEE PAMI) and is in particular addressing computer vision and machine learning scientists as audience.
In the second part of the talk, I will describe a three level hierarchical cognitive robot system in which actions are learned by observing humans performing these actions (Kruger et al. 2013, KI). Learning is taking place at the different levels of the hierarchy in rather different representations. On the sensory-motor level, the shape and appearance of objects as well as optimal action trajectories and force torque profiles are represented. On the mid-level, a discrete visual representation based on semantic event chains (Aksoy et al. 2011) bridges towards the planning level, the highest representational level. I will describe different the learning problems on the different representational levels and their interaction.
20th edition of the Portuguese Conference on Pattern Recognition.Esta edição do Recpad foi organizada por um comitté do University of Beira Interior e decorreu no Museu Royal Veiga Factory.
- Luís A. Alexandre – General Chair (UBI)
- Hugo Proença – (UBI)
- Paulo Fazendeiro – (UBI)
- Dulce Ribeiro – Secretary (UBI)
PatrocinadoresFoi patrocinado pela EyeSee Lda e pela INDRA Portugal e Inova Prime.
Institut de Robòtica i Informàtica Industrial (CSIC-UPC)
Francesc Moreno-Noguer received the MSc degrees in industrial engineering and electronics from the Technical University of Catalonia (UPC) and the Universitat de Barcelona in 2001 and 2002, respectively, and the PhD degree from UPC in 2005. From 2006 to 2008, he was a postdoctoral fellow at the computer vision departments of Columbia University and the Ecole Polytecnique Fédérale de Lausanne. In 2009, he joined the Institut de Robòtica i Informàtica Industrial in Barcelona as an associate researcher of the Spanish Scientific Research Council. His research interests include retrieving rigid and nonrigid shape, motion, and camera pose from single images and video sequences, with applications to both robotics and medical imaging. He received UPC’s Doctoral Dissertation Extraordinary Award for his work and an outstanding reviewer award at ECCV’12 and CVPR’14. Further information can be found at http://www.iri.upc.edu/people/fmoreno/.
Monocular 3D detection of rigid and non-rigid shapes
In this talk, I will first present an approach to the PnP problem, the estimation of the pose of a calibrated camera from n point correspondences between an image and a 3D model of a rigid object, whose computational complexity grows linearly with n. Our central idea is to express the 3D points as a weighted sum of four virtual control points. The problem then reduces to estimating the coordinates of these control points in the camera coordinate system, which can be done in O(n) using simple linearization techniques. I will then show how an algebraic outlier rejection scheme can be introduced within the computation of the pose, without the need to resort to RANSAC-based strategies.
In the second part of the talk I will show how the same linear formulations can be extended to retrieving the shape of 3D deformable objects. However, since monocular non-rigid reconstruction is severely under-constrained we will have to consider additional constraints, either based on local rigidity (to reconstruct deformable and inextensible surfaces), or based on shading coherence (to reconstruct deformable and stretchable surfaces). Finally I will discuss the major limitations of these linear formulations and propose a novel and alternative stochastic exploration strategy. I will present results both for non-rigid shape and human pose recovery.
19th edition of the Portuguese Conference on Pattern Recognition.Esta edição do Recpad foi organizada por um comitté do Instituto Superior Técnico
- João Sanches – General chair (ISR / IST)
- Manya Afonso – Local chair (ISR / IST)
- David Afonso – Informatics (ISR / IST)
- Alexandre Domingues (ISR / IST)
- Martina Fonseca (ISR / IST)
- Anastasiya Strembitska (ISR / IST)
Ana L.N. Fred received the MS and PhD degrees in electrical and computer engineering, in 1989 and 1994, respectively, both from the Instituto Superior Técnico (IST), Technical University of Lisbon, Portugal.
She has been a faculty member of IST since 1986, where she is currently a professor (Professora associada com agregação) with the Department of Electrical and Computer Engineering.
She is a researcher at the Communication Theory and Pattern Recognition Group of the Institute of Telecommunications. Her research interests include information theory, pattern recognition, signal processing, and artificial intelligence.
PHYSIOLOGICAL COMPUTING – A PATTERN RECOGNITION PERSPECTIVE
18th edition of the Portuguese Conference on Pattern Recognition.
RecPad was organized by:
- Nuno Cid Martins
- Verónica Vasconcelos
- Fernando Lopes
- Inácio Fonseca
- Jorge Barbosa
- Nuno Rodrigues
- Simão Paredes
- Inês Duarte
- Teresa Jorge
Carlos Gonzalez-Morcillo is an Associate Professor of Computer Science at the University of Castilla-La Mancha (Spain). He received the BsC and PhD degrees in Computer Science from the University of Castilla-La Mancha in 2002 and 2007, respectively. His research interests include Distributed Rendering, Augmented Reality, MultiAgent Systems, and Intelligent Surveillance. Dr Morcillo has worked in the fields of Computer Vision and Data Mining at the Software Competence Center Hagenberg (Austria). He is Blender Foundation Certified Trainer and member of the Eurographics Society. He is also co-author of the Spanish book Fundamentals of 3D Image Synthesis, a practical approach with Blender. Further information can be found at http://www.esi.uclm.es/www/cglez
Indoor Navigation Infrastructure based on Augmented Reality Techniques
Indoor navigation systems have represented a hot research topic for the research community with many different proposals to position people and keep track of their movements. In this lecture, Dr. Morcillo will take a step forward describing an object-oriented distributed architecture for highly scalable indoor navigation systems. The idea behind this architecture is to assist people with different needs while they stay in large spaces or buildings, such as public administration buildings, transport facilities, hospitals and so on, helping them reach their goals in such environments.
In this context, the use of Augmented Reality techniques boosts the concept of mobility thanks to the underlying architecture, integrating heterogeneous hardware devices and tracking methods. The resulting platform is a multi-layered scalable architecture based on autonomous agents. The most relevant work carried out by the Applied Artificial Intelligence Research Group at the University of Castilla-La Mancha will be also discussed.
17th edition of the Portuguese Conference on Pattern RecognitionOrganizing Committee
- Ricardo Sousa (FEUP / INESC Porto)
Daniel Gatica-Perez is a senior researcher at Idiap Research Institute and the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland, where he directs the Social Computing Group. His research integrates methods from multimedia signal processing, machine learning, ubiquitous computing, and social sciences to develop computational models for human and social behavior analysis from sensor data. His recent work has studied small groups at work in multisensor spaces, populations of smartphone users in urban environments, and on-line communities in social media. His work
has been supported by the Swiss and US governments, the European Union, and industry. Among several professional activities, he currently serves as Associate Editor of the IEEE Transactions on Multimedia, Image and Vision Computing, and the Journal of Ambient Intelligence and Smart Environments.
Reality Mining for Real: Large-Scale Human Behavior and Smartphone Data
The large-scale understanding of personal and social behavior from smartphone sensor data is an emerging trend in computing. Smartphones can constantly sense human location, motion, proximity, and communication, and represent one of the most accurate means of tracing human activities. All this data,as never before, is being generated at massive scales.
I will present an overview of recent work in my research group in this domain, which is addressing mobile sensing, data analysis, and applications. I will first describe our experience with the collection of a rich corpus of real life data using smartphones as sensors, and discuss a few of the many associated challenges – both human and technological. I will then present computational methods that we have developed to discover a variety of patterns, including daily routines of individuals, trends of phone application usage, social interaction types, and personality traits. I will finally discuss about open issues in this domain.
16th Portuguese Conference on Pattern RecognitionOrganizing Committee
- Paulo Salgado
- João A. Pavão
- Pedro Couto
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Jesús Chamorro Martinez – firstname.lastname@example.org | Associate professor
Department of Computer Science and Artificial Intelligence – University of Granada
Jesús Chamorro was born in 1972. He received the MS and PhD degrees, both in Computer Science, from the University of Granada in 1995 and 2001 respectively. Since 1996, he is member of the Computer Science Department at the University of Granada where he is now an Associate Professor. His research interests include motion analysis and optical flow estimation, information retrieval from image databases, soft computing image processing, and biomedical image analysis. He has participated in several projects from the Spanish Research Council (CICYT) and currently he is participating in the Spanish FIS project “Imagen Médica Molecular y Multimodalidad” (IM3).
Multiple Motion Segmentation based on Spatio-temporal Volume Recognition. Application to Optical flow estimation
In this lecture, a new methodology for extracting motion patterns is applied to optical flow estimation in the presence of multiple motions. The proposed approximation deals with the problem in two stages. In the first one, the most important motions are segmented; in the second one, the optical flow is estimated on the basis of the motions detected in the previous stage. To extract relevant motions, a new approach based on a spatio-temporal filtering is presented. The approach groups together parts of a moving object that have been separated into various filter responses because of the object’s spatial structure, thereby avoiding the spatial dependency problem associated with a representation based on spatio-temporal filters. The proposed model, therefore, generates one “motion pattern” for each motion detected in the sequence. To obtain an optical flow estimation, which is able to represent multiple velocities, the gradient constraint is applied to the output of each filter so that multiple estimations of the velocity at the same location may be obtained. For each “motion pattern” detected in the previous stage, the velocities at a given point corresponding to the same motion are then combined using a probabilistic approach. In the application to optical flow estimation, the use of “motion patterns” allows multiple velocities to be represented, while the combination of estimations fromdifferent filters helps reduce the aperture problem. This technique is illustrated on real and simulated data sets, including sequences with occlusion and transparencies.
15th Portuguese Conference on Pattern Recognition
13ª Conferência Portuguesa de Reconhecimento de PadrõesComissão Organizadora
- Pedro Pina (Chair, IST)
- José Saraiva (IST)
- Cristina Lira (IST)
- Michele Mengucci (IST)
- Nuno Benavente (IST)
- Lourenço Bandeira (IST)
- Aurélio Campilho (INEB/FEUP)
- Beatriz Sousa Santos (Univ. Aveiro)
- Helder Araújo (Univ. Coimbra)
- João Rogério Caldas Pinto (IST)
- Pedro Pina (IST)
Júlio Martín-Herrero, Professor da Escuela Técnica Superior de Ingenieros de Telecomunicación da Universidade de Vigo em Espanha, que tem como principais interesses de investigação o Processamento de Imagem e Visão Artificial, a Análise Espacial e as suas aplicações multidisciplinares. O título da apresentação que irá efectuar é o seguinte:
Robust Oil Slick Identification in Synthetic Aperture Radar Images with Support Vector Machines
14ª Conferência Portuguesa de Reconhecimento de PadrõesComissão Organizadora
- Jorge Batista (Chair,ISR/FCTUC)
- Helder Araújo (ISR/FCTUC)
- Paulo Peixoto (ISR/FCTUC)
- Gonçalo Monteiro (ISR)
- Pedro Martins (ISR)
Dr. Jordi Gonzàlez, Investigador no Institut de Robòtica i Informàtica Industrial (UPC-CSIC), Espanha.
Dr. Jordi Gonzàlez obtained his PhD degree in 2004, from Universitat Autònoma de Barcelona. At present he is a Juan de la Cierva postdoctoral researcher at the Institut de Robòtica i Informàtica Industrial (UPC-CSIC). The topic of his research is the cognitive evaluation of human behaviours in image sequences. The aim is the generation of both linguistic descriptions and virtual environments, which explain those observed behaviours. He has more than 70 publications about his research on active camera control; segmentation and tracking; human action recognition; human behaviour understanding and interpretation; natural language text generation; and automatic behavioural animation. He has also participated as a WP-leader in the European projects HERMES and VIDI-Video, and as a member in the euCognition network. He co-founded the Image Sequence Evaluation research group at the CVC in Barcelona.
Towards Human Sequence Evaluation