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X. Varona; Juan J. Villanueva |
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NeuroFilters: Neural Networks for image Processing. |
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1997 |
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Vision Systems: New image Processing Techniques and Applications Algorithms, Methods, and Components. Proceedings of the SPIE. |
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Munich |
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ISE @ ise @ VaV1997a |
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207 |
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Author |
Xavier Baro |
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Title |
Fast traffic sign detection on gray-scale images |
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2005 |
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CVC Technical Report #82 |
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OR;HuPBA;MV |
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BCNPCL @ bcnpcl @ Bar2005 |
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550 |
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Author |
Xavier Baro |
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Title |
Probabilistic Darwin Machines: A New Approach to Develop Evolutionary Object Detection |
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2009 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ever since computers were invented, we have wondered whether they might perform some of the human quotidian tasks. One of the most studied and still nowadays less understood problem is the capacity to learn from our experiences and how we generalize the knowledge that we acquire. One of that unaware tasks for the persons and that more interest is awakening in different scientific areas since the beginning, is the one that is known as pattern recognition. The creation of models that represent the world that surrounds us, help us for recognizing objects in our environment, to predict situations, to identify behaviors... All this information allows us to adapt ourselves and to interact with our environment. The capacity of adaptation of individuals to their environment has been related to the amount of patterns that are capable of identifying.
This thesis faces the pattern recognition problem from a Computer Vision point of view, taking one of the most paradigmatic and extended approaches to object detection as starting point. After studying this approach, two weak points are identified: The first makes reference to the description of the objects, and the second is a limitation of the learning algorithm, which hampers the utilization of best descriptors.
In order to address the learning limitations, we introduce evolutionary computation techniques to the classical object detection approach.
After testing the classical evolutionary approaches, such as genetic algorithms, we develop a new learning algorithm based on Probabilistic Darwin Machines, which better adapts to the learning problem. Once the learning limitation is avoided, we introduce a new feature set, which maintains the benefits of the classical feature set, adding the ability to describe non localities. This combination of evolutionary learning algorithm and features is tested on different public data sets, outperforming the results obtained by the classical approach. |
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Barcelona (Spain) |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Jordi Vitria |
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OR;HuPBA;MV |
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BCNPCL @ bcnpcl @ Bar2009 |
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1262 |
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Author |
Xavier Baro; David Masip; Elena Planas; Julia Minguillon |
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Title |
PeLP: Plataforma para el Aprendizaje de Lenguajes de Programación |
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Miscellaneous |
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2013 |
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XV Jornadas de Enseñanza Universitaria de la Informatica |
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JENUI |
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OR;HuPBA;MV |
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Admin @ si @ BMP2013 |
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2237 |
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Author |
Xavier Baro; Jordi Gonzalez; Junior Fabian; Miguel Angel Bautista; Marc Oliu; Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera |
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Title |
ChaLearn Looking at People 2015 challenges: action spotting and cultural event recognition |
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2015 |
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2015 IEEE Conference on Computer Vision and Pattern Recognition Worshops (CVPRW) |
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1-9 |
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Following previous series on Looking at People (LAP) challenges [6, 5, 4], ChaLearn ran two competitions to be presented at CVPR 2015: action/interaction spotting and cultural event recognition in RGB data. We ran a second round on human activity recognition on RGB data sequences. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes the two performed challenges and obtained results. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/. |
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Boston; EEUU; June 2015 |
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CVPRW |
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HuPBA;MV |
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2652 |
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Author |
Xavier Baro; Jordi Vitria |
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Title |
Feature Selection with Non-Parametric Mutual Information for Adaboost Learning |
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Miscellaneous |
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2005 |
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OR;HuPBA;MV |
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BCNPCL @ bcnpcl @ BaV2005a |
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582 |
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Author |
Xavier Baro; Jordi Vitria |
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Title |
Feature Selection with Non-Parametric Mutual Information for Adaboost Learning |
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2005 |
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Frontiers in Artificial Intelligence and Applications / Artificial intelligence Research and Development, 131:131–138, Eds: B. Lopez, J. Melendez, P. Radeva, J. Vitria, IOS Press, ISBN: 1–58603–560–6 |
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OR;HuPBA;MV |
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BCNPCL @ bcnpcl @ BaV2005b |
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583 |
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Author |
Xavier Baro; Jordi Vitria |
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Title |
Evolutionary Object Detection by Means of Naive Bayes Models Estimation |
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2008 |
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Applications of Evolutionary Computing. EvoWorkshops |
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4974 |
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235–244 |
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Naples (Italy) |
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M. Giacobini |
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OR;HuPBA;MV |
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no |
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BCNPCL @ bcnpcl @ BaV2008a |
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976 |
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Author |
Xavier Baro; Jordi Vitria |
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Title |
Weighted Dissociated Diploes: An Extended Visual Feature Set |
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2008 |
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Computer Vision Systems. 6th International Conference ICVS |
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5008 |
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281–290 |
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Santorini (Greece) |
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OR;HuPBA;MV |
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BCNPCL @ bcnpcl @ BaV2008b |
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977 |
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Author |
Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas |
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Title |
Coompetitions in machine learning: case studies |
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2016 |
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30th Annual Conference on Neural Information Processing Systems Worshops |
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Barcelona; Spain; December 2016 |
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NIPSW |
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HuPBA |
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Admin @ si @ BEG2016 |
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2911 |
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Author |
Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva |
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Title |
Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification |
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Journal Article |
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2009 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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10 |
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1 |
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113–126 |
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The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination. |
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1524-9050 |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ BEV2008 |
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1116 |
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Author |
Xavier Baro; Sergio Escalera; Petia Radeva; Jordi Vitria |
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Title |
Generic Object Recognition in Urban Image Databases |
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Conference Article |
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2009 |
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12th International Conference of the Catalan Association for Artificial Intelligence |
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202 |
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27-34 |
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In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (>500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. All this information is extracted without an object of reference, which allows to search for any type of objects using their visual appearance. A new Visual Content layer is built over Google Maps, allowing the object recognition information to be organized and fused with other content, like satellite images, street maps, and business locations. |
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Cardona (Spain) |
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978-1-60750-061-2 |
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CCIA |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ VER2009 |
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1183 |
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Xavier Baro; Sergio Escalera; Petia Radeva; Jordi Vitria |
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Title |
Visual Content Layer for Scalable Recognition in Urban Image Databases, Internet Multimedia Search and Mining |
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2009 |
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10th IEEE International Conference on Multimedia and Expo |
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1616–1619 |
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Rich online map interaction represents a useful tool to get multimedia information related to physical places. With this type of systems, users can automatically compute the optimal route for a trip or to look for entertainment places or hotels near their actual position. Standard maps are defined as a fusion of layers, where each one contains specific data such height, streets, or a particular business location. In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (> 500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. This allows an efficient and scalable way of accessing maps by visual content. |
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New York (USA) |
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ICME |
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OR;MILAB;HuPBA;MV |
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BCNPCL @ bcnpcl @ BER2009 |
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1189 |
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Author |
Xavier Boix |
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Learning Conditional Random Fields for Stereo |
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2009 |
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CVC Technical Report |
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136 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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CIC |
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Admin @ si @ Boi2009 |
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2395 |
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Xavier Boix; Josep M. Gonfaus; Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Marco Pedersoli; Jordi Gonzalez; Joan Serrat |
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Combining local and global bag-of-word representations for semantic segmentation |
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2009 |
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Workshop on The PASCAL Visual Object Classes Challenge |
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Kyoto (Japan) |
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Area |
|
Expedition |
|
Conference |
ICCV |
|
| |
Notes |
ADAS;ISE |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ BGS2009 |
Serial |
1273 |
|
| Permanent link to this record |
