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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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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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BCNPCL @ bcnpcl @ BaV2008a |
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976 |
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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; 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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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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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 |
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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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CVC (UAB) |
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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 |
X. Orriols; X. Binefa |
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Title |
An EM Algorithm for Video Summarization, Generative Model Approach. |
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2001 |
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Eighth International Conference on Computer Vision, IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence, 1:335–342. |
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Vancouver. |
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Admin @ si @ OBi2001 |
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199 |
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X. Orriols; Ricardo Toledo; X. Binefa; Petia Radeva; Jordi Vitria; Juan J. Villanueva |
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Probabilistic Saliency Approach for Elongated Structure Detection using Deformable Models. |
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2000 |
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15 th International Conference on Pattern Recognition |
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3 |
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1006-1009 |
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Barcelona. |
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ICPR |
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OR;MILAB;ADAS;MV |
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BCNPCL @ bcnpcl @ OTB2000 |
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224 |
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X. Orriols; Lluis Barcelo; X. Binefa |
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Polynomial Fiber Description of Motion for Video Mosaicing, Proceeding ICIP 2001. |
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2001 |
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IEEE International Conference on Image Processing, Grecia, 1:1030–1033. |
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Admin @ si @ OBB2001a |
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143 |
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X. Orriols; Lluis Barcelo; X. Binefa |
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An Appearance-Based Method for Parametric Video Registration. |
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2001 |
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Admin @ si @ OBB2001b |
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145 |
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X. Orriols; Andrew Willis; X. Binefa; David B. Cooper |
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Title |
Bayesian estimation of axial symmetries from partial data, a generative model approach |
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2000 |
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CVC Technical Report #49 |
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DAG @ dag @ OWB2000 |
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536 |
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X. Orriols |
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Models locals lineals per a l´analisi d´imatges |
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1999 |
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CVC Technical Report #31 |
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Admin @ si @ Orr1999 |
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192 |
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X. Jing; David Zhang; Zhong Jin |
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Improvements on the uncorrelated optimal discriminant vectors |
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2003 |
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Pattern Recognition, 36(8): 1921–1923 (IF: 1.611) |
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Admin @ si @ JZJ2003a |
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428 |
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X. Jing; David Zhang; Zhong Jin |
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Improved algorithm and generalized theory |
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2003 |
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Pattern Recognition, 36(11): 2593–2602 (IF: 1.611) |
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Admin @ si @ JZJ2003b |
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