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Donostia / San Sebastián, Spain

Arruti A.,University of the Basque Country | Cearreta I.,University of the Basque Country | Alvarez A.,Vicomtech IK4 Research Alliance | Lazkano E.,University of the Basque Country | Sierra B.,University of the Basque Country
PLoS ONE | Year: 2014

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. © 2014 Arruti et al. Source

Olaizola I.G.,Vicomtech IK4 Research Alliance | Barandiaran I.,Vicomtech IK4 Research Alliance | Sierra B.,University of the Basque Country | Grana M.,University of the Basque Country
VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications | Year: 2013

Global and local image feature extraction is one of the most common tasks in computer vision since they provide the basic information for further processes, and can be employed on several applications such as image search & retrieval, object recognition, 3D reconstruction, augmented reality, etc. The main parameters to evaluate a feature extraction algorithm are its discriminant capability, robustness and invariance behavior to certain transformations. However, other aspects such as computational performance or provided feature length can be crucial for domain specific applications with specific constraints (real-time, massive datasets, etc.). In this paper, we analyze the main characteristics of the DITEC method used both as global and local descriptor method. Our results show that DITEC can be effectively applied in both contexts. Source

Nieto M.,Vicomtech IK4 Research Alliance | Cortes A.,Vicomtech IK4 Research Alliance | Otaegui O.,Vicomtech IK4 Research Alliance | Etxabe I.,Datik
VISAPP 2012 - Proceedings of the International Conference on Computer Vision Theory and Applications | Year: 2012

This paper introduces a rail inspection system which detects rail flaws using computer vision algorithms. Unlike other methods designed for the same purpose, we propose a method that automatically fits a 3D rail model to the observations during regular services and normal traffic conditions. The proposed strategy is based on a novel application of the slice sampling technique with overrelaxation in the framework of MCMC (Markov Chain Monte Carlo) particle filters. This combination allows us to efficiently exploit the temporal coherence of observations and to obtain more accurate estimates than with other techniques such as importance sampling or Metropolis-Hastings. The results show that the system is able to efficient and robustly obtain measurements of the wear of the rails, while we show as well that it is possible to introduce the slice sampling technique into MCMC particle filters. Source

Unzueta L.,Vicomtech IK4 Research Alliance | Nieto M.,Vicomtech IK4 Research Alliance | Cortes A.,Vicomtech IK4 Research Alliance | Barandiaran J.,Vicomtech IK4 Research Alliance | And 2 more authors.
IEEE Transactions on Intelligent Transportation Systems | Year: 2012

In this paper, we present a robust vision-based system for vehicle tracking and classification devised for traffic flow surveillance. The system performs in real time, achieving good results, even in challenging situations, such as with moving casted shadows on sunny days, headlight reflections on the road, rainy days, and traffic jams, using only a single standard camera. We propose a robust adaptive multicue segmentation strategy that detects foreground pixels corresponding to moving and stopped vehicles, even with noisy images due to compression. First, the approach adaptively thresholds a combination of luminance and chromaticity disparity maps between the learned background and the current frame. It then adds extra features derived from gradient differences to improve the segmentation of dark vehicles with casted shadows and removes headlight reflections on the road. The segmentation is further used by a two-step tracking approach, which combines the simplicity of a linear 2-D Kalman filter and the complexity of a 3-D volume estimation using Markov chain Monte Carlo (MCMC) methods. Experimental results show that our method can count and classify vehicles in real time with a high level of performance under different environmental situations comparable with those of inductive loop detectors. © 2011 IEEE. Source

Barandiaran I.,Vicomtech IK4 Research Alliance | Barandiaran I.,University of the Basque Country | Cortes C.,Vicomtech IK4 Research Alliance | Cortes C.,EAFIT University | And 3 more authors.
VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications | Year: 2013

Key point extraction and description mechanisms play a crucial role in image matching, where several image points must be accurately identified to robustly estimate a transformation or to recognize an object or a scene. New procedures for keypoint extraction and for feature description are continuously emerging. In order to assess them accurately, normalized data and evaluation protocols are required. In response to these needs, we present a (1) new evaluation framework that allow assessing the performance of the state-of-the-art feature point extraction and description mechanisms, (2) a new image dataset acquired under controlled affine and photometric transformations and (3) a testing image generator. Our evaluation framework allows generating detailed curves about the performance of different approaches, providing a valuable insight about their behavior. Also, it can be easily integrated in many research and development environments. The contributions mentioned above are available on-line for the use of the scientific community. Source

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