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Joost Van de Weijer; Theo Gevers; A. Gijsenij |
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Edge-Based Color Constancy |
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2007 |
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IEEE Trans. on Image Processing, vol. 16(9):2207–2214 |
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CAT @ cat @ WGG2007 |
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949 |
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Mikhail Mozerov; Ariel Amato; Xavier Roca; Jordi Gonzalez |
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Trajectory Occlusion Handling with Multiple View Distance Minimisation Clustering |
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2008 |
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Optical Engineering, vol. 47(04)04702, DOI:10.11781.2909665 |
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ISE @ ise @ MAR2008c |
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970 |
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Carles Fernandez; Xavier Roca; Jordi Gonzalez |
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Providing Automatic Multilingual Text Generation to Artificial Cognitive Systems |
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2008 |
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ISE @ ise @ FRG2008 |
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1021 |
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Mikhail Mozerov; Ignasi Rius; Xavier Roca; Jordi Gonzalez |
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Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences |
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2010 |
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EURASIP Journal on Advances in Signal Processing |
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Article ID 507247
A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes. |
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1110-8657 |
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ISE @ ise @ MRR2010 |
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1208 |
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Ariel Amato; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez |
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Robust Real-Time Background Subtraction Based on Local Neighborhood Patterns |
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2010 |
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EURASIP Journal on Advances in Signal Processing |
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Article ID 901205
This paper describes an efficient background subtraction technique for detecting moving objects. The proposed approach is able to overcome difficulties like illumination changes and moving shadows. Our method introduces two discriminative features based on angular and modular patterns, which are formed by similarity measurement between two sets of RGB color vectors: one belonging to the background image and the other to the current image. We show how these patterns are used to improve foreground detection in the presence of moving shadows and in the case when there are strong similarities in color between background and foreground pixels. Experimental results over a collection of public and own datasets of real image sequences demonstrate that the proposed technique achieves a superior performance compared with state-of-the-art methods. Furthermore, both the low computational and space complexities make the presented algorithm feasible for real-time applications. |
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1110-8657 |
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ISE @ ise @ AMR2010 |
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1463 |
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