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Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2008). Providing Automatic Multilingual Text Generation to Artificial Cognitive Systems.
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Mikhail Mozerov, Ignasi Rius, Xavier Roca, & Jordi Gonzalez. (2010). Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences. EURASIPJ - EURASIP Journal on Advances in Signal Processing, .
Abstract: 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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Ariel Amato, Mikhail Mozerov, Xavier Roca, & Jordi Gonzalez. (2010). Robust Real-Time Background Subtraction Based on Local Neighborhood Patterns. EURASIPJ - EURASIP Journal on Advances in Signal Processing, , 7.
Abstract: 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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Wenjuan Gong, Jordi Gonzalez, & Xavier Roca. (2012). Human Action Recognition based on Estimated Weak Poses. EURASIPJ - EURASIP Journal on Advances in Signal Processing, .
Abstract: We present a novel method for human action recognition (HAR) based on estimated poses from image sequences. We use 3D human pose data as additional information and propose a compact human pose representation, called a weak pose, in a low-dimensional space while still keeping the most discriminative information for a given pose. With predicted poses from image features, we map the problem from image feature space to pose space, where a Bag of Poses (BOP) model is learned for the final goal of HAR. The BOP model is a modified version of the classical bag of words pipeline by building the vocabulary based on the most representative weak poses for a given action. Compared with the standard k-means clustering, our vocabulary selection criteria is proven to be more efficient and robust against the inherent challenges of action recognition. Moreover, since for action recognition the ordering of the poses is discriminative, the BOP model incorporates temporal information: in essence, groups of consecutive poses are considered together when computing the vocabulary and assignment. We tested our method on two well-known datasets: HumanEva and IXMAS, to demonstrate that weak poses aid to improve action recognition accuracies. The proposed method is scene-independent and is comparable with the state-of-art method.
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Wenjuan Gong, W.Zhang, Jordi Gonzalez, Y.Ren, & Z.Li. (2015). Enhanced Asymmetric Bilinear Model for Face Recognition. IJDSN - International Journal of Distributed Sensor Networks, , Article ID 218514.
Abstract: Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initializing the factor probabilities randomly, we initialize them with nearest neighbor method and optimize them for the test data. Above that, we update the factor model to be identified. We validate the proposed method on a designed data sample and extended Yale B dataset. The experiment results show that the enhanced asymmetric models give promising results and good recognition accuracies.
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