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Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera |
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Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps |
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Journal Article |
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Year |
2019 |
Publication |
IEEE Transactions on Circuits and Systems for Video Technology |
Abbreviated Journal |
TCSVT |
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29 |
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6 |
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1729-1740 |
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Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding |
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Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset. |
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June 2019, |
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HUPBA; no proj |
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no |
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Admin @ si @ AAK2018 |
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3213 |
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Author |
Francesco Ciompi; Oriol Pujol; Petia Radeva |
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ECOC-DRF: Discriminative random fields based on error correcting output codes |
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Journal Article |
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2014 |
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Pattern Recognition |
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PR |
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47 |
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6 |
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2193-2204 |
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Discriminative random fields; Error-correcting output codes; Multi-class classification; Graphical models |
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We present ECOC-DRF, a framework where potential functions for Discriminative Random Fields are formulated as an ensemble of classifiers. We introduce the label trick, a technique to express transitions in the pairwise potential as meta-classes. This allows to independently learn any possible transition between labels without assuming any pre-defined model. The Error Correcting Output Codes matrix is used as ensemble framework for the combination of margin classifiers. We apply ECOC-DRF to a large set of classification problems, covering synthetic, natural and medical images for binary and multi-class cases, outperforming state-of-the art in almost all the experiments. |
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LAMP; HuPBA; MILAB; 605.203; 600.046; 601.043; 600.079 |
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Admin @ si @ CPR2014b |
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2470 |
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Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla |
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Editorial: Special Issue on Computational Intelligence for Vision and Robotics |
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2017 |
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Neural Computing and Applications |
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Neural Computing and Applications |
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28 |
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5 |
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853–854 |
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HuPBA;MILAB; no menciona |
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Admin @ si @ GGE2017 |
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2845 |
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Author |
Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez |
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Title |
End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data |
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2022 |
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IET Computer Vision |
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IETCV |
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16 |
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1 |
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50-66 |
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Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation |
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Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets. |
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HUPBA; ISE; 600.098; 600.119 |
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Admin @ si @ MEB2022 |
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3652 |
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Author |
Miguel Angel Bautista; Oriol Pujol; Fernando De la Torre; Sergio Escalera |
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Title |
Error-Correcting Factorization |
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2018 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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40 |
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2388-2401 |
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Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi- class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without analyzing how it distributes among classes, hindering a deeper analysis of pair-wise error-correction. To address these limitations this paper proposes an Error-Correcting Factorization (ECF) method, our contribution is three fold: (I) We propose a novel representation of the error-correction capability, called the design matrix, that enables us to build an ECOC on the basis of allocating correction to pairs of classes. (II) We derive the optimal code length of an ECOC using rank properties of the design matrix. (III) ECF is formulated as a discrete optimization problem, and a relaxed solution is found using an efficient constrained block coordinate descent approach. (IV) Enabled by the flexibility introduced with the design matrix we propose to allocate the error-correction on classes that are prone to confusion. Experimental results in several databases show that when allocating the error-correction to confusable classes ECF outperforms state-of-the-art approaches. |
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0162-8828 |
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HuPBA; no menciona |
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Admin @ si @ BPT2018 |
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3015 |
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