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Author |
Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments |
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Journal Article |
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2014 |
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IEEE Transactions on Systems, Man and Cybernetics (Part B) |
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TSMCB |
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44 |
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12 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2379-2390 |
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The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios. |
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2168-2267 |
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HuPBA; ISE; 600.078;MILAB |
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no |
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Admin @ si @ LRE2014 |
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2442 |
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Author |
Carlo Gatta; Eloi Puertas; Oriol Pujol |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Multi-Scale Stacked Sequential Learning |
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Journal Article |
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2011 |
Publication |
Pattern Recognition |
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PR |
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44 |
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10-11 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2414-2416 |
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Stacked sequential learning; Multiscale; Multiresolution; Contextual classification |
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Abstract |
One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. |
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Elsevier |
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MILAB;HuPBA |
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Admin @ si @ GPP2011 |
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1802 |
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Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
![goto web page url](http://refbase.cvc.uab.es/img/www.gif)
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Title |
A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding |
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Journal Article |
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2013 |
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Pattern Recognition |
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PR |
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46 |
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10 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
2830-2839 |
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Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification |
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Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques. |
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Elsevier |
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0031-3203 |
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HuPBA;MILAB |
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Admin @ si @ BGE2013a |
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2247 |
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Author |
Cristina Cañero; Petia Radeva |
![goto web page (via DOI) doi](http://refbase.cvc.uab.es/img/doi.gif)
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Title |
Vesselness enhancement diffusion |
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Journal Article |
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2003 |
Publication |
Pattern Recognition Letters |
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PRL |
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24 |
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16 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
3141–3151 |
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IF: 0.809 |
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MILAB |
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BCNPCL @ bcnpcl @ CaR2003 |
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371 |
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Author |
Eduardo Aguilar; Beatriz Remeseiro; Marc Bolaños; Petia Radeva |
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Title |
Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants |
Type |
Journal Article |
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Year |
2018 |
Publication |
IEEE Transactions on Multimedia |
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20 |
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12 |
Pages ![sorted by First Page field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
3266 - 3275 |
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The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments. |
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MILAB; no proj |
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no |
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Admin @ si @ ARB2018 |
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3236 |
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