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Author Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez edit  doi
openurl 
  Title Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments Type Journal Article
  Year 2014 Publication IEEE Transactions on Systems, Man and Cybernetics (Part B) Abbreviated Journal TSMCB  
  Volume (down) 44 Issue 12 Pages 2379-2390  
  Keywords  
  Abstract 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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  ISSN 2168-2267 ISBN Medium  
  Area Expedition Conference  
  Notes HuPBA; ISE; 600.078;MILAB Approved no  
  Call Number Admin @ si @ LRE2014 Serial 2442  
Permanent link to this record
 

 
Author Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Fabio Ferreira; Isabelle Guyon; Sirui Hong; Frank Hutter; Rongrong Ji; Julio C. S. Jacques Junior; Ge Li; Marius Lindauer; Zhipeng Luo; Meysam Madadi; Thomas Nierhoff; Kangning Niu; Chunguang Pan; Danny Stoll; Sebastien Treguer; Jin Wang; Peng Wang; Chenglin Wu; Youcheng Xiong; Arber Zela; Yang Zhang edit  url
doi  openurl
  Title Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019 Type Journal Article
  Year 2021 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume (down) 43 Issue 9 Pages 3108 - 3125  
  Keywords  
  Abstract This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a “meta-learner”, “data ingestor”, “model selector”, “model/learner”, and “evaluator”. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free “AutoDL self-service.”  
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  Notes HUPBA; no proj;MILAB Approved no  
  Call Number Admin @ si @ LPX2021 Serial 3587  
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Author Oriol Pujol; Sergio Escalera; Petia Radeva edit  openurl
  Title An Incremental Node Embedding Technique for Error Correcting Output Codes Type Journal
  Year 2008 Publication Pattern Recognition Abbreviated Journal PR  
  Volume (down) 41 Issue 2 Pages 713–725  
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  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ PER2008 Serial 942  
Permanent link to this record
 

 
Author Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva edit  doi
openurl 
  Title Circular Blurred Shape Model for Multiclass Symbol Recognition Type Journal Article
  Year 2011 Publication IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) Abbreviated Journal TSMCB  
  Volume (down) 41 Issue 2 Pages 497-506  
  Keywords  
  Abstract In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.  
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  Series Volume Series Issue Edition  
  ISSN 1083-4419 ISBN Medium  
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  Notes MILAB; DAG;HuPBA Approved no  
  Call Number Admin @ si @ EFP2011 Serial 1784  
Permanent link to this record
 

 
Author Jordi Esquirol; Cristina Palmero; Vanessa Bayo; Miquel Angel Cos; Sergio Escalera; David Sanchez; Maider Sanchez; Noelia Serrano; Mireia Relats edit  doi
openurl 
  Title Automatic RBG-depth-pressure anthropometric analysis and individualised sleep solution prescription Type Journal
  Year 2017 Publication Journal of Medical Engineering & Technology Abbreviated Journal JMET  
  Volume (down) 41 Issue 6 Pages 486-497  
  Keywords  
  Abstract INTRODUCTION:
Sleep surfaces must adapt to individual somatotypic features to maintain a comfortable, convenient and healthy sleep, preventing diseases and injuries. Individually determining the most adequate rest surface can often be a complex and subjective question.
OBJECTIVES:
To design and validate an automatic multimodal somatotype determination model to automatically recommend an individually designed mattress-topper-pillow combination.
METHODS:
Design and validation of an automated prescription model for an individualised sleep system is performed through a single-image 2 D-3 D analysis and body pressure distribution, to objectively determine optimal individual sleep surfaces combining five different mattress densities, three different toppers and three cervical pillows.
RESULTS:
A final study (n = 151) and re-analysis (n = 117) defined and validated the model, showing high correlations between calculated and real data (>85% in height and body circumferences, 89.9% in weight, 80.4% in body mass index and more than 70% in morphotype categorisation).
CONCLUSIONS:
Somatotype determination model can accurately prescribe an individualised sleep solution. This can be useful for healthy people and for health centres that need to adapt sleep surfaces to people with special needs. Next steps will increase model's accuracy and analise, if this prescribed individualised sleep solution can improve sleep quantity and quality; additionally, future studies will adapt the model to mattresses with technological improvements, tailor-made production and will define interfaces for people with special needs.
 
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  Notes HUPBA; no menciona;MILAB Approved no  
  Call Number Admin @ si @ EPB2017 Serial 3010  
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