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Author Fernando Barrera; Felipe Lumbreras; Angel Sappa edit   pdf
doi  openurl
  Title Multimodal Stereo Vision System: 3D Data Extraction and Algorithm Evaluation Type Journal Article
  Year 2012 Publication IEEE Journal of Selected Topics in Signal Processing Abbreviated Journal J-STSP  
  Volume 6 Issue 5 Pages 437-446  
  Keywords  
  Abstract This paper proposes an imaging system for computing sparse depth maps from multispectral images. A special stereo head consisting of an infrared and a color camera defines the proposed multimodal acquisition system. The cameras are rigidly attached so that their image planes are parallel. Details about the calibration and image rectification procedure are provided. Sparse disparity maps are obtained by the combined use of mutual information enriched with gradient information. The proposed approach is evaluated using a Receiver Operating Characteristics curve. Furthermore, a multispectral dataset, color and infrared images, together with their corresponding ground truth disparity maps, is generated and used as a test bed. Experimental results in real outdoor scenarios are provided showing its viability and that the proposed approach is not restricted to a specific domain.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 1932-4553 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ BLS2012b Serial 2155  
Permanent link to this record
 

 
Author Katerine Diaz; Francesc J. Ferri; W. Diaz edit  doi
openurl 
  Title Incremental Generalized Discriminative Common Vectors for Image Classification Type Journal Article
  Year 2015 Publication IEEE Transactions on Neural Networks and Learning Systems Abbreviated Journal TNNLS  
  Volume 26 Issue 8 Pages 1761 - 1775  
  Keywords  
  Abstract Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model.  
  Address  
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  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 2162-237X ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ DFD2015 Serial 2547  
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Author Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva edit   pdf
url  doi
openurl 
  Title Occlusion handling via random subspace classifiers for human detection Type Journal Article
  Year 2014 Publication IEEE Transactions on Systems, Man, and Cybernetics (Part B) Abbreviated Journal TSMCB  
  Volume 44 Issue 3 Pages 342-354  
  Keywords Pedestriand Detection; occlusion handling  
  Abstract This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes  
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  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 2168-2267 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 Approved no  
  Call Number ADAS @ adas @ MVL2014 Serial 2213  
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Author Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores edit   pdf
doi  openurl
  Title On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts Type Journal Article
  Year 2017 Publication IEEE Transactions on cybernetics Abbreviated Journal Cyber  
  Volume 47 Issue 11 Pages 3980 - 3990  
  Keywords Multicue; multimodal; multiview; object detection  
  Abstract Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient.  
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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 2168-2267 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.085; 600.082; 600.076; 600.118 Approved no  
  Call Number Admin @ si @ Serial 2810  
Permanent link to this record
 

 
Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil edit  doi
openurl 
  Title Continuous head pose estimation using manifold subspace embedding and multivariate regression Type Journal Article
  Year 2018 Publication IEEE ACCESS Abbreviated Journal ACCESS  
  Volume 6 Issue Pages 18325 - 18334  
  Keywords Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression  
  Abstract In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN (up) 2169-3536 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ DMH2018b Serial 3091  
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