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Author Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta edit   pdf
doi  openurl
  Title Semantic Pyramids for Gender and Action Recognition Type Journal Article
  Year 2014 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 23 Issue 8 Pages 3633-3645  
  Keywords  
  Abstract Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition.  
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  Series Volume Series Issue Edition  
  ISSN 1057-7149 ISBN Medium  
  Area Expedition Conference  
  Notes (down) CIC; LAMP; 601.160; 600.074; 600.079;MILAB;ADAS Approved no  
  Call Number Admin @ si @ KWR2014 Serial 2507  
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg edit   pdf
doi  openurl
  Title Coloring Action Recognition in Still Images Type Journal Article
  Year 2013 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 105 Issue 3 Pages 205-221  
  Keywords  
  Abstract In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.  
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  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes (down) CIC; ADAS; 600.057; 600.048 Approved no  
  Call Number Admin @ si @ KRW2013 Serial 2285  
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Author A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva edit  doi
openurl 
  Title Topological principal component analysis for face encoding and recognition Type Journal Article
  Year 2001 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 22 Issue 6-7 Pages 769–776  
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  Abstract IF: 0.552  
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  Notes (down) ADAS;OR;MV Approved no  
  Call Number ADAS @ adas @ PVL2001 Serial 155  
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Author Jaume Amores; Petia Radeva edit  url
doi  openurl
  Title Registration and Retrieval of Highly Elastic Bodies using Contextual Information Type Journal Article
  Year 2005 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 26 Issue 11 Pages 1720–1731  
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  Abstract IF: 1.138  
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  Notes (down) ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ AmR2005b Serial 592  
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Author Jaume Amores; Petia Radeva edit  openurl
  Title Retrieval of IVUS Images Using Contextual Information and Elastic Matching Type Journal
  Year 2005 Publication International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657) Abbreviated Journal  
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  Notes (down) ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ AmR2005a Serial 593  
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Author Jaume Amores; N. Sebe; Petia Radeva edit  doi
openurl 
  Title Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier Type Journal Article
  Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 27 Issue 3 Pages 201–209  
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  Notes (down) ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ ASR2006 Serial 643  
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Author Jaume Amores; N. Sebe; Petia Radeva edit  openurl
  Title Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms Type Journal
  Year 2007 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29(10):1818–1833, (ISI 3,81) Abbreviated Journal  
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  Notes (down) ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ ASR2007b Serial 922  
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Author Yu Jie; Jaume Amores; N. Sebe; Petia Radeva; Tian Qi edit  openurl
  Title Distance Learning for Similarity Estimation Type Journal
  Year 2008 Publication IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(3):451–462 Abbreviated Journal  
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  Notes (down) ADAS;MILAB Approved no  
  Call Number ADAS @ adas @ JAS2008 Serial 961  
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Author Daniel Ponsa; Robert Benavente; Felipe Lumbreras; Judit Martinez; Xavier Roca edit  doi
openurl 
  Title Quality control of safety belts by machine vision inspection for real-time production Type Journal Article
  Year 2003 Publication Optical Engineering (IF: 0.877) Abbreviated Journal  
  Volume 42 Issue 4 Pages 1114-1120  
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  Publisher SPIE Place of Publication Editor  
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  Notes (down) ADAS;ISE;CIC Approved no  
  Call Number ADAS @ adas @ PRL2003 Serial 399  
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Author Jose Manuel Alvarez; Theo Gevers; Antonio Lopez edit   pdf
doi  openurl
  Title Learning photometric invariance for object detection Type Journal Article
  Year 2010 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 90 Issue 1 Pages 45-61  
  Keywords road detection  
  Abstract Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile
Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.
Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.
Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods
 
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  Publisher Springer US Place of Publication Editor  
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  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes (down) ADAS;ISE Approved no  
  Call Number ADAS @ adas @ AGL2010c Serial 1451  
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