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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 ADAS;MILAB Approved no  
  Call Number (down) ADAS @ adas @ ASR2007b Serial 922  
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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 ADAS;MILAB Approved no  
  Call Number (down) ADAS @ adas @ ASR2006 Serial 643  
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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 ADAS;MILAB Approved no  
  Call Number (down) 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 ADAS;MILAB Approved no  
  Call Number (down) ADAS @ adas @ AmR2005a Serial 593  
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Author Jose Manuel Alvarez; Antonio Lopez edit   pdf
openurl 
  Title Road Detection Based on Illuminant Invariance Type Journal Article
  Year 2011 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS  
  Volume 12 Issue 1 Pages 184-193  
  Keywords road detection  
  Abstract By using an onboard camera, it is possible to detect the free road surface ahead of the ego-vehicle. Road detection is of high relevance for autonomous driving, road departure warning, and supporting driver-assistance systems such as vehicle and pedestrian detection. The key for vision-based road detection is the ability to classify image pixels as belonging or not to the road surface. Identifying road pixels is a major challenge due to the intraclass variability caused by lighting conditions. A particularly difficult scenario appears when the road surface has both shadowed and nonshadowed areas. Accordingly, we propose a novel approach to vision-based road detection that is robust to shadows. The novelty of our approach relies on using a shadow-invariant feature space combined with a model-based classifier. The model is built online to improve the adaptability of the algorithm to the current lighting and the presence of other vehicles in the scene. The proposed algorithm works in still images and does not depend on either road shape or temporal restrictions. Quantitative and qualitative experiments on real-world road sequences with heavy traffic and shadows show that the method is robust to shadows and lighting variations. Moreover, the proposed method provides the highest performance when compared with hue-saturation-intensity (HSI)-based algorithms.  
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  Call Number (down) ADAS @ adas @ AlL2011 Serial 1456  
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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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  ISSN 0920-5691 ISBN Medium  
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  Notes ADAS;ISE Approved no  
  Call Number (down) ADAS @ adas @ AGL2010c Serial 1451  
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