Demos
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Romera, Alvarez, Bergasa and Arroyo. Efficient ConvNets for Real-Time Semantic Segmentation. IV'2017.
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A compact ConvNet running at real-time (65fps +) in an embedded platform. A join work with Lars Andersson and Lars Petersson.
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José M. Álvarez and Lars Petersson. DecomposeMe: Simplifying ConvNets for end to end Learning. In Arxiv.
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German Ros and José M. Álvarez. Unsupervised Image Transformation for Outdoor Semantic Labelling. In IEEE-IV 2015.
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José M. Álvarez, Mathieu Salzmann and Nick Barnes. Large-Scale Semantic Co-Labeling of Image Sets. In WACV'14.
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A Convolutional Neural network is trained using a large dataset with machine-generated labels (weak ground truth). These labels are generated using a general algorithm. The algorithm shows how convolutional neural networks can generalize from these weakly annotated data.
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Jose M. Alvarez, Theo Gevers, Yann LeCun and Antonio M. Lopez, Road Scene Understanding from a Single Image. European Conference Computer Vision (ECCV 2012)
José M. Álvarez, Antonio M. López, Theo Gevers and Felipe Lumbreras.
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José M. Álvarez, Antonio M. Lopez, Theo Gevers and Felipe Lumbreras. Combining Priors, Color and Context for Road Detection. IEEE Trans. Intelligent Transportation Systems (ITS), to appear.
José M. Álvarez, Felipe Lumbreras, Theo Gevers and Antonio M. López. Geographic Information for Vision-Based Road Detection. IEEE Intelligent Vehicle Symposium (IV'10), San Diego, CA. 2010.
Method for Obtaining Drivable Road Area. José M. Álvarez and Felipe Lumbreras.
Jose M. Alvarez and Antonio M. Lopez
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By using an on-board 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 to support 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 intra-class variability caused by lighting conditions. An especially difficult scenario appears when the road surface has both shadowed and non--shadowed 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 on-line 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 either on road shape or temporal restrictions. Quantitative and qualitative results are presented to support the validity of our proposal. |
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Jose M. Alvarez and Antonio M. Lopez, Road Detection based on Illuminant Invariance. IEEE Trans. Intelligent Transportation Systems (ITS).
Jose M. Alvarez and Antonio M. Lopez and R. Baldrich, Illuminant Invariant Model-Based Road Segmentation. IEEE Intelligent Vehicles Symposium (IV'08).
José M. Álvarez, Theo Gevers and Antonio M. López.
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José M. Álvarez, Theo Gevers and Antonio M. López. Learning Photometric Invariance for Object Detection. In International Journal of Computer Vision Volume 90, Number 1, 45-6, 2010.
José M. Álvarez, Theo Gevers and Antonio M. Lopez. Learning Photometric Invariance from Diversified Color Model Ensembles. In Proc. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR'09), Miami, Fl 2009.
José M. Álvarez and Jose López-López.
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"J. López-López, Jose M. Alvarez, E. Jané-Salas, A. Estrugo-Devesa, R. Ayuso-Montero, E. Velasco-Ortega, Computer-Aided System for Morphometric Mandibular Index Computation (using dental panoramic radiographs). Dentistry, Oral Surgery & Medicine
J. Serrat, F. Diego, F. Lumbreras, José M. Álvarez and Antonio M. López.
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J. Serrat, F. Diego, F. Lumbreras, José M. Álvarez, Antonio M. López and C. Elvira. Dynamic comparison of headlights. In Jourmal of Automobile Engineering, Proc. of the Institution of Mechanical Engineers, Part D, June 2007