D. Domínguez and R. R. Morales, Image Segmentation: Advances. Magnum Publishing LLC, vol.1, issue.1, 2016.

L. Guigues, J. P. Cocquerez, and H. Le-men, Scale-sets image analysis, International Journal of Computer Vision, vol.68, issue.3, pp.289-317, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00705364

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, vol.1, 2016.

C. Farabet, C. Couprie, L. Najman, and Y. Lecun, Learning Hierarchical Features for Scene Labeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1915-1929, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00742077

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, CoRR, 2014.

C. Lee, S. Xie, P. Gallagher, Z. Zhang, and Z. Tu, Deeply-Supervised Nets, Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, vol.38, pp.562-570, 2015.

S. Xie and Z. Tu, Holistically-nested edge detection, International Journal of Computer Vision, vol.125, issue.1, pp.3-18, 2017.

M. Cheng, Y. Liu, Q. Hou, J. Bian, P. Torr et al., HFS: Hierarchical Feature Selection for Efficient Image Segmentation, Computer Vision -ECCV 2016, pp.867-882, 2016.

, Visual representation of the results. The generated image was provided by KITTI Evaluation Server. Green pixels represents true positives, red pixels are false negatives and blue pixels are false positives

K. Maninis, J. Pont-tuset, P. Arbeláez, and L. Van-gool, Convolutional oriented boundaries, Computer Vision -ECCV 2016, pp.580-596, 2016.

Y. Liu, M. Cheng, X. Hu, K. Wang, and X. Bai, Richer convolutional features for edge detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5872-5881, 2017.

G. Yang, Z. Yu, and Y. Wang, A deep convolutional networks for monocular road segmentation *, 2018 Chinese Automation Congress (CAC), pp.3334-3338, 2018.

L. Najman and H. Talbot, Mathematical morphology: from theory to applications, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00622479

N. H. Saleem, R. Klette, and F. Huang, Use of a confidence map towards improved multi-layer stixel segmentation, IEEE International Conference on Advanced Video and Signal-based Surveillance AVSS2018), 2018.

M. Rezaei and R. Klette, Vision-Based Driver-Assistance Systems, pp.1-18, 2017.

R. Ilin, T. Watson, and R. Kozma, Abstraction hierarchy in deep learning neural networks, International Joint Conference on Neural Networks, vol.30, pp.768-774, 2017.

C. J. Kuo, Understanding convolutional neural networks with a mathematical model, Journal of Visual Communication and Image Representation, vol.41, pp.406-413, 2016.

D. Eigen, J. Rolfe, R. Fergus, and Y. Lecun, Understanding deep architectures using a recursive convolutional network, International Conference on Learning Representations, 2014.

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, Understanding deep learning requires rethinking generalization, International Conference on Learning Representations, 2017.

K. Simonyan, A. Vedaldi, and A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013.

M. D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks, European Conference on Computer Vision, vol.8689, pp.818-833, 2014.

S. Xie and Z. Tu, Holistically-nested edge detection, The IEEE International Conference on Computer Vision (ICCV), 2015.

J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3431-3440, 2015.

M. Oeljeklaus, F. Hoffmann, and T. Bertram, A fast multi-task cnn for spatial understanding of traffic scenes, 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp.2825-2830, 2018.

J. M. Alvarez, Y. Lecun, T. Gevers, and A. M. Lopez, Semantic road segmentation via multi-scale ensembles of learned features, Computer Vision -ECCV 2012. Workshops and Demonstrations, pp.586-595, 2012.

G. L. Oliveira, W. Burgard, and T. Brox, Efficient deep models for monocular road segmentation, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.4885-4891, 2016.

R. Jones, Component trees for image filtering and segmentation, IEEE Workshop on Nonlinear Signal and Image Processing, vol.9, 1997.

J. Cardelino, G. Randall, M. Bertalmio, and V. Caselles, Region based segmentation using the tree of shapes, 2006 International Conference on Image Processing, pp.2421-2424, 2006.

P. Soille and L. Najman, On morphological hierarchical representations for image processing and spatial data clustering, Applications of Discrete Geometry and Mathematical Morphology, pp.43-67, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00733251

Y. Xu, T. Graud, and L. Najman, Hierarchical image simplification and segmentation based on mumfordshah-salient level line selection, Pattern Recognition Letters, vol.83, pp.278-286, 2016.

J. Cousty, L. Najman, Y. Kenmochi, and S. Guimarães, Hierarchical segmentations with graphs: Quasi-flat zones, minimum spanning trees, and saliency maps, Journal of Mathematical Imaging and Vision, vol.60, issue.4, pp.479-502, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01344727

V. Dumoulin and F. Visin, A guide to convolution arithmetic for deep learning, 2016.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016.

J. Fritsch, T. Kuehnl, and A. Geiger, A new performance measure and evaluation benchmark for road detection algorithms, International Conference on Intelligent Transportation Systems (ITSC), 2013.

F. Chollet, Keras, 2015.

M. Abadi, TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.

L. Caltagirone, M. Bellone, L. Svensson, and M. Wahde, Lidar-camera fusion for road detection using fully convolutional neural networks, Robotics and Autonomous Systems, 2018.