R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua et al., SLIC Superpixels Compared to State-of-the-Art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2274-2282, 2012.
DOI : 10.1109/TPAMI.2012.120

P. Arbelaez and L. Cohen, Constrained image segmentation from hierarchical boundaries, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587492
URL : https://hal.archives-ouvertes.fr/hal-00708568

P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik, From contours to regions: 33, pp.898-916, 2009.
DOI : 10.1109/cvprw.2009.5206707

J. E. Bresenham, Algorithm for computer control of a digital plotter, IBM Systems Journal, vol.4, issue.1, pp.25-30, 1965.
DOI : 10.1147/sj.41.0025

P. Buyssens, I. Gardin, S. Ruan, and A. Elmoataz, Eikonal-based region growing for efficient clustering, Image and Vision Computing, vol.32, issue.12, pp.1045-1054, 2014.
DOI : 10.1016/j.imavis.2014.10.002
URL : https://hal.archives-ouvertes.fr/hal-01134406

J. Chang, D. Wei, and J. W. Fisher, A Video Representation Using Temporal Superpixels, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.2051-2058, 2013.
DOI : 10.1109/CVPR.2013.267
URL : http://people.csail.mit.edu/donglai/paper/chang13_CVPR.pdf

J. Chen, Z. Li, and B. Huang, Linear Spectral Clustering Superpixel, IEEE Transactions on Image Processing, vol.26, issue.7, 2017.
DOI : 10.1109/TIP.2017.2651389

K. S. Choi and K. W. Oh, Subsampling-based acceleration of simple linear iterative clustering for superpixel segmentation, Computer Vision and Image Understanding, vol.146, pp.1-8, 2016.
DOI : 10.1016/j.cviu.2016.02.018

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236
URL : http://nichol.as/papers/Comaniciu/Mean%20Shift:%20A%20Robust%20Approach%20Toward.pdf

P. Dollár and C. L. Zitnick, Structured Forests for Fast Edge Detection, 2013 IEEE International Conference on Computer Vision, pp.1841-1848, 2013.
DOI : 10.1109/ICCV.2013.231

P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, vol.59, issue.2, pp.167-181, 2004.
DOI : 10.1023/B:VISI.0000022288.19776.77

B. Fulkerson, A. Vedaldi, and S. Soatto, Class segmentation and object localization with superpixel neighborhoods, 2009 IEEE 12th International Conference on Computer Vision, pp.670-677, 2009.
DOI : 10.1109/ICCV.2009.5459175
URL : http://www.vision.cs.ucla.edu/papers/fulkersonVS09.pdf

R. Giraud, V. T. Ta, and N. Papadakis, SCALP: Superpixels with Contour Adherence using Linear Path, 2016 23rd International Conference on Pattern Recognition (ICPR), pp.2374-2379, 2016.
DOI : 10.1109/ICPR.2016.7899991
URL : https://hal.archives-ouvertes.fr/hal-01349569

R. Giraud, V. T. Ta, and N. Papadakis, Robust shape regularity criteria for superpixel evaluation, Proc. of IEEE International Conference on Image Processing, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01510062

R. Giraud, V. T. Ta, and N. Papadakis, Evaluation framework of superpixel methods with a global regularity measure, Journal of Electronic Imaging, vol.26, issue.6, pp.26-44, 2017.
DOI : 10.1117/1.JEI.26.6.061603
URL : https://hal.archives-ouvertes.fr/hal-01519635

S. Gould, J. Rodgers, D. Cohen, G. Elidan, and D. Koller, Multi-Class Segmentation with Relative Location Prior, International Journal of Computer Vision, vol.30, issue.6, pp.300-316, 2008.
DOI : 10.1007/s11263-008-0140-x
URL : http://ai.stanford.edu/~koller/Papers/Gould+al:IJCV08.pdf

S. Gould, J. Zhao, X. He, and Y. Zhang, Superpixel Graph Label Transfer with Learned Distance Metric, Proc. of European Conference on Computer Vision, pp.632-647, 2014.
DOI : 10.1007/978-3-319-10590-1_41
URL : http://users.cecs.anu.edu.au/~sgould/papers/eccv14-spgraph.pdf

V. Gulshan, C. Rother, A. Criminisi, A. Blake, and A. Zisserman, Geodesic star convexity for interactive image segmentation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3129-3136, 2010.
DOI : 10.1109/CVPR.2010.5540073
URL : http://eprints.pascal-network.org/archive/00006962/01/gulshan10.pdf

C. R. Huang, W. A. Wang, S. Y. Lin, and Y. Y. Lin, USEQ: Ultra-fast superpixel extraction via quantization, International Conference on Pattern Recognition, 2016.

A. Kae, K. Sohn, H. Lee, and E. Learned-miller, Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.2019-2026, 2013.
DOI : 10.1109/CVPR.2013.263
URL : http://vis-www.cs.umass.edu/papers/gloc_cvpr13.pdf

A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson et al., TurboPixels: Fast Superpixels Using Geometric Flows, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.12, pp.2290-2297, 2009.
DOI : 10.1109/TPAMI.2009.96
URL : http://www.cim.mcgill.ca/~shape/publications/pami09.pdf

M. Y. Liu, O. Tuzel, S. Ramalingam, and R. Chellappa, Entropy rate superpixel segmentation, CVPR 2011, pp.2097-2104, 2011.
DOI : 10.1109/CVPR.2011.5995323
URL : http://www.umiacs.umd.edu/~mingyliu/papers/liu_cvpr2011.pdf

S. Lloyd, Least squares quantization in PCM, IEEE Transactions on Information Theory, vol.28, issue.2, pp.129-137, 1982.
DOI : 10.1109/TIT.1982.1056489
URL : http://www.cs.toronto.edu/~roweis/csc2515-2006/readings/lloyd57.pdf

V. Machairas, M. Faessel, D. Cárdenas-peña, T. Chabardes, T. Walter et al., Waterpixels, IEEE Transactions on Image Processing, vol.24, issue.11, pp.3707-3716, 2015.
DOI : 10.1109/TIP.2015.2451011
URL : https://hal.archives-ouvertes.fr/hal-01212760

M. Maire, P. Arbelaez, C. Fowlkes, and J. Malik, Using contours to detect and localize junctions in natural images, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587420

D. Martin, C. Fowlkes, D. Tal, and J. Malik, A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.416-423, 2001.
DOI : 10.1109/ICCV.2001.937655

D. Martin, C. Fowlkes, and J. Malik, Learning to detect natural image boundaries using local brightness, color, and texture cues, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.5, pp.530-549, 2004.
DOI : 10.1109/TPAMI.2004.1273918
URL : http://statistic.gunadarma.ac.id/research/texture/i0530.pdf

B. Menze, A. Jakab, and S. Bauer, The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), IEEE Transactions on Medical Imaging, vol.34, issue.10, 1993.
DOI : 10.1109/TMI.2014.2377694
URL : https://hal.archives-ouvertes.fr/hal-00935640

G. Mori, X. Ren, A. A. Efros, and J. Malik, Recovering human body configurations: combining segmentation and recognition, Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp.326-333, 2004.
DOI : 10.1109/cvpr.2004.1315182

P. Neubert and P. Protzel, Superpixel benchmark and comparison, in: Forum Bildverarbeitung, pp.1-12, 2012.

P. Neubert and P. Protzel, Compact Watershed and Preemptive SLIC: On Improving Trade-offs of Superpixel Segmentation Algorithms, 2014 22nd International Conference on Pattern Recognition, pp.996-1001, 2014.
DOI : 10.1109/ICPR.2014.181

M. Reso, J. Jachalsky, B. Rosenhahn, and J. Ostermann, Temporally Consistent Superpixels, 2013 IEEE International Conference on Computer Vision, pp.385-392, 2013.
DOI : 10.1109/ICCV.2013.55
URL : http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Reso_Temporally_Consistent_Superpixels_2013_ICCV_paper.pdf

A. Rubio, L. Yu, E. Simo-serra, and F. Moreno-noguer, BASS: Boundary-Aware Superpixel Segmentation, 2016 23rd International Conference on Pattern Recognition (ICPR), 2016.
DOI : 10.1109/ICPR.2016.7900064
URL : http://upcommons.upc.edu/bitstream/2117/106299/1/1820-BASS--Boundary-Aware-Superpixel-Segmentation%20%281%29.pdf

R. Sawhney, F. Li, and H. I. Christensen, GASP: Geometric Association with Surface Patches, 2014 2nd International Conference on 3D Vision, 2014.
DOI : 10.1109/3DV.2014.113
URL : http://arxiv.org/pdf/1411.4098.pdf

A. Schick, M. Fischer, and R. Stiefelhagen, Measuring and evaluating the compactness of superpixels, Proc. of International Conference on Pattern Recognition, pp.930-934, 2012.

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. and Mach. Intell, vol.22, pp.888-905, 2000.

J. Strassburg, R. Grzeszick, L. Rothacker, and G. A. Fink, On the Influence of Superpixel Methods for Image Parsing, Proceedings of the 10th International Conference on Computer Vision Theory and Applications, pp.518-527, 2015.
DOI : 10.5220/0005355705180527

D. Stutz, A. Hermans, and B. Leibe, Superpixels: An evaluation of the state-of-the-art, Computer Vision and Image Understanding, vol.166, 2017.
DOI : 10.1016/j.cviu.2017.03.007

J. Tighe and S. Lazebnik, Superparsing, Proc. of European Conference on Computer Vision, pp.352-365, 2010.
DOI : 10.1109/TPAMI.2008.128

M. Van-den-bergh, X. Boix, G. Roig, B. De-capitani, and L. Van-gool, SEEDS: Superpixels extracted via energy-driven sampling, Proc. of European Conference on Computer Vision, pp.13-26, 2012.

A. Vedaldi and S. Soatto, Quick Shift and Kernel Methods for Mode Seeking, Proc. of European Conference on Computer Vision, pp.705-718, 2008.
DOI : 10.1007/978-3-540-88693-8_52
URL : http://vision.ucla.edu/papers/vedaldiS08quick.pdf

O. Veksler, Y. Boykov, and P. Mehrani, Superpixels and Supervoxels in an Energy Optimization Framework, Proc. of European Conference on Computer Vision, pp.211-224, 2010.
DOI : 10.1007/978-3-642-15555-0_16
URL : http://www.csd.uwo.ca/~yuri/Papers/eccv10-superpix.pdf

L. Vincent and P. Soille, Watersheds in digital spaces: an efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.6, pp.583-598, 1991.
DOI : 10.1109/34.87344

P. Wang, G. Zeng, R. Gan, J. Wang, and H. Zha, Structure-Sensitive Superpixels via Geodesic Distance, International Journal of Computer Vision, vol.22, issue.8, pp.1-21, 2013.
DOI : 10.1016/j.jcp.2005.08.005

S. Wang, H. Lu, F. Yang, and M. H. Yang, Superpixel tracking, Proc. of IEEE International Conference on Computer Vision, pp.1323-1330, 2011.

R. Xiaofeng and L. Bo, Discriminatively trained sparse code gradients for contour detection, Proc. of Conf. on Neural Information Processing Systems, pp.584-592, 2012.

C. Xu and J. J. Corso, Evaluation of super-voxel methods for early video processing, Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp.1202-1209, 2012.

Y. Yang, S. Hallman, D. Ramanan, and C. Fowlkes, Layered object detection for multi-class segmentation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3113-3120, 2010.
DOI : 10.1109/CVPR.2010.5540070
URL : http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_yang.pdf

J. Yao, M. Boben, S. Fidler, and R. Urtasun, Real-time coarse-to-fine topologically preserving segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2947-2955, 2015.
DOI : 10.1109/CVPR.2015.7298913

N. Zhang and L. Zhang, SSGD: Superpixels using the shortest gradient distance, Proc. of the IEEE International Conference on Image Processing, 2017.

Y. Zhang, X. Li, X. Gao, and C. Zhang, A Simple Algorithm of Superpixel Segmentation with Boundary Constraint, IEEE Transactions on Circuits and Systems for Video Technology, pp.1-1, 2016.
DOI : 10.1109/TCSVT.2016.2539839