超像素分割github资源列表

2019-03-02 17:59

https://github.com/davidstutz/superpixel-benchmark/blob/master/docs/ALGORITHMS.md 

 

Algorithms

An overview of the evaluated algorithms can be found below. More details can be found in the paper or in the original README found in the respective library directory. Also check the corresponding web pages for author and license information. The corresponding references are given below the table.

Algorithm Library Executable Implementation Reference Link
CCS lib_ccs ccs_cli C++ [22,23] Web
CIS lib_cis cis_cli C++ [10] Web
CRS lib_crs crs_cli C++ [13,14] Web
CW lib_cw cw_cli C++ [25] Web
DASP lib_dasp dasp_cli C++ [17] Web
EAMS lib_eams eams_cli MatLab [2] Web
ERS lib_ers ers_cli C++ [15] Web
FH lib_fh fh_cli C++ [4] Web
reFH lib_refh refh_cli C++ -- Web
MSS lib_mss mss_cli C++ [28] --
PB lib_pb pb_cli C++ [16] Web
preSLIC lib_preslic preslic_cli C++ [25] Web
SEEDS lib_seeds seeds_cli C++ [18] Web
reSEEDS lib_reseeds reseeds_cli C++ -- Web
SEAW lib_seaw seaw_cli MatLab [34] Web
SLIC lib_slic slic_cli C++ [11,12] Web
vlSLIC lib_clslic vlslic_cli C++ -- Web
TP lib_tp tp_cli MatLab [9] Web
TPS lib_tps tps_cli MatLab [19,20] Web
W lib_w w_cli C++ [1] Web
WP lib_wp wp_cli Python [29,30] Web
PF lib_pf pf_cli Java [8] Web
LSC lib_lsc lsc_cli C++ [32] Web
RW lib_rw rw_cli MatLab [5, 6] Web
QS lib_qs qs_cli MatLab [7] Web
NC lib_nc nc_cli C++ [3] Web
VCCS lib_vccs vccs_cli MatLab [24] Web
POISE lib_poise poise_cli MatLab [33] Web
VC lib_vc vc_cli C++ [21] Web
ETPS lib_etps etps_cli C++ [31] Web
ERGC lib_ergc ergc_cli C++ [26,27] Web
[1] F. Meyer.
    Color image segmentation.
    International Conference on Image Processing and its Applications, 1992, pp. 303-306.
[2] D. Comaniciu, P. Meer.
    Mean shift: A robust approach toward feature space analysis.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (5) (2002) 603–619.
[3] X. Ren, J. Malik.
    Learning a classification model for segmentation.
    International Conference on Computer Vision, 2003, pp. 10–17.
[4] P. F. Felzenswalb, D. P. Huttenlocher.
    Efficient graph-based image segmentation.
    International Journal of Computer Vision 59 (2) (2004) 167–181.
[5] L. Grady, G. Funka-Lea.
    Multi-label image segmentation for medical applications based on graph-theoretic electrical potentials.
    ECCV Workshops on Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis, 2004, pp. 230–245.
[6] L. Grady.
    Random walks for image segmentation.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 28 (11) (2006) 1768–1783.
[7] A. Vedaldi, S. Soatto.
    Quick shift and kernel methods for mode seeking.
    European Conference on Computer Vision, Vol. 5305, 2008, pp. 705–718.
[8] F. Drucker, J. MacCormick.
    Fast superpixels for video analysis.
    Workshop on Motion and Video Computing, 2009, pp. 1–8.
[9] A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson, K. Siddiqi.
    TurboPixels: Fast superpixels using geometric flows.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (12) (2009) 2290–2297.
[10] O. Veksler, Y. Boykov, P. Mehrani.
     Superpixels and supervoxels in an energy optimization framework.
     European Conference on Computer Vision, Vol. 6315, 2010, pp. 211–224.
[11] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk.
     SLIC superpixels.
     Tech. rep., Ecole Polytechnique Federale de Lausanne (2010).
[12] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Susstrunk.
     SLIC superpixels compared to state-of-the-art superpixel methods.
     IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11) (2012) 2274–2281.
[13] R. Mester, C. Conrad, A. Guevara.
     Multichannel segmentation using contour relaxation: Fast super-pixels and temporal propagation.
     Scandinavian Conference Image Analysis, 2011, pp. 250–261.
[14] C. Conrad, M. Mertz, R. Mester, Contour-relaxed superpixels. 
     Energy Minimization Methods.
     Computer Vision and Pattern Recognition, 2013, pp. 280–293.
[15] M. Y. Lui, O. Tuzel, S. Ramalingam, R. Chellappa.
     Entropy rate superpixel segmentation.
     IEEE Conference on Computer Vision and Pattern Recognition, 2011, pp. 2097–2104.
[16] Y. Zhang, R. Hartley, J. Mashford, S. Burn.
     Superpixels via pseudo-boolean optimization.
     International Conference on Computer Vision, 2011, pp. 1387–1394.
[17] D. Weikersdorfer, D. Gossow, M. Beetz.
     Depth-adaptive superpixels.
     International Conference on Pattern Recognition, 2012, pp. 2087–2090.
[18] M. van den Bergh, X. Boix, G. Roig, B. de Capitani, L. van Gool.
     SEEDS: Superpixels extracted via energy-driven sampling.
     European Conference on Computer Vision, Vol. 7578, 2012, pp. 13–26.
[19] D. Tang, H. Fu, X. Cao.
     Topology preserved regular superpixel.
     IEEE International Conference on Multimedia and Expo, 2012, pp. 765–768.
[20] H. Fu, X. Cao, D. Tang, Y. Han, D. Xu.
     Regularity preserved superpixels and supervoxels.
     IEEE Transactions on Multimedia 16 (4) (2014) 1165–1175.
[21] J. Wang, X. Wang.
     VCells: Simple and efficient superpixels using edge-weighted centroidal voronoi tessellations.
     IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (6)(2012) 1241–1247.
[22] H. E. Tasli, C. Cigla, T. Gevers, A. A. Alatan.
     Super pixel extraction via convexity induced boundary adaptation.
     IEEE International Conference on Multimedia and Expo, 2013, pp. 1–6.
[23] H. E. Tasli, C. Cigla, A. A. Alatan.
     Convexity constrained efficient superpixel and supervoxel extraction.
     Signal Processing: Image Communication 33 (2015) 71–85.
[24] J. Papon, A. Abramov, M. Schoeler, F. Wörgötter.
     Voxel cloud connectivity segmentation - supervoxels for point clouds.
     IEEE Conference on Computer Vision and Pattern Recognition, 2013, pp. 2027–2034.
[25] P. Neubert, P. Protzel.
     Compact watershed and preemptive SLIC: on improving trade-offs of superpixel segmentation algorithms.
     International Conference on Pattern Recognition, 2014, pp. 996–1001.
[26] P. Buyssens, I. Gardin, S. Ruan.
     Eikonal based region growing for superpixels generation: Application to semi-supervised real time organ segmentation in CT images.
     Innovation and Research in BioMedical Engineering 35 (1) (2014) 20–26.
[27] P. Buyssens, M. Toutain, A. Elmoataz, O. Lézoray.
     Eikonal-based vertices growing and iterative seeding for efficient graph-based segmentation.
     International Conference on Image Processing, 2014, pp. 4368–4372
[28] W. Benesova, M. Kottman.
     Fast superpixel segmentation using morphological processing.
     Conference on Machine Vision and Machine Learning, 2014.
[29] V. Machairas, E. Decencière, T. Walter.
     Waterpixels: Superpixels based on the watershed transformation.
     International Conference on Image Processing, 2014, pp. 4343–4347.
[30] V. Machairas, M. Faessel, D. Cardenas-Pena, T. Chabardes, T. Walter, E. Decencière.
     Waterpixels.
     Transactions on Image Processing 24 (11) (2015) 3707–3716.
[31] J. Yao, M. Boben, S. Fidler, R. Urtasun.
     Real-time coarse-to-fine topologically preserving segmentation.
     IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2947–2955.
[32] Z. Li, J. Chen.
     Superpixel segmentation using linear spectral clustering.
     IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 1356–1363.
[33] J. M. R. A. Humayun, F. Li.
     The middle child problem: Revisiting parametric min-cut and seeds for object proposals.
     International Conference on Computer Vision, 2015, pp. 1600–1608.
[34] J. Strassburg, R. Grzeszick, L. Rothacker, G. A. Fink.
     On the influence of superpixel methods for image parsing.
     International Conference on Computer Vision Theory and Application, 2015, pp. 518–527.

点击微信扫一扫

最近留言

没有数据
扫描二维码访问
欢迎使用手机扫描二维码访问