A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets
10.1007/978-3-319-46478-7_35
Crossref book-chapter
Springer International Publishing
Lecture Notes in Computer Science (297)
Bibliography

Haubold, C., Aleš, J., Wolf, S., & Hamprecht, F. A. (2016). A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets. Computer Vision – ECCV 2016, 566–582.

Authors 4
  1. Carsten Haubold (first)
  2. Janez Aleš (additional)
  3. Steffen Wolf (additional)
  4. Fred A. Hamprecht (additional)
References 25 Referenced 7
  1. Bise, R., Yin, Z., Kanade, T.: Reliable cell tracking by global data association. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1004–1010 (2011) (10.1109/ISBI.2011.5872571)
  2. Schiegg, M., Hanslovsky, P., Kausler, B.X., Hufnagel, L., Hamprecht, F.A.: Conservation tracking. In: IEEE International Conference on Computer Vision (ICCV), pp. 2928–2935. IEEE (2013) (10.1109/ICCV.2013.364)
  3. Maška, M., Ulman, V., Svoboda, D., Matula, P., Matula, P., Ederra, C., Urbiola, A., España, T., Venkatesan, S., Balak, D.M., et al.: A benchmark for comparison of cell tracking algorithms. Bioinformatics 30(11), 1609–1617 (2014) (10.1093/bioinformatics/btu080) / Bioinformatics by M Maška (2014)
  4. Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: IEEE Conference on Computer Vision and Pattern Recognition,(CVPR), pp. 1–8. IEEE (2008) (10.1109/CVPR.2008.4587584)
  5. Pirsiavash, H., Ramanan, D., Fowlkes, C.C.: Globally-optimal greedy algorithms for tracking a variable number of objects. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1201–1208. IEEE (2011) (10.1109/CVPR.2011.5995604)
  6. Lenz, P., Geiger, A., Urtasun, R.: Followme: Efficient online min-cost flow tracking with bounded memory and computation. In: IEEE International Conference on Computer Vision (ICCV), pp. 4364–4372 (2015) (10.1109/ICCV.2015.496)
  7. Berclaz, J., Fleuret, F., Türetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1806–1819 (2011) (10.1109/TPAMI.2011.21) / IEEE Trans. Pattern Anal. Mach. Intell. by J Berclaz (2011)
  8. Padfield, D., Rittscher, J., Roysam, B.: Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Med. Image Anal. 15(4), 650–668 (2011) (10.1016/j.media.2010.07.006) / Med. Image Anal. by D Padfield (2011)
  9. Butt, A., Collins, R.: Multi-target tracking by Lagrangian relaxation to min-cost network flow. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1846–1853.IEEE (2013) (10.1109/CVPR.2013.241)
  10. Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logistics Q. 2(1–2), 83–97 (1955) (10.1002/nav.3800020109) / Naval Res. Logistics Q. by HW Kuhn (1955)
  11. Xing, J., Ai, H., Lao, S.: Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In: CVPR 2013 (2009)
  12. Castanon, G., Finn, L.: Multi-target tracklet stitching through network flows. In: IEEE Aerospace Conference, pp. 1–7. IEEE (2011) (10.1109/AERO.2011.5747436)
  13. Jaqaman, K., Loerke, D., Mettlen, M., Kuwata, H., Grinstein, S., Schmid, S.L., Danuser, G.: Robust single-particle tracking in live-cell time-lapse sequences. Nat. Methods 5(8), 695–702 (2008) (10.1038/nmeth.1237) / Nat. Methods by K Jaqaman (2008)
  14. Andriyenko, A., Schindler, K., Roth, S.: Discrete-continuous optimization for multi-target tracking. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1926–1933. IEEE (2012) (10.1109/CVPR.2012.6247893)
  15. Brendel, W., Amer, M., Todorovic, S.: Multiobject tracking as maximum weight independent set. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1273–1280. IEEE (2011) (10.1109/CVPR.2011.5995395)
  16. Wang, X., Türetken, E., Fleuret, F., Fua, P.: Tracking interacting objects optimally using integer programming. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 17–32. Springer, Heidelberg (2014). doi: 10.1007/978-3-319-10590-1_2 (10.1007/978-3-319-10590-1_2) / Computer Vision – ECCV 2014 / Lecture Notes in Computer Science by X Wang (2014)
  17. Magnusson, K., Jalden, J., Gilbert, P., Blau, H.: Global linking of cell tracks using the Viterbi algorithm. Trans. Med. Imaging 34(4), 911–929 (2014) (10.1109/TMI.2014.2370951) / Trans. Med. Imaging by K Magnusson (2014)
  18. Bertsekas, D.P.: Network Optimization: Continuous and Discrete Models. Athena Scientific, Belmont (1998) / Network Optimization: Continuous and Discrete Models by DP Bertsekas (1998)
  19. Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network flows. Technical report, DTIC Document (1988) (10.21236/ADA594171)
  20. Cormen, T.H.: Introduction to Algorithms. MIT press, Cambridge (2009) / Introduction to Algorithms by TH Cormen (2009)
  21. Magnusson, K.E., Jaldén, J.: A batch algorithm using iterative application of the Viterbi algorithm to track cells and construct cell lineages. In: International Symposium on Biomedical Imaging (ISBI), pp. 382–385. IEEE (2012) (10.1109/ISBI.2012.6235564)
  22. Rapoport, D.H., Becker, T., Mamlouk, A.M., Schicktanz, S., Kruse, C.: A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters. PloS one 6(11), e27315 (2011) (10.1371/journal.pone.0027315) / PloS one by DH Rapoport (2011)
  23. Jüttner, A., Dezsö, B., Kovács, P.: Lemon: library for efficient modeling and optimization in networks. Technical report. Department of Operations Research, Eötvös Loránd University, Budapest
  24. Schiegg, M., Hanslovsky, P., Haubold, C., Koethe, U., Hufnagel, L., Hamprecht, F.A.: Graphical model for joint segmentation and tracking of multiple dividing cells (2014). doi: 10.1093/bioinformatics/btu764 (10.1093/bioinformatics/btu764) / Bioinformatics by Martin Schiegg (2014)
  25. Sommer, C., Straehle, C., Kothe, U., Hamprecht, F.A.: Ilastik: interactive learning and segmentation toolkit. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), pp. 230–233. IEEE (2011) (10.1109/ISBI.2011.5872394)
Dates
Type When
Created 8 years, 11 months ago (Sept. 15, 2016, 5:48 a.m.)
Deposited 2 months, 1 week ago (June 10, 2025, 3:01 p.m.)
Indexed 2 months, 1 week ago (June 11, 2025, 12:13 a.m.)
Issued 9 years, 7 months ago (Jan. 1, 2016)
Published 9 years, 7 months ago (Jan. 1, 2016)
Published Online 8 years, 11 months ago (Sept. 16, 2016)
Published Print 9 years, 7 months ago (Jan. 1, 2016)
Funders 0

None

@inbook{Haubold_2016, title={A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets}, ISBN={9783319464787}, ISSN={1611-3349}, url={http://dx.doi.org/10.1007/978-3-319-46478-7_35}, DOI={10.1007/978-3-319-46478-7_35}, booktitle={Computer Vision – ECCV 2016}, publisher={Springer International Publishing}, author={Haubold, Carsten and Aleš, Janez and Wolf, Steffen and Hamprecht, Fred A.}, year={2016}, pages={566–582} }