K-smallest spanning tree segmentations

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8142 LNCS, Issue , 2013, Pages 375-384

  Straehle, Christoph ^a   Peter, Sven ^a   Köthe, Ullrich ^a   Hamprecht, Fred A ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ENERGY FUNCTIONS; MINIMUM SPANNING TREES; ORDERS OF MAGNITUDE; REAL-WORLD IMAGE; SPANNING TREE; UNCERTAINTY INDICATOR;

ALGORITHMS; PATTERN RECOGNITION; TREES (MATHEMATICS);

IMAGE SEGMENTATION;

EID: 84886394122     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40602-7_40     Document Type: Conference Paper

References (20)

1. Batra, D., Yadollahpour, P., Guzman-Rivera, A., Shakhnarovich, G.: Diverse M-best solutions in markov random fields. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 1-16. Springer, Heidelberg (2012)
2. Blake, A., Kohli, P., Rother, C.: Markov random fields for vision and image processing. MIT Press (2011)
3. Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary and region segmentation of objects in ND images. In: ICCV (2001)
4. Brendel, W., Todorovic, S.: Segmentation as maximumweight independent set. In: NIPS, vol. 4 (2010)
5. Briggman, K.L., Denk, W., et al.: Towards neural circuit reconstruction with volume electron microscopy techniques. Current Opinion in Neurobiology (2006)
6. Couprie, C., Grady, L., Najman, L., Talbot, H.: Power watershed: A unifying graph-based optimization framework. IEEE PAMI (2010)
7. Cousty, J., Bertrand, G., Najman, L., Couprie, M.: Watershed cuts: Minimum spanning forests and the drop of water principle. IEEE PAMI, 1362-1374 (2009)
8. Falcão, A.X., Stolfi, J., Lotufo, R.A.: The image foresting transform: Theory, algorithms, and applications. IEEE PAMI 26 (2004)
9. Fromer, M., Globerson, A.: An LP view of the M-best MAP problem. NIPS (2009)
10. Gabow, H.: Two algorithms for generating weighted spanning trees in order. SIAM Journal on Computing (1977)
11. Grady, L.: Random walks for image segmentation. IEEE PAMI 28 (2006)
12. Meyer, F.: Minimum spanning forests for morphological segmentation. In: Mathematical Morphology and its Applications to Image Processing, pp. 77-84. Springer (1994)
13. Meyer, F., Beucher, S.: Morphological segmentation. Journal of Visual Communication and Image Representation 1(1), 21-46 (1990)
14. Nilsson, D.: An efficient algorithm for finding the M most probable configurations in probabilistic expert systems. Statistics and Computing 8(2), 159-173 (1998)
15. Rollon, N.E., Dechter, R.: Inference schemes for M-best solutions for soft CSPs. In: Proceedings of Workshop on Preferences and Soft Constraints, vol. 2 (2011)
16. Seroussi, B., Golmard, J.: An algorithm directly finding the K most probable configurations in bayesian networks. International Journal of Approximate Reasoning 11(3), 205-233 (1994)
17. Straehle, C.N., Köthe, U., Knott, G., Hamprecht, F.A.: Carving: Scalable interactive segmentation of neural volume electron microscopy images. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 653-660. Springer, Heidelberg (2011)
18. Tzeng, W.J., Wu, F.: Spanning trees on hypercubic lattices and nonorientable surfaces. Applied Mathematics Letters 13 (2000)
19. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE PAMI (1991)
20. Yanover, C., Weiss, Y.: Finding the AI most probable configurations using loopy belief propagation. In: NIPS (2004)