Linear time maximally stable extremal regions

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

Volumn 5303 LNCS, Issue PART 2, 2008, Pages 183-196

  Nistér, David ^a   Stewénius, Henrik ^b  

^a MICROSOFT   (United States)

^b GOOGLE   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DATA STRUCTURES; PIXELS;

COMPUTATIONAL ORDERS; CONNECTED COMPONENT; CONNECTED COMPONENT ALGORITHM; FPGA IMPLEMENTATIONS; MAXIMALLY STABLE EXTREMAL REGIONS; QUASI-LINEAR TIME; STANDARD ALGORITHMS; UNION-FIND DATA STRUCTURES;

TREES (MATHEMATICS);

EID: 56749169028     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88688-4_14     Document Type: Conference Paper

References (24)

1. Tuytelaars, T., Gool, L.V.: Matching widely separated views based on affine invariant regions. International Journal of Computer Vision 59(1), 61-85 (2004)
2. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151-172 (2000)
3. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, pp. 384-393 (2002)
4. Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60(1), 63-86 (2004)
5. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. International Journal of Computer Vision 65(1-2), 43-72 (2005)
6. Kadir, T., Zisserman, A., Brady, M.: An affine invariant salient region detector. In: European Conference on Computer Vision, pp. 404-416 (2004)
7. Lowe, D.: Distinctive image features from scale invariant keypoints. International Journal of Computer Vision 60(2), 91-110 (2004)
8. Lindeberg, T.: Feature detection with automatic scale selection. International Journal of Computer Vision 30(2), 77-116 (1998)
9. Triggs, B.: Detecting keypoints with stable position, orientation and scale under illumination changes. In: European Conference on Computer Vision, vol. 4, pp. 100-113 (2004)
10. Rothganger, F., Lazebnik, S., Schmid, C., Ponce, J.: 3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints. International Journal of Computer Vision 66(3), 231-259 (2006)
11. Donoser, M., Bischof, H.: Efficient maximally stable extremal region (mser) tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 553-560 (2006)
12. Brown, M., Lowe, D.: Invariant features from interest point groups. In: British Machine Vision Conference, pp. 656-665 (2002)
13. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147-151 (1988)
14. Sivic, J., Zisserman, A.: Video google: A text retrieval approach to object matching in videos. In: International Conference on Computer Vision, vol. 2, pp. 1470-1477 (2003)
15. Nistér, D., Stewénius, H.: Scalable recognition with a vocabulary tree. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 2161-2168 (2006)
16. Obdrzalek, S., Matas, J.: Object recognition using local affine frames on distinguished regions. In: British Machine Vision Conference, vol. 1, pp. 113-122 (2002)
17. Forssén, P.E.: Maximally stable colour regions for recognition and matching. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)
18. Donoser, M., Bischof, H.: 3d segmentation by maximally stable volumes (msvs). In: ICPR 2006: Proceedings of the 18th International Conference on Pattern Recognition, pp. 63-66 (2006)
19. Kristensen, F., MacLean, W.: Fpga real-time extraction of maximally-stable extremal regions. In: IEEE International Symposium on Circuits and Systems (2007)
20. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 583-598 (1991)
21. Roerdink, J., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41, 187-228 (2000)
22. Couprie, M., Najman, L., Bertrand, G.: Quasi-linear algorithms for the topological watershed. Journal of Mathematical Imaging and Vision 22(2), 231-249 (2005)
23. Tarjan, R.: Data Structures and Network Algorithms. SIAM, Philadelphia (1983)
24. Gabow, H., Tarjan, R.: A linear-time algorithm for a special case of disjoint set union. Journal of Computer and System Sciences 30(2), 209-220 (1985)