Multi-scale regions from edge fragments: A graph theory approach

VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications

Volumn 1, Issue , 2014, Pages 165-172

  Kazmi, Wajahat ^a   Andersen, Hans Jørgen ^a  

^a AALBORG UNIVERSITY   (Denmark)

Author keywords

Affine invariant regions; Edge fragments; Interest points; Shape detector

Indexed keywords

ALGORITHMS; BINARY TREES; FACE RECOGNITION; GRAPH THEORY;

AFFINE INVARIANT; EDGE FRAGMENTS; ELLIPSE FITTING; GRAPH CONNECTIVITY; IMAGE COMPRESSION ALGORITHMS; INTEREST POINTS; LINEAR INTERPOLATION; SALIENT REGIONS;

IMAGE RETRIEVAL;

EID: 84906907713     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Arandjelovi, O. (2012). Object Matching Using Boundary Descriptors. In Procedings of the British Machine Vision Conference 2012, pages 85.1-85.11. British Machine Vision Association.
2. Avrithis, Y. and Rapantzikos, K. (2011). The medial feature detector: Stable regions from image boundaries. Computer Vision (ICCV), 2011 IEEE International Conference on, 1(c):1724-1731.
3. Bellavia, F., Tegolo, D., and Valenti, C. (2011). Improving Harris corner selection strategy. Computer Vision, IET, 5(2):87-96.
4. Belongie, S., Malik, J., and Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(4):509-522.
5. Distasi, R. and Nappi, M. (1997). Image compression by B-tree triangular coding. IEEE Transactions on Communications, 45(9):1095-1100.
6. Ferrari, V., Fevrier, L., Jurie, F., and Schmid, C. (2008). Groups of adjacent contour segments for object detection. IEEE transactions on pattern analysis and machine intelligence, 30(1):36-51.
7. Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In Proceedings of the 4th Alvey Vision Conference, pages 147-152.
8. Kadir, T. (2002). Scale, saliency and scene description. PhD thesis, University of Oxford.
9. Kadir, T., Zisserman, A., and Brady, M. (2004). An affine invariant salient region detector. In Computer Vision-ECCV 2004, pages 228-241. Springer.
10. Kim, J. and Grauman, K. (2011). Boundary preserving dense local regions. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on 1553-1560. Ieee.
11. Lazebnik, S., Schmid, C., and Ponce, J. (2003). A sparse texture representation using affine-invariant regions. In Computer Vision and Pattern Recognition (CVPR), pages 319-324.
12. Lindeberg, T. (1994). Scale-space theory: A basic tool for analyzing structures at different scales. Journal of applied statistics, 21(1-2):225-270.
13. Lowe, D. G. (2004). Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision, 60(2):91-110.
14. Matas, J., Chum, O., Urban, M., and Pajdla, T. (2002). Robust wide-baseline stereo from maximally stable extremal regions. In British Machine Vision Conference, Volume 22, pages 384-393, Cardiff, Wales, UK. Elsevier.
15. Mikolajczyk, K. and Schmid, C. (2004). Scale and affine invariant interest point detectors. International journal of computer vision, 60(1):63-86.
16. Mikolajczyk, K. and Schmid, C. (2005). A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615-1630.
17. Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., and Gool, L. V. (2005). A Comparison of Affine Region Detectors. International Journal of Computer Vision, 65(1-2):43-72.
18. Nguyen, G. P. and Andersen, H. Jr. (2012). An Alternative to Scale-space Representation for Extracting Local Features in Image Recognition. In International Conference on Computer Vision Theory and Applications, Volume 1, pages 341-345, Rome, Italy. SciTePress.
19. Shotton, J., Blake, A., and Cipolla, R. (2008a). Efficiently Combining Contour and Texture Cues for Object Recognition. In Procedings of the British Machine Vision Conference 2008, pages 7.1-7.10. British Machine Vision Association.
20. Shotton, J., Blake, A., and Cipolla, R. (2008b). Multiscale categorical object recognition using contour fragments. IEEE transactions on pattern analysis and machine intelligence, 30(7):1270-81.
21. Tarjan, R. (1971). Depth-first search and linear graph algorithms. In Annual Symposium on Switching and Automata Theory, pages 114-121.
22. Tuytelaars, T. and Van Gool, L. (2004). Matching widely separated views based on affine invariant regions. International journal of computer vision, 59(1):61-85.
23. Varytimidis, C., Rapantzikos, K., and Avrithis, Y. (2012). WαSH: weighted α-shapes for local feature detection. In European Conference on Computer Vision (ECCV 2012).
24. Vedaldi, A. and Fulkerson, B. (2008). VLFeat: An Open and Portable Library of Computer Vision Algorithms. In ver (0.9.16), page http://www.vlfeat.org/.