Real-time learning and detection of 3D texture-less objects: A scalable approach

BMVC 2012 - Electronic Proceedings of the British Machine Vision Conference 2012

Volumn , Issue , 2012, Pages

  Damen, Dima ^a   Bunnun, Pished ^b   Calway, Andrew ^a   Mayol Cuevas, Walterio ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b NATIONAL ELECTRONICS AND COMPUTER TECHNOLOGY CENTER   (Thailand)

Author keywords

[No Author keywords available]

Indexed keywords

STATISTICAL TESTS; TEXTURES;

3D OBJECT; DESCRIPTORS; FRAME RATE; MULTIOBJECT; RANDOM SAMPLE; REAL-TIME LEARNING; SCALABLE APPROACH; SCALE-INVARIANT;

COMPUTER VISION;

EID: 84898453638     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.26.23     Document Type: Conference Paper

References (28)

1. J Beis and D Lowe. Indexing without invariants in 3D object recognition. IEEE Transactions on Pattern And Machine Intelligence (PAMI), 21(10), 1999.
2. O Carmichael and M Hebert. Object recognition by a cascade of edge probes. In British Machine Vision Conference (BMVC), 2002.
3. A Chia, S Rahardja, D Rajan, and M Leung. Object recognition by discriminative combinations of line segments and ellipses. In Computer Vision and Pattern Recognition (CVPR), 2010.
4. D Damen, A Gee, A Calway, and W Mayol-Cuevas. Detecting and localising multiple 3D objects: A fast and scalable approach. In IROS Workshop on Active Semantic Perception and Object Search in the Real World (ASP-AVS-11), 2011.
5. O Danielsson, S Carlsson, and J Sullivan. Automatic learning and extraction of multilocal features. In International Conference on Computer Vision (ICCV), 2009.
6. M Everingham and J Winn. The PASCAL visual object classes challenge (VOC2007) development kit. Technical report, 2007.
7. R Fergus, P Perona, and A Zisserman. Object class recognition by unsupervised scaleinvariant learning. In Computer Vision and Pattern Recognition (CVPR), 2003.
8. V Ferrari, T Tuytelaars, and L Van Gool. Object detection by contour segment networks. In European Conference on Computer Vision (ECCV), 2006.
9. V Ferrari, F Jurie, and C Schmid. From images to shape models for object detection. International Journal of Computer Vision (IJCV), 87(3), 2009.
10. S Fidler, M Boben, and A Leonardis. A coarse-to-fine taxonomy of constellations for fast multi-class object detection. In European Conference on Computer Vision (ECCV), 2010.
11. J Gall and V Lempitsky. Class-specific Hough forests for object detection. In Computer Vision and Pattern Recognition (CVPR), 2010.
12. W Grimson and D Huttenlocher. On the sensitivity of geometric hashing. In International Conference on Computer Vision (ICCV), 1990.
13. S Hinterstoisser, V Lepetit, S Ilic, P Fua, and N Navab. Dominant orientation templates for real-time detection of texture-less objects. In Computer Vision and Pattern Recognition (CVPR), 2010.
14. Y Lamdan and H Wolfson. Geometric hashing: A general and efficient model-based recognition scheme. In International Conference on Computer Vision (ICCV), 1988.
15. M Leordeanu, M Hebert, and R Sukthankar. Beyond local appearance: Category recognition from pairwise interactions of simple features. In Computer Vision and Pattern Recognition (CVPR), 2007.
16. M Liu, O Tuzel, A Veeraraghavan, and R Chellappa. Fast directional chamfermatching. In Computer Vision and Pattern Recognition (CVPR), 2010.
17. S Maji and J Malik. Object detection using a max-margin Hough transform. In Computer Vision and Pattern Recognition (CVPR), 2009.
18. D Martin, C Fowlkes, and J Malik. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. on Pattern and Machine Intelligence (PAMI), 26(5), 2004.
19. A Opelt, A Pinz, and A Zisserman. A boundary-fragment-model for object detection. In European Confernece on Computer Vision (ECCV), 2006.
20. M Ozuysal, M Calonder, V Lepetit, and P Fua. Fast keypoint recognition using random forests. IEEE Trans. on Pattern and Machine Intelligence (PAMI), 32(3), 2010.
21. S Procter and J Illingworth. Foresight: fast object recognition using geometric hashing with edge-triple features. In International Conference on Image Processing (ICIP), 1997.
22. C Rothwell, A Zisserman, D Forsyth, and J Mundy. Canonical frames for planar object recognition. In European Conference on Computer Vision (ECCV), 1992.
23. S Savarese and L Fei-Fei. 3D generic object categorization, localization and pose estimation. In International Conference on Computer Vision (ICCV), 2007.
24. J Shotton, A Blake, and R Cipolla. Contour-based learning for object detection. In International Conference on Computer Vision (ICCV), 2005.
25. R Grompone von Gioi, J Jakubowicz, J Morel, and G Randall. LSD: A line segement detector. IEEE Trans. on Pattern and Machine Intelligence (PAMI), 32(4), 2010.
26. C Wiedemann, M Ulrich, and C Steger. Recognition and tracking of 3D objects. In DAGM Symposium on Pattern Recognition, 2008.
27. P Yarlagadda, A Monroy, and B Ommer. Voting by grouping dependent parts. In European Conference on Computer Vision (ECCV), 2010.
28. Z Zhang. Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision (IJCV), 13(2), 1994.