D - Clutter: Building object model library from unsupervised segmentation of cluttered scenes

2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Volumn , Issue , 2009, Pages 2783-2789

  Rohith, M V ^a   Somanath, Gowri ^a   Metaxas, Dmitris ^b   Kambhamettu, Chandra ^a  

^a UNIVERSITY OF DELAWARE   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

3D MODELING; COMPUTER VISION; IMAGE SEGMENTATION; RADAR CLUTTER; TEXTURES; THREE DIMENSIONAL COMPUTER GRAPHICS;

AUTONOMOUS SYSTEMS; CLUTTERED SCENES; MULTIPLE OBJECTS; NON-HOMOGENEOUS; OBJECT BOUNDARIES; RANDOM CONFIGURATIONS; UNSUPERVISED SEGMENTATION; VISUAL VOCABULARIES;

STEREO IMAGE PROCESSING;

EID: 70450181375     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2009.5206579     Document Type: Conference Paper

References (25)

1. B. C. Russell, A. Efros, J. Sivic, W. T. Freeman, and A. Zisserman, Using multiple segmentations to discover objects and their extent in image collections, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) 2006.
2. J. Sivic, B. Russell, A. A. Efros, A. Zisserman, W. T. Freeman, Discovering Objects and their Location in Images International Conference on Computer Vision (ICCV), Beijing, China, Oct. 2005.
3. R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from google's image search. International Conference on Computer Vision (ICCV), Oct 2005.
4. J. Winn and N. Jojic. Locus: Learning object classes with unsupervised segmentation International Conference on Computer Vision (ICCV), 2005.
5. A. Stein, T. Stepleton, and M. Hebert, Towards Unsupervised Whole-Object Segmentation: Combining Automated Matting with Boundary Detection, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2008.
6. M. Bravo and H. Farid. Object segmentation by topdown processes. Visual Cognition, Volume 10(4), pages 471-491, 2003.
7. S. Obdrzálek and J. Matas. Sub-linear indexing for large scale object recognition. Proceedings of the British Machine Vision Conference, Volume 1, pages 1-10, 2005.
8. Noah Snavely, Steven M. Seitz and Richard Szeliski, Modeling the World from Internet Photo Collections Int. J. Computer Vision, 80(2), pages 189-210, 2008.
9. R. r. Jana Kostkov. Stratified dense matching for stereopsis in complex scenes. British Machine Vision Conference, pages 339- 348, 2003.
10. J.-H. Ahn, K. Kim, and H. Byun. Robust object segmentation using graph cut with object and background seed estimation. Intl Conference on Pattern Recognition (ICPR), pages 361-364, 2006.
11. K. Y. Lee S H. Object segmentation in stereo images using cooperative stochastic diffusion. Proceedings of the IEICE General Conference, page 239, 2001.
12. A. Kushal and J. Ponce. Modeling 3d objects from stereo views and recognizing them in photographs, European Conference on Computer Vision (ECCV), pages 563-574, 2006.
13. M. P. Kumar, P. H. S. Torr, and A. Zisserman. Obj cut. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005.
14. H. Arora, N. Loeff, D. A. Forsyth, and N. Ahuja. Unsupervised segmentation of objects using efficient learning, IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2007.
15. K. N. Anastasios Doulamis, Nikolaos Doulamis and S. Kollias. Unsupervised semantic object segmentation of stereoscopic video sequences. ICIIS '99: Proceedings of the 1999 International Conference on Information Intelligence and Systems, page 527, 1999.
16. A. Fusiello and L. Irsara. Quasi-euclidean Uncalibrated Epipolar Rectification. International Conference on Pattern Recognition (ICPR), 2008.
17. P. M. D. Comaniciu. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Analysis Machine Intelligence, pages 603-619, 2002.
18. D. G. Lowe. Distinctive image features from scale invariant keypoints. International Journal of Computer Vision, volume 60, pages 91-110, 2004.
19. B. K. P. Horn, Closed-form solution of absolute orientation using unit quaternions, J. Opt. Soc. Am. A 4 pages 629-642, 1987.
20. J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 731-743, 1997.
21. http://www.semantic-robot-vision-challenge.org/.
22. Hartley, Rand, Zisserman, A, Multiple View Geometry in Computer Vision, Cambridge University Press, 2004.
23. Vittorio Ferrari, Tinne Tuytelaars, Luc VanGool, Integrating Multiple Model Views for Object Recognition, IEEE Computer Vision and Pattern Recognition Washington, USA, June 2004.
24. A. Johnson and M. Hebert, Using spin images for efficient object recognition in cluttered 3D scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 21, No. 5, pages 433 -449.1999.
25. Palmer, Stephen E., Vision Science: Photons to Phenomenology. Cambridge: The MIT Press 1999.