PRISM: PRincipled implicit shape model

British Machine Vision Conference, BMVC 2009 - Proceedings

Volumn , Issue , 2009, Pages

  Lehmann, Alain ^a   Leibe, Bastian ^b   Van Gool, Luc ^a  

^a ETH ZURICH   (Switzerland)

^b RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

HOUGH TRANSFORMS; OBJECT RECOGNITION;

GAUSSIAN MIXTURE MODEL; IMPLICIT SHAPE MODELS; KERNEL DENSITY ESTIMATORS; NEAREST NEIGHBOUR; OBJECT DETECTION; PROBABILISTIC MODELING; RECOGNITION TIME; VISUAL VOCABULARIES;

COMPUTER VISION;

EID: 84898904816     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5244/C.23.64     Document Type: Conference Paper

References (28)

1. D.H. Ballard. Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition, 13(2):111-122, 1981.
2. H. Bay, A. Ess, T. Tuytelaars, and L. van Gool. Surf: Speeded up robust features. Computer Vision and Image Understanding, 110(3):346-359, 2008.
3. M. Á. Carreira Perpiñán. Mode-finding for mixtures of gaussian distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1318-1323, 2000.
4. O. Chum and A. Zisserman. An exemplar model for learning object classes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007.
5. N. Cornelis and L. Van Gool. Fast scale invariant feature detection and matching on programmable graphics hardware. In Computer Vision and Pattern Recognition (CVPR) Workshop, June 2008.
6. N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005.
7. P. Felzenszwalb, D. McAllester, and D. Ramanan. A discriminatively trained, multiscale, de-formable part model. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008.
8. V. Ferrari, F Jurie, and C. Schmid. Accurate object detection with deformable shape models learnt from images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007.
9. M. Fritz, B. Leibe, B. Caputo, and B. Schiele. Integrating representative and discriminant models for object category detection. In Proceedings of the IEEE International Conference on Computer Vision, 2005.
10. J. Gall and V. Lempitsky. Class-specific hough forests for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009.
11. J. Kittler, M. Hatef, R. Duin, and J. Matas. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):226-239, 1998.
12. C. H. Lampert, M. B. Blaschko, and T. Hofmann. Beyond sliding windows: Object localization by efficient subwindow search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008.
13. A. Lehmann, B. Leibe, and L. van Gool. Feature-centric efficient subwindow search. In Proceedings of the IEEE International Conference on Computer Vision, 2009.
14. B. Leibe, A. Leonardis, and B. Schiele. Robust object detection by interleaving categorization and segmentation. International Journal of Computer Vision, 77(1-3):259-289, 2008.
15. B. Leibe and B. Schiele. Scale-invariant object categorization using a scale-adaptive mean-shift search. In Proceedings of the DAGM Symposium, 2004.
16. B. Leibe, E. Seemann, and B. Schiele. Pedestrian detection in crowded scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2005.
17. J. Liebelt, C. Schmid, and K. Schertler. Viewpoint-independent object class detection using 3D feature maps. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008.
18. S. Maji and J. Malik. Object detection using a max-margin hough transform. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2009.
19. S. Maji, A. C. Berg, and J. Malik. Classification using intersection kernel support vector machines is efficient. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008.
20. K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. International Journal of Computer Vision, 60(1):63-86, 2004.
21. K. Mikolajczyk and C. Schmid. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10):1615-1630, 2005.
22. B. Ommer and J. M. Buhmann. Learning the compositional nature of visual objects. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2007.
23. A. Opelt, A. Pinz, and A. Zisserman. A boundary-fragment-model for object detection. In Proceedings of the European Conference on Computer Vision, 2006.
24. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2008.
25. H. Schneiderman and T. Kanade. Object detection using the statistics of parts. International Journal of Computer Vision, 56(3):151-177, 2004.
26. E. B. Sudderth, A. Torralba, W. T. Freeman, and A. S. Willsky. Learning hierarchical models of scenes, objects, and parts. In Proceedings of the IEEE International Conference on Computer Vision, 2005.
27. P. A. Viola and M. J. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154, 2004.
28. C. K. I. Williams and M. Allan. On a connection between object localization with a generative template of features and pose-space prediction methods. Technical Report 0719, University of Edinburgh, 2006.