Accurate object detection with deformable shape models learnt from images

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2007, Pages

  Ferrari, Vittorio ^a   Jurie, Frederic ^a   Schmid, Cordelia ^a  

^a INRIA   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLUTTER (INFORMATION THEORY); HOUGH TRANSFORMS; MATHEMATICAL MODELS;

HOUGH-STYLE VOTING; OBJECT CLASS DETECTION; OBJECT DETECTORS; SHAPE MATCHERS;

OBJECT RECOGNITION;

EID: 34948828581     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.383043     Document Type: Conference Paper

References (20)

1. A. Berg, T. Berg and J. Malik, Shape Matching and Object Recognition using Low Distortion Correspondence, CVPR, 2005.
2. S. Belongie and J. Malik, Shape Matching and Object Recognition using Shape Contexts, PAMI, 2002.
3. H. Chui and A. Rangarajan, A new point matching algorithm for non-rigid registration, CVIU, 2003.
4. T. Cootes, An introduction to Active Shape Models, 2000.
5. D. Cremers, C. Schnorr, and J. Weickert, Diffusion-Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework, Workshop on Variational and Levelset Methods, 2001.
6. G. Elidan, G. Heitz, D. Koller, Learning Object Shape: From Drawings to Images, CVPR, 2006.
7. V. Ferrari, T. Tuytelaars, and L. Van Gool, Object Detection with Contour Segment Networks, ECCV, 2006.
8. V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, Groups of Adjacent Contour Segments for Object Detection, PAMI, 2007 (to appear).
9. P. Felzenswalb, Representation and Detection of Deformable Shapes, PAMI, 2004.
10. A. Hill and C. Taylor, A Method of Non-Rigid Correspondence for Automatic Landmark Identification, BMVC, 1996.
11. F. Jurie and C. Schmid Scale-invariant Shape Features for Recognition of Object Categories, CVPR, 2004.
12. P. Kumar, P. Torr, and A. Zisserman, OBJ CUT, CVPR, 2005.
13. B. Leibe and B. Schiele, Scale-Invariant Object Categorization using a Scale-Adaptive Mean-Shift Search, DAGM, 2004.
14. D. Martin, C. Fowlkes and J. Malik, Learning to detect natural image boundaries using local brightness, color, and texture cues, PAMI, 2004.
15. A. Opelt, A. Pinz, and A. Zisserman, A Boundary-Fragment-Model for Object Detection, ECCV, 2006.
16. J. Shotton, A. Blake, and R. Cipolla, Contour-Based Learning for Object Detection, ICCV, 2005.
17. J. Shotton, J. Winn, C. Rother, and A. Criminisi, TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-Class Object Recognition and Segmentation, ECCV, 2006.
18. S. Todorovic and N. Ahuja, Extracting Subimages of an Unknown Category from a Set of Images, CVPR, 2006.
19. A. Torralba, K. Murphy, and W. Freeman, Sharing Features: efficient boosting procedures for multiclass object detection, CVPR, 2004.
20. J. Winn and N. Jojic, LOCUS: Learning Object Classes with Unsupervised Segmentation, ICCV, 2005.