A note on the convergence of the mean shift

Pattern Recognition

Volumn 40, Issue 6, 2007, Pages 1756-1762

  Li, Xiangru ^a   Hu, Zhanyi ^a   Wu, Fuchao ^a  

^a INSTITUTE OF AUTOMATION   (China)

Author keywords

Computer vision; Convergence; Local structure; Mean shift

Indexed keywords

ALGORITHMS; COMPUTER VISION; ITERATIVE METHODS; PATTERN RECOGNITION;

ITERATIVE ALGORITHMS; MEAN SHIFT;

CONVERGENCE OF NUMERICAL METHODS;

EID: 33947136257     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2006.10.016     Document Type: Article

References (9)

1. Fukunaga K., and Hostetler L.D. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 21 (1975) 32-40
2. Cheng Y.Z. Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17 8 (1995) 790-799
3. Comaniciu D., and Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24 5 (2002) 603-619
4. Fashing M., and Tomasi C. Mean shift is a bound optimization. IEEE Trans. Pattern Anal. Mach. Intell. 27 3 (2005) 471-474
5. Singh M., Arora H., and Ahuja N. A robust probabilistic estimation framework for parametric image models. European Conference on Computer Vision vol. 1 (2004) 508-522
6. Comaniciu D., Ramesh V., and Meer P. Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25 5 (2003) 564-575
7. Comaniciu D., Ramesh V., and Meer P. Real-time tracking of non-rigid objects using mean shift. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) vol. 2 (2000) 142-149
8. Comaniciu D., Ramesh V., and Meer P. The variable bandwidth mean shift and data-driven scale selection. Proceedings of the IEEE International Conference on Computer Vision (ICCV) vol. 1 (2001) 438-445
9. Xue C.X. Real Function Theory and Functional Analysis (1993), High Education Press, Beijing, China (in Chinese)