Object Detection and Segmentation Using Adaptive MeanShift Blob Tracking Algorithm and Graph Cuts Theory-Reference-Cited by-同舟云学术

Object Detection and Segmentation Using Adaptive MeanShift Blob Tracking Algorithm and Graph Cuts Theory

Published:2014 Issue: Volume: Page:143-151
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Mohcine Boudhane,Benayad Nsiri

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-01622-1_17

Reference10 articles.

1. Comaniciu, D., Ramesh, V., Meer, P.: Real Time Tracking of Non-Rigid Objects using Mean Shift. In: IEEE Computer Vision and Pattern Recognition II, pp. 142–149 (2000)

2. Collins, R.T.: Mean-shift Blob Tracking through Scale Space. In: IEEE, pp. 234–240 (2003)

3. Liang, D., Huang, Q., Jiang, S., Yao, H., Gao, H.: Mean shift blob tracking with kernel histogram filtering and hypothesis testing, pp. 605–614 (2005)

4. Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. on Information Theory 21(1), 32–40 (1975)

5. Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. II, pp. 142–149 (2000)

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