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Assessing Error Bound For Dominant Point Detection
Dilip K. Prasad
Pages - 326 - 333     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
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KEYWORDS
Dominant Point Detection, Nonparametric, Non-heuristic, Error bound, Comparison, Digitization
ABSTRACT
This paper compares three error bounds that can be used to make dominant point detection methods non-parametric. The three error bounds are based on the error in slope estimation due to digitization. However, each of the three methods takes a different approach for calculating the error bounds. This results into slightly different natures of the three methods and slightly different values. The impact of these error bounds is studied in the context of the non-parametric version of the widely used RDP method [1, 2] of dominant point detection. It is seen that the recently derived error bound (the third error bound in this paper), which depends on both the length and the slope of the line segment, provides the most balanced dominant point detection results for a variety of curves.
CITED BY (10)  
1 Prasad, D. K., Leung, M. K., Quek, C., & Brown, M. S. (2014). DEB: Definite error bounded tangent estimator for digital curves. Image Processing, IEEE Transactions on, 23(10), 4297-4310.
2 Hammoud, R., Sahin, C. S., Blasch, E. P., & Rhodes, B. J. (2014, June). Multi-Source Multi-Modal Activity Recognition in Aerial Video Surveillance. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2014 IEEE Conference on (pp. 237-244). IEEE.
3 Hammoud, R. I., Sahin, C. S., Blasch, E. P., Rhodes, B. J., & Wang, T. (2014). Automatic association of chats and video tracks for activity learning and recognition in aerial video surveillance. Sensors, 14(10), 19843-19860.
4 Abbasi, H., Olyaee, M., & Ghafari, H. R. (2013). Rectifying reverse polygonization of digital curves for dominant point detection. IJCSI International Journal of Computer Science Issues, 10.
5 Prasad, D. K. (2013). Geometric primitive feature extraction-concepts, algorithms, and applications. arXiv preprint arXiv:1305.3885.
6 Prasad, D. K. (2013). PRO: A novel approach to precision and reliability optimization based dominant point detection. Journal of Optimization, 2013.
7 Prasad, D. K., & Quek, C. (2013, December). Comparison of error bounds for non-parametric dominant point detection. In Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on (pp. 1-5). IEEE.
8 Prasad, D. K. (2013). Object detection in real images. arXiv preprint arXiv:1302.5189.
9 Prasad, D. K. (2012). Survey of the problem of object detection in real images. International Journal of Image Processing (IJIP), 6(6), 441.
10 Prasad, D. K., Quek, C., & Leung, M. K. (2012). Fast segmentation of sub-cellular organelles. International Journal of Image Processing (IJIP), 6(5), 317.
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Dr. Dilip K. Prasad
- Singapore
dilipprasad@gmail.com