A New Multi-Scale Harris Interesting Point Detector

Guang Xue Wang; Yong Chun Liu; Huan He

doi:10.4028/www.scientific.net/AMM.602-605.1950

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605A New Multi-Scale Harris Interesting Point...

A New Multi-Scale Harris Interesting Point Detector

Abstract:

The interesting point is important features in images, which is frequently used for image registration, scene analysis, object tracking. Many algorithms for detecting the interesting points have been developed up to now. Among them, the Harris interesting point detector is proved most stable against rotation and noise, while it is also very sensitive to scale change. In order to solve this problem, a new multi-scale Harris interesting point detection algorithm is proposed in this paper. The algorithm consists of two main stages. In stage one, we build a multi-scale representation for Harris measure and find candidate interesting points at each scale level. In stage two, a novel measure is defined to measure the stability of each point. Based on the measure, the final interesting point is selected from candidates. The experiments on both optical and sar data sets have shown that, compared with stand Harris interesting point detector and some other multi-scale interesting point detectors, our algorithm is more robust.

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Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

1950-1955

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.1950

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Online since:

August 2014

Authors:

Guang Xue Wang*, Yong Chun Liu, Huan He

Keywords:

Harris, Interesting Point Detection, Multi-Scale, SIFT

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References

[1] H. P. Moravec. Towards Automatic Visual Obstacle Avoidance[C], Proceedings of 5th International Joint Conference on Artificial Intelligence, 1997. 584.

Google Scholar

[2] W. Forstner, E. Gulch. A Fast Operator for detection and precise location of distinct poits, corners, and centers of circular feature[C], Proceedings of the ISPRS Workshop on Fast Processing of Photogrammetric Data, 1981. 281-305.

Google Scholar

[3] C. Harris, M. Stephen. A combined corner and edge detector[C], Proc 4th Alvey Vision Conference, 1988. 147-151.

Google Scholar

[4] S.M. Smith, J.M. Brady. SUSAN-a new approach to low level image processing[J]. International Journal of computer Vision, 1997(23). 45-78.

Google Scholar

[5] L. Kitchen, A. Rosenfeld. Gray-Level corner detection[J]. Pattern Recognition 1982(1). 95-102.

DOI: 10.1016/0167-8655(82)90020-4

Google Scholar

[6] T. Lindeberg. Feature detection with automatic scale selection[J]. International Journal of computer Vision, 1998: 30 (2). 79-116.

Google Scholar

[7] K. Mikolajczyk, C. Schmid. Scale and affine invariant interest point detectors[J]. Image and Vision Computing, 2004(22). 977-1000.

Google Scholar

[8] D.G. Lowe. Distinctive image features from scale-invariant key points[J]. the International Journal of Computer Vision 2004. 60(2). 91–110.

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar