Abstract
An algorithm for automatically extracting feature points is developed after the area of feature points in 2-dimensional (2D) image being located by probability theory, correlated methods and criterion for abnormity. Feature points in 2D image can be extracted only by calculating standard deviation of gray within sampled pixels area in our approach statically. While extracting feature points, the limitation to confirm threshold by tentative method according to some a priori information on processing image can be avoided. It is proved that the proposed algorithm is valid and reliable by extracting feature points on actual natural images with abundant and weak texture, including multi-object with complex background, respectively. It can meet the demand of extracting feature points of 2D image automatically in machine vision system.
References
S. T. Barnard, W. B. Thompson, Disparity analysis of images, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2(1980)4, 333–340.
C. Schmid, R. Mohr, Local grayvalue invariants for image retrieval, IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(1997)15, 530–535.
Guan Yepeng, Tong Linsu, A fast algorithm for extracting feature point in 2-dimensional image, Journal of Image and Graphics, 7A(2002)12, 1296–1301.
Ostu Jun, A threshold selection method from gray-scale histograms, IEEE Trans. on System Man Cybernetics, SMC-9(1988)1, 62–66.
N. Papamarakors, B. Gatos, A new approach for multilevel threshold selection, CVGIP: Graphic Model and Image Processing, 56(1994)5, 357–370.
M. C. De Andrade, B. C. Vemuri, An interactive algorithm for image denoising and segmentation, In: 2001 Proceedings of XIV Brazilian Symposium on Computer Graphics and Image Processing, Florianopolis, Brazil, 2001, 274–281.
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Guan, Y., Gu, W., Ye, X. et al. Statistical probability based algorithm for extracting feature points in 2-dimensional image. J. of Electron.(China) 21, 170–176 (2004). https://doi.org/10.1007/BF02687834
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DOI: https://doi.org/10.1007/BF02687834