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Proceedings Paper

A new robust image feature point detector
Author(s): Yi Zhao
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Paper Abstract

A scale space-variant filter (SVF) is proposed on the basis of Harris arithmetic operators, which can smoothly isolate noise efficiently at the situation of keeping edge information of the image. Comparing SVF with Gaussian filter under step jump signal and initial image input, the result indicates that SVF is better than Gaussian filter. Using SVF to detect feature points of an image, the experiment shows that feature points detected from SVF output contain more edge information. Using 2D space limitations, Euclidian distance limitation and angle limitation, we can eliminate redundant feature points so that all the useful feature points are distributed in all regions of the image evenly. From the result of the examination for noise-contained image, we can draw the conclusions that the new robust feature point detector can get more accurate position of feature points and the distribution of the points is more rational than that of the points without those limitations.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142904 (14 February 2020); doi: 10.1117/12.2535793
Show Author Affiliations
Yi Zhao, HuBei Preschool Teacher College (China)


Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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