A new robust image feature point detector

Yi Zhao

doi:10.1117/12.2535793

14 February 2020 A new robust image feature point detector

Yi Zhao

Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 1142904 (2020) https://doi.org/10.1117/12.2535793
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Yi Zhao "A new robust image feature point detector", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 1142904 (14 February 2020); https://doi.org/10.1117/12.2535793

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available