%I IEEE
%X In this paper, an automated algorithm to flatten lines from Atomic Force Microscopy (AFM) images is presented. Due to the mechanics of the AFM, there is a curvature distortion (bowing effect) present in the acquired images. At present, flattening such images requires human intervention to manually segment object data from the background, which is time consuming and highly inaccurate. The proposed method classifies the data into objects and background, and fits convex lines in an iterative fashion. Results on real images from DNA wrapped carbon nanotubes (DNA-CNTs) and synthetic experiments are presented, demonstrating the effectiveness of the proposed algorithm in increasing the resolution of the surface topography.
%T Automated line flattening of Atomic Force Microscopy images
%P 2968 -2971
%K AFM mechanics; DNA wrapped carbon nanotube; atomic force microscopy image; automated line flattening; curve fitting; object data segmentation; object detection; polynomial approximation; surface topography; atomic force microscopy; carbon nanotubes; curve fitting; image segmentation; object detection; polynomial approximation; surface topography;
%B IEEE International conference on image processing (ICIP 2008)
%R 10.1109/ICIP.2008.4712418
%D 2008
%A Sotirios A. Tsaftaris
%A Jana Zujovic
%A Aggelos K. Katsaggelos
%L eprints821