Beilstein J. Nanotechnol.2018,9, 975–985, doi:10.3762/bjnano.9.91
automatically segmented with accurate boundary detection. The extracted foreground features were taken as exclusion masks. In the second step, data points in the background were fitted as polynomial curves/surfaces, which were then subtracted from raw images to get the flattened images. Moreover, sliding-window
-based polynomial fitting was proposed to process images with complex background trends. The working principle of the two-step image flattening scheme were presented, followed by the investigation of the influence of a sliding-window size and polynomial fitting direction on the flattened images
. Additionally, the role of image flattening on the morphological characterization and segmentation of AFM images were verified with the proposed method.
Keywords: atomic force microscopy; contour expansion; image flattening; polynomial fitting; slidingwindow; Introduction
Since its invention, the atomic
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Figure 1:
Schematic diagram of an AFM system and mechanism behind the distortion and artifacts present in AFM...