DocumentCode
250035
Title
Automated registration of low and high resolution atomic force microscopy images using scale invariant features
Author
Yun-feng Wang ; Kilpatrick, Jason I. ; Jarvis, Suzanne P. ; Boland, Frank ; Kokaram, Anil ; Corrigan, David
Author_Institution
Dept. of Electron. & Electr. Eng., Trinity Coll. Dublin, Dublin, Ireland
fYear
2014
fDate
27-30 Oct. 2014
Firstpage
5866
Lastpage
5870
Abstract
This paper introduces a method for registering scans acquired by Atomic Force Microscopy (AFM). Due to compromises between scan size, resolution, and scan rate, high resolution data is only attainable in a very limited field of view. The proposed method uses a sparse set of feature matches between the low and high resolution AFM scans and maps them onto a common coordinate system. This can provide a wider field of view of the sample and give context to the regions where high resolution AFM data has been obtained. The algorithm employs a robust approach overcoming complications due to temporal sample changes and sample drift of the AFM system which becomes significant at higher-resolutions. To our knowledge, this is the first approach for automatic high resolution AFM image registration. Experimental results show the correctness and robustness of our approach and shows that the estimated transforms can be used to deduce plausible measures of sample drift.
Keywords
atomic force microscopy; image matching; image registration; image resolution; coordinate system; high resolution AFM data; high resolution atomic force microscopy image automated registration; low resolution atomic force microscopy image automated registration; scale invariant features; Atomic force microscopy; Force; Image resolution; Lipidomics; Robustness; Transforms; Atomic Force Microscopy; Feature matching; Image registration; RANSAC;
fLanguage
English
Publisher
ieee
Conference_Titel
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location
Paris
Type
conf
DOI
10.1109/ICIP.2014.7026185
Filename
7026185
Link To Document