Paper Info
Title
Adaptive local scanning: A comprehensive and intelligent method for fast scanning of indiscrete objects
Abstract
A pixel-by-pixel scanning that is usually performed by a single point-like sensor or probe is being widely used in the applications such as scanning probe microscopy techniques. Typically their scanning time is several seconds to minutes long due to a raster scanning that needs to be conducted for capturing every single point on the surface of the sample area. To improve the scanning efficiency, recent research has been focused on investigating effective scanning patterns and methods. This work presents an adaptive local scanning method for efficiently sampling indiscrete objects like string-like one-piece connected objects under the microscopy. An initial scanning pattern is firstly investigated. Once the initial scanning reaches the object, an adaptive sinusoidal scanning method that can on-line adjust its scanning frequency and amplitude by predicting both the curvatures and the shape of the object is employed. The method also addresses scanning intersections and bifurcations associated with objects. Based on extensive implementation, it was validated that our method has high performance as it has high scanning efficiency and the scanned results match objects with high precision and high accuracy.
Year
DOI
Venue
2014
10.1109/MFI.2014.6997690
Multisensor Fusion and Information Integration for Intelligent Systems
Keywords
Field
DocType
image sampling,scanning probe microscopy,shape recognition,adaptive local scanning method,adaptive sinusoidal scanning method,indiscrete object sampling,indiscrete objects,initial scanning pattern,intelligent method,pixel-by-pixel scanning,point-like sensor,scanning bifurcations,scanning intersections,scanning probe microscopy techniques,string-like one-piece connected objects,adaptive method,local scanning,microscopy,scanning patterns,shape,bifurcation,spirals
Feature-oriented scanning,Computer vision,Computer science,Optics,Raster scan,Scanning probe microscopy,Sampling (statistics),Artificial intelligence,Microscopy
Conference
ISSN
Citations 
PageRank 
2153-0858
0
0.34
References 
Authors
4
2
Name
Order
Citations
PageRank
Mehdi Rahimi111.42
Yantao Shen27625.35