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https://doi.org/10.1117/12.806...
Article . 2009 . Peer-reviewed
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Mark detection from scanned ballots

Authors: Elisa H. Barney Smith; George Nagy; Daniel P. Lopresti;

Mark detection from scanned ballots

Abstract

Analyzing paper-based election ballots requires finding all marks added to the base ballot. The position, size, shape, rotation and shade of these marks are not known a priori. Scanned ballot images have additional differences from the base ballot due to scanner noise. Different image processing techniques are evaluated to see under what conditions they are able to detect what sorts of marks. Basing mark detection on the difference of raw images was found to be much more sensitive to the mark darkness. Converting the raw images to foreground and background and then removing the form produced better results.

Country
United States
Related Organizations
Keywords

658, Electrical and Computer Engineering, BRC

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    popularity
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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Top 10%
Average