Stem
End and Calyx Detection
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Distinguishing stem end
and calyx from true defects is the main challenge
for automatic apple blemish detection systems.
This paper presents the results of using our novel
general object recognition algorithm called
Evolutionary COnstructed (ECO) features for
distinguishing bruises and blemishes from the stem
end and calyx of apples. Rather than relying on
human experts to build features sets to tune their
parameters, our method uses simulated evolution to
construct series of transforms that convert the
input signal of raw pixels of apple images into
high quality features. The use of this method
demonstrates the feasibility of using machine
vision technology with the off-the-shelf optical
and electronics components to detect true bruises
and blemishes on apples with higher than 94%
accuracy. Although we use apple in this paper as
an example to prove its feasibility, this method
could be easily adapted for other stone fruits.
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Collaborators: |
Dr. Dong Zhang, Sun Yat-sen University, Guangzhou,
China |
Graduate
Students:
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Kirt
Lillywhite and Beau Tippetts
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Publications:
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D.
Zhang, K.D. Lillywhite, D.J. Lee, and B.J.
Tippetts, “Automated Apple Stem End and
Calyx Detection using Evolution-COnstructed
Features, “ Journal of Food Engineering,
vol. 119/3, p. 411-418, December 2013.
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image to view.)
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