Barnes, Michael and Cielniak, Grzegorz and Duckett, Tom (2010) Minimalist AdaBoost for blemish identification in potatoes. In: International Conference on Computer Vision and Graphics 2010, Sep 20-22 2010, PJIIT - Polish-Japanese Institute of Information Technology, Warsaw, Poland.
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We present a multi-class solution based on minimalist Ad-
aBoost for identifying blemishes present in visual images of potatoes.
Using training examples we use Real AdaBoost to rst reduce the fea-
ture set by selecting ve features for each class, then train binary clas-
siers for each class, classifying each testing example according to the
binary classier with the highest certainty. Against hand-drawn ground
truth data we achieve a pixel match of 83% accuracy in white potatoes
and 82% in red potatoes. For the task of identifying which blemishes
are present in each potato within typical industry dened criteria (10%
coverage) we achieve accuracy rates of 93% and 94%, respectively.
|Item Type:||Conference or Workshop Item (Paper)|
|Additional Information:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 6374 LNCS, Issue PART 1, 2010, Pages 209-216|
|Keywords:||Potatoes, Computer Vision, Machine Learning, bmjtype|
|Subjects:||G Mathematical and Computer Sciences > G760 Machine Learning|
G Mathematical and Computer Sciences > G740 Computer Vision
|Divisions:||College of Science > School of Computer Science|
|Deposited By:||Michael Barnes|
|Deposited On:||11 May 2012 11:15|
|Last Modified:||09 Jul 2013 15:06|
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Minimalist AdaBoost for blemish identification
in potatoes. (deposited 19 Jan 2011 14:01)
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