Minimalist AdaBoost for blemish identification in potatoes

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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Abstract

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: 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.
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 Sciences > Faculty of Science > Lincoln School of Computer Science
Depositing User: Michael Barnes
Date Deposited: 11 May 2012 11:15
Last Modified: 13 Mar 2013 09:08
URI: http://eprints.lincoln.ac.uk/id/eprint/5517

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