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.
|Item Type:||Conference or Workshop Item (Paper)|
This is the latest version of this item.
|Divisions:||College of Science > School of Computer Science|
|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.|
|Date Deposited:||11 May 2012 11:15|
Available Versions of this Item
Minimalist AdaBoost for blemish identification
in potatoes. (deposited 19 Jan 2011 14:01)
- Minimalist AdaBoost for blemish identification in potatoes. (deposited 11 May 2012 11:15) [Currently Displayed]
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