Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Comprehensive Colour Image Normalization
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
A similarity-based leaf image retrieval scheme: Joining shape and venation features
Computer Vision and Image Understanding
Venation pattern analysis of leaf images
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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The measure of leaf damage is a basic tool in plant epidemiology research. Measuring the area of a great number of leaves is subjective and time consuming. We investigate the use of machine learning approaches for the objective segmentation and quantification of leaf area damaged by mites in avocado leaves. After extraction of the leaf veins, pixels are labeled with a look-up table generated using a Support Vector Machine with a polynomial kernel of degree 3, on the chrominance components of YCrCb color space. Spatial information is included in the segmentation process by rating the degree of membership to a certain class and the homogeneity of the classified region. Results are presented on real images with different degrees of damage.