We present results of applying a feature extraction process to images of coatings of
molecularly imprinted polymers (MIPs) coatings on glass substrates for defect detec-
tion. Geometric features such as MIP side lengths, aspect ratio, internal angles, edge
regularity, and edge strength are obtained by using Hough transforms, and Canny
edge detection. A Self Organizing Map (SOM) is used for classification of texture of
MIP surfaces. The SOM is trained on a data set comprised of images of manufactured
MIPs. The raw images are first processed using Hough transforms and Canny edge
detection to extract just the MIP-coated portion of the surface, allowing for surface
area estimation and reduction of training set size. The training data set is comprised
of 20-dimensional feature vectors, each of which is calculated from a single section of a
gray scale image of a MIP. Haralick textures are among the quantifiers used as feature
vector components. The training data is then processed using principal component
analysis to reduce the number of dimensions of the data set. After training, the SOM
is capable of classifying texture, including defects
