Optimizing Feature Calculation in Adaptive Machine Vision Systems

A classifier’s accuracy substantially depends on the features that are utilized to characterize an input sample. The selection of a representative and—ideally—small set of features that yields high discriminative power is an important step in setting up a classification system. The features are a set of functions that transform the raw input data (an image in the case of machine vision systems) into a vector of real numbers. This transformation may be a quite complex algorithm, with lots of parameters to tune and consequently with much room for optimization. In order to efficiently use this additional room for optimizing the features, we propose an integrated optimization step that adapts the feature parameters in such a way that the separation of the classes in feature space is improved, thus reducing the number of misclassifications. Furthermore, these optimization techniques may be used to “shape” the decision boundary in such a way that it can be easily modeled by a classifier. After covering the relevant elements of the theory behind this automatic feature optimization process, we will demonstrate and assess the performance on two typical machine vision applications. The first one is a quality control task, where different types of defects need to be distinguished, and the second example is a texture classification problem as it appears in image segmentation tasks. We will show how the optimization process can be successfully applied in morphological and textural features that both offer a number of parameters to tune and select.