Spectrally Enhanced Image Processing and Nonlinear Alternating Decision Trees

Visual inspection is a sub-class of non-destructive testing extensively used in high volume manufacturing. Existing inspection technologies are heavily reliant on complex machine learning algorithms and expensive cameras for visual recognition. This research investigates an alternative framework whereby light incident on the target object is optimized to enhance object contrast before classification. Optimized illumination spectra are calculated by a machine learning algorithm called Alternating Decision Trees (ADTree). The ADTree algorithm is a powerful classification algorithm on its own right. Further extensions are proposed to ADTree to improve its effectiveness as a tool for data exploration.