A learning-based thresholding method customizable to computer vision applications

Although a large variety of thresholding techniques have been developed, the selection of a suitable technique for a particular computer vision application is still unsolved and often requires long error and trial procedures analyzing the performance and robustness of different methods. This paper proposes a training-based method that is capable of capturing, learning and imitating thresholding performance from a set of training images allowing ad-hoc adaptation to a given problem. It is applied in two stages: learning and application. In the learning stage a histogram mode object/background classifier is trained with a set of training images and their respective desired threshold values determined by a human. In the application stage, the histogram modes resulting from multi-mode decomposition are classified with the trained classifier and the threshold is computed using a tunable minimum classification error criterion. The presented method can be used in bi-level and multi-level thresholding and requires no settings since all its parameters are determined in the learning step. It has been successfully applied to several problems, some of which are described in the paper.