Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels

The classic image processing method for flaw detection uses one image of the scene, or multiple images without correspondences between them. To improve this scheme, automated inspection using multiple views has been developed in recent years. This strategy's key idea is to consider as real flaws those regions that can be tracked in a sequence of multiple images because they are located in positions dictated by geometric conditions. In contrast, false alarms (or noise) can be successfully eliminated in this manner, since they do not appear in the predicted places in the following images, and thus cannot be tracked. This paper presents a method to inspect aluminum wheels using images taken from different positions using a method called automatic multiple view inspection. Our method can be applied to uncalibrated image sequences, therefore, it is not necessary to determine optical and geometric parameters normally present in the calibrated systems. In addition, to improve the performance, we designed a false alarm reduction method in two and three views called intermediate classifier block (ICB). The ICB method takes advantage of the classifier ensemble methodology by making use of feature analysis in multiple views. Using this method, real flaws can be detected with high precision while most false alarms can be discriminated.