Feature selection for automatic visual inspection of solder joints

In this paper, three successive feature reduction methods are employed to select good features for the automatic visual inspection of solder joints. This reduction strategy includes (1) a stability test to remove the features with unstable performance, (2) a separability examination to select the features with good classification capabilities, (3) a correlation analysis to delete the redundant features. Three sets of features are implemented in this feature reduction work: (1) a circular sub-area feature set is related to the intensity conditions within distinct areas in the joint image, (2) a moment of inertia feature set is based upon the intensity of pixels and their relative position in the image plane, (3) a surface curvature feature set analyses the three-dimensional joint topology. Initially 50 features are formulated based on the above strategy. The reduction technique deletes 39 features from this set because of instability, poor performance, and high correlation with other features. Finally, the remaining 11 feature are tested and shown to be superior to state-of-the-art identification methods.