Translation and Rotation Invariant Histogram Features for Series of Images

Abstract

Some surfaces, like metallic and varnished ones, can only be properly controlled, if they are inspected under different illumination directions. This requires a three-dimensional input signal: a series of images, where each image shows the same surface but is illuminated from a different angle. This paper presents a method to extract translation and rotation invariant features from such a series to detect and classify topographic irregularities on the inspected surfaces. Invariant features are represented by 3D fuzzy histograms and classified by a support vector machine (SVM). The proposed method performs successfully on varnished wooden surfaces to detect and classify defects on the varnish film. This sort of defects is extremely difficult to recognize, which makes it appropriate to demonstrate the robustness of the method.