Inspecting mixed textured lens collar images using discrete Fourier transformation

Abstract

Superficial and electroplating defects are the most commonly seen flaws on the lens collar. The former arose from cutter offset or chip winding during the cutting process, while the later occurred if the surface was stained with rough or foreign material during the electroplating process. Relying on human inspection to ensure quality of a lens collar was time consuming and accounted for occupational injury. Thus, implementation of automatic inspection technology became invertible in the mass production environment. Since the texture on the surface of lens collar was not only regular but also statistical, the system used image restoration based on discrete Fourier transformation (DFT) to detect defects embedding on those two types of textures.