Fourier descriptors for defect indication in a multiscale and multisensor measurement system

Efficient inspection of an object for deformations and defects requires comparison with an existing real or simulated reference model. Fourier descriptor (FDs) based shape analysis is an effective method for describing a shape using the Fourier transform. This shape representation can be easily modified to achieve shift, rotation, and scale invariance. We propose two new methods, namely the ring sampling and the spiral sampling methods, which enable the usage of FDs in order to detect defects on micro-optical elements like microlens arrays. As an example the measurement data obtained from a confocal microscope has been used to show the effectiveness of the two approaches for both indicating and detecting surface defects. Microlens arrays with different types of defects including global (deformed lenses causing aberrations) and local defects (scratches) were simulated using a confocal microscopy simulation tool to test the reliability of the methods. A classifier differentiates between global and local defective lenses. In order to represent other kinds of objects using FDs, the methods can be easily modified or extended. The whole process has been implemented into an automated multiscale multisensor measurement system, which focuses on fast detection of defects on micro-optical and microelectromechanical systems.