Optical filter selection for automatic visual inspection

The color of a material is one of the most frequently used features in automated visual inspection systems. While this is sufficient for many “easy” tasks, mixed and organic materials usually require more complex features. Spectral signatures, especially in the near infrared range, have been proven useful in many cases. However, hyperspectral imaging devices are still very costly and too slow to use them in practice. As a work-around, off-the-shelve cameras and optical filters are used to extract few characteristic features from the spectra. Often, these filters are selected by a human expert in a time consuming and error prone process; surprisingly few works are concerned with automatic selection of suitable filters. We approach this problem by stating filter selection as feature selection problem. In contrast to existing techniques that are mainly concerned with filter design, our approach explicitly selects the best out of a large set of given filters. Our method becomes most appealing for use in an industrial setting, when this selection represents (physically) available filters. We show the application of our technique by implementing six different selection strategies and applying each to two real-world sorting problems.