Book contents
8 - Limited-modulation filters
Published online by Cambridge University Press: 06 August 2009
Summary
In this chapter we shall optimize statistical correlation pattern recognition under the constraints of being implemented optically. We shall also trace their genealogy and look at some of their predecessors.
Introduction
The objective of this chapter is to treat optical correlation pattern recognition (OCPR) by considering signals, correlation metrics, noise, and limited filter domains.
Digital correlation is computationally more flexible and less noisy than optical correlation. On the other hand, optical correlation can be much faster, and have less weight and volume and power consumption, etc., which motivates us to give it a try. The constraints are very different in the two processes; in some digital correlation filter designs we have seen the necessity of introducing constraints (such as that the filter should have unit energy). In optics we are thoroughly constrained already, without introducing any artificial constraints. Unfortunately, in contrast to the digital version, the form of the optical constraint does not usually provide the solution for an optimizing filter. In this chapter we will nevertheless see how to operate optimally within the limitations imposed by optical practicalities.
The objective of pattern recognition is to recognize the presence of the reference object in the input signal or scene. Optical correlation aims to make'a comparatively bright spot of light that is detectable against a notably dimmer background when the desired object is present in the input image. We shall work with various criterion functions that measure the optical distinctness.
- Type
- Chapter
- Information
- Correlation Pattern Recognition , pp. 295 - 356Publisher: Cambridge University PressPrint publication year: 2005