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Spatial-domain implementation of optimal multicriteria correlation filters.

S P Kozaitis, P Puapunpongse

|May 10, 1997

Summary

This study demonstrates that spatial domain convolution kernels can approximate frequency domain correlation filter support regions. This enables real-time, low-cost optical correlation implementations with improved performance.

Area of Science:

Optics and Photonics
Image Processing
Computer Vision

Background:

Correlation filters are crucial for pattern recognition in optical systems.
Frequency domain methods for defining filter support regions are computationally intensive.
Existing methods face limitations with certain spatial light modulators.

Purpose of the Study:

To introduce a novel spatial domain approach for approximating optimal regions of support for correlation filters.
To develop efficient methods for generating these regions for optical correlators.
To demonstrate the performance and advantages of the proposed convolution-based method.

Main Methods:

Approximating frequency domain regions of support using small spatial domain convolution kernels.

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Developing an optimal and a fast nonoptimal approach for generating these kernels.

Implementing the convolution kernels on low-cost arithmetic frame grabbers.

Main Results:

The convolution-based approach yields performance comparable to optimal frequency-domain methods.
The resulting input images for the correlator are always positive-valued due to low-pass filtering characteristics.
The method is compatible with spatial light modulators that cannot produce a zero state.

Conclusions:

Spatial domain convolution offers an efficient and effective alternative for defining correlation filter regions of support.
This approach facilitates real-time, low-cost implementation in optical correlators.
The method enhances the applicability of correlation filters in various optical processing systems.