Gopalan Ravichandran

Minimum noise and correlation energy optical correlation filter.

A new distortion-invariant optical correlation filter to produce easily detectable correlation peaks in the presence of noise and clutter and to provide better intraclass recognition is presented. The basic ideas of the minimum variance synthetic discriminant function correlation filter (which minimizes noise variance in the output correlation peak/plane) and the minimum average correlation energy filter (which minimizes the average correlation plane energy over all the training images) are unified in a new filter that produces sharp correlation peaks while maintaining an acceptable signal-to-noise ratio in the correlation plane output. This new minimum noise and correlation energy filter approach introduces the concept of using the spectral envelope of the training images and the noise power spectrum to obtain a tight bound to the energy minimization problem that is associated with distortion-invariant filters in noise while allowing the user a variable parameter to adjust depending on the noise or clutter that is expected. We present the mathematical basis for the minimum noise and correlation energy filter and the initial simulation results.

Gaussian–minimum average correlation energy filters

Correlation filters with sharp delta-function correlation peaks [such as phase-only filters and minimum average correlation energy (MACE) filters] do not recognize images on which they are not trained. We show that the MACE filter cannot always recognize intermediate images of true class objects (e.g., aspect views or rotations midway between two training images). New Gaussian-MACE filters offer a solution to this problem.

Advanced in-plane rotation-invariant correlation filters

Advanced correlation filter synthesis algorithms to achieve rotation invariance are described. We use a specified form for the filter as the rotation invariance constraint and derive a general closed-form solution for a multiclass rotation-invariant filter that can recognize a number of different objects. By requiring the filter to minimize the average correlation plane energy, we produce a multiclass rotation invariant (RI) RI-MACE filter, which controls correlation plane sidelobes and improves discrimination against false targets. To improve noise performance, we require the filter to minimize a weighted sum of correlation plane signal and noise energy. Initial test results of all filters are provided. >

Circular-harmonic function, minimum average correlation energy filters

New distortion-invariant correlation filters for in-plane rotation invariance are considered. These use circular-harmonic functions combined with minimum-average correlation-plane filter techniques. Various circular-harmonic function shortcomings are quantified.

Generalized in-plane rotation-invariant minimum average correlation energy filter

A rotation-invariant correlation filter to minimize correlation plane energy and produce a sharp and easily detected peak is addressed. The filter uses symmetry requirements to achieve rotation invariance and hence is generalized. Full mathematical detail of the filter synthesis and initial test results are presented. Drawbacks of the existing circular harmonic function (CHF) and other rotation-invariant filters are noted and used as motivation for these new filters.

Noise and discrimination performance of the MINACE optical correlation filter

A new distortion-invariant optical correlation filter used to produce easily detectable correlation peaks in the presence of noise and clutter, and to provide better intraclass recognition, is presented. The new minimum noise and correlation energy (MINACE) filter produces sharp correlation peaks while maintaining an acceptable SNR in the correlation plane output in the presence of false targets (discrimination) and noise (clutter and background). The MINACE filter achieves this by use of a new tighter bound to the correlation plane energy problem. New test results are presented to show that this approach provides improved recognition (and reduced true class training set size), good discrimination, and improved performance in noise and clutter. Filter size requirements for such advanced distortion- invariant filters are also addressed.

Modified MACE Filters For Distortion-Invariant Recognition Of Relocatable Targets

The original MACE (Minimum Average Correlation Energy ) filters are addressed with attention to the effect of iterative refinements, new database tests (on strategic relocatable objects, missile launch-ers), depression angle and resolution effects on the number of training set imagery required and noise performance. Major attention is given to our new MACE filter algorithms for distortion-invariant pattern recognition: iterative shift filter synthesis, MV-MACE (Minimum Variance-MACE) filters (for improved noise performance), multiple symbolic encoded filters and Gaussian Minimum Mean Square Error(MMSE) filters.

Advanced in-plane rotation-invariant filter results

Advanced filter algorithms using in-plane rotation invariance as an additional constraint during filter synthesis are presented. The new rotation-invariant Minimum Average Correlation Energy (RI-MACE) filter provides sharp, easily detected correlation peaks and excellent discrimination against other false objects. In the presence of background noise and clutter, the rotation-invariant Minimum Noise and Correlation Energy (RI-MINACE) filter (modified RI- MACE filter) uses the noise information during filter synthesis to obtain improved noise performance and also maintain easily detected correlation peaks. New test results are presented to show the improved discrimination capability of the RI-MACE filter and the improved noise performance of the RI-MINACE filter.

Initial key word OCR filter results

Initial simulated optical correlation filter results to locate key words in destination address blocks on machine printed United States Postal Service (USPS) envelope mail are presented. The filters used are SDF MACE G-MACE and MINACE. These filters provide sharp correlation peaks allow controlled tolerance of intra-class (pitch font) variations and improved false class rejection (using symbolic filters).

Nonlinear distortion-invariant filter performance with false targets, noise, and clutter

We provide insight into the disadvantages of various non-linear distortion-invariant optical correlation filters. From this, guidelines for improved optical correlation filters emerge including: filters for intra-class recognition, filters for clutter rejection and the different types of clutter that arise, hierarchical inference filters, remarks of Fourier vs. image domain synthesis, filter space-bandwidth product (SBWP), techniques to reduce correlation plane energy and filter performance measures and issues.