1992
DOI: 10.1364/ao.31.001109
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Advanced distortion-invariant minimum average correlation energy (MACE) filters

Abstract: The original minimum average correlation energy (MACE) filter is addressed by using a new database (strategic relocatable objects, missile launchers) and including noise performance, depression angle, and resolution effects on the number of training set images that are required. Major attention is given to our new MACE filter algorithms for distortion-invariant pattern recognition: shifted-MACE filters (to suppress large false correlation peaks), minimum variance-MACE filters (for improved noise performance), … Show more

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Cited by 39 publications
(19 citation statements)
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“…The MACE filter Casasent & Ravichandran (1992) is designed with the objective of minimizing the average energy in the correlation plane resulting from the training images, restricting the value at the origin with respect to a preset value. The solution is represented by:…”
Section: Mace (Minimum Average Correlation Energy)mentioning
confidence: 99%
“…The MACE filter Casasent & Ravichandran (1992) is designed with the objective of minimizing the average energy in the correlation plane resulting from the training images, restricting the value at the origin with respect to a preset value. The solution is represented by:…”
Section: Mace (Minimum Average Correlation Energy)mentioning
confidence: 99%
“…The MACE filter reduces false alarms and sidelobes and improves discrimination, but can result in poor intraclass recognition of nontraining set data. 8 Tests of these filters were performed on a three object SAR problem with a 35-deg range of aspect angles for each object and with the same 100 clutter chips we used. 9 One filter was formed for each object; using about half of the 35 images per object for training ͑17͒ and about half ͑18͒ for testing.…”
Section: Prior Workmentioning
confidence: 99%
“…In order to reduce the arduous computational burden of storing and processing vast image dictionaries, arising from the object image variability effects caused by intrinsic and extrinsic inconsistencies, synthetic discriminant functions (SDF) and distortion tolerant filters have been developed [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46]. In the vein of SDF, this paper presents a novel and straightforward technique for substantially reducing the number of images in the target dictionary without adversely affecting robustness of the system.…”
Section: Introductionmentioning
confidence: 99%