9 April 2012 Multiclass pattern recognition using adaptive correlation filters with complex constraints
Author Affiliations +
An efficient method for reliable multiclass pattern recognition using a bank of adaptive correlation filters is proposed. The method can recognize and classify multiple targets from an input scene by using both the intensity and phase distributions of the output complex correlation plane. The adaptive filters are synthesized with the help of an iterative algorithm based on synthetic discriminant functions with complex constraints. The algorithm optimizes the discrimination capability of the adaptive filters and determines the minimum number of filters in a bank to guarantee a desired classification efficiency. As a result, the computational complexity of the proposed system is low. Computer simulation results obtained with the proposed approach in cluttered and noisy scenes are discussed and compared with those obtained through existing methods in terms of recognition performance, classification efficiency, and computational complexity.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2012/$25.00 © 2012 SPIE
Victor H. Díaz-Ramírez, Oliver G. Campos-Trujillo, Vitaly I. Kober, and Pablo M. Aguilar-Gonzalez "Multiclass pattern recognition using adaptive correlation filters with complex constraints," Optical Engineering 51(3), 037203 (9 April 2012). https://doi.org/10.1117/1.OE.51.3.037203
Published: 9 April 2012
Lens.org Logo
Cited by 14 scholarly publications.
Get copyright permission  Get copyright permission on Copyright Marketplace
Digital filtering

Image filtering

Detection and tracking algorithms

Target recognition


Pattern recognition

Optical filters

Back to Top