Multiclass pattern recognition using adaptive correlation filters with complex constraints
| Autor: | Vitaly Kober, Oliver G. Campos-Trujillo, Pablo Mario Aguilar-González, Victor H. Diaz-Ramirez |
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| Rok vydání: | 2012 |
| Předmět: |
Contextual image classification
Computer science business.industry Iterative method General Engineering Pattern recognition Atomic and Molecular Physics and Optics Adaptive filter ComputingMethodologies_PATTERNRECOGNITION Discriminant Pattern recognition (psychology) Artificial intelligence Optical filter business Digital filter |
| Zdroj: | Optical Engineering. 51:037203 |
| ISSN: | 0091-3286 |
| DOI: | 10.1117/1.oe.51.3.037203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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