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2 November 2011 Performance of composite correlation filters for object recognition
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Correlation filters have become an important tool for detection, localization, recognition and object tracking in digital media. This interest in correlation filters has increased thanks to the processing speed advances of the computers that enable the implementation of digital correlation filters in real-time. This paper compares the performance of three correlation filters in the activity of object recognition, specifically human faces with variations in facial expression, pose, rotation, partial occlusion, illumination and additive white Gaussian noise. The analyzed filters are k-law, MACE and OTSDF. Simulation results show that the k-law nonlinear composite filter has the best performance in terms of accuracy and false acceptance rate. Finally, we conclude that a preprocessing algorithm improves significantly the performance of correlation filters for recognizing objects when they have variations in illumination and noise.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Everardo Santiago-Ramirez, J. A. González-Fraga, J. I. Ascencio-Lopez, and Olimpia Buenrostro "Performance of composite correlation filters for object recognition", Proc. SPIE 8011, 22nd Congress of the International Commission for Optics: Light for the Development of the World, 801174 (2 November 2011);

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