Efficient pattern recognition and classification using a combination of maximum average correlation height and polynomial distance classifier correlation filters

2005 ◽  
Author(s):  
Sharif M. A. Bhuiyan ◽  
Mohammad S. Alam ◽  
S. Richard F. Sims
Keyword(s):  
Pattern Recognition ◽  
Correlation Filters ◽  
Average Correlation

scholarly journals Real-Time Pattern Recognition with Adaptive Correlation Filters

10.5772/4977 ◽  
2007 ◽  
Author(s):  
Vitaly Kober ◽  
Victor H. ◽  
J. Angel ◽  
Josue Alvarez-Borrego
Keyword(s):  
Pattern Recognition ◽  
Real Time ◽  
Correlation Filters ◽  
Time Pattern

Circular harmonic correlation filters for rotationally invariant color pattern recognition

1990 ◽  
Author(s):  
Juan C. Campos Rubio ◽  
Salvador Bosch ◽  
Maria S. Millan Garcia-Verela ◽  
Carlos Ferreira ◽  
Maria J. Yzuel
Keyword(s):  
Pattern Recognition ◽  
Color Pattern ◽  
Correlation Filters ◽  
Rotationally Invariant

scholarly journals Pattern recognition with composite correlation filters designed with multi-objective combinatorial optimization

2015 ◽  
Vol 338 ◽  
pp. 77-89 ◽  
Author(s):  
Victor H. Diaz-Ramirez ◽  
Andres Cuevas ◽  
Vitaly Kober ◽  
Leonardo Trujillo ◽  
Abdul Awwal
Keyword(s):  
Pattern Recognition ◽  
Combinatorial Optimization ◽  
Correlation Filters ◽  
Multi Objective

Spatial Distorted Target Recognition Based on Improved Maximum Average Correlation Height Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 1811-1814
Author(s):  
Hong Zhi Liu ◽  
Yu Chen ◽  
Li Qin Zheng
Keyword(s):  
Pattern Recognition ◽  
Frequency Domain ◽  
Experimental Analysis ◽  
Background Noise ◽  
Target Recognition ◽  
Angular Distortion ◽  
Joint Transform Correlator ◽  
Average Correlation ◽  
Different Shapes ◽  
Mach Filter

Spatial distorted target is very hard to be recognized for complexity and variety of targets, which has restricted the development of pattern recognition technology to a great extent. Joint transform correlator is one of the key equipments to detect and recognize distorted targets. The appearance and development of maximum average correlation height (MACH) algorithm is introduced in this paper. Based on the principle the algorithm and experimental analysis, an improved maximum average correlation height algorithm fit for joint transform correlator is proposed, which has powerful capability of suppressing background noise and widening distortion tolerance. Target images with different shapes including scale or angular distortion constitute MACH filter in frequency domain, which is projected to space domain as reference template including varieties of attitude. To show the feasibility of the algorithm, an airplane with angular distortion in sky is processed by MACH filter as an example. Simulation and optical experimental results are given in this paper. The experiments show the angular distortion tolerance can reach up to 15 degrees. The actual effect of the improved MACH filter algorithm is confirmed very well.

Improvement of pattern recognition with a heuristic design of correlation filters

2009 ◽  
Author(s):  
Oskardie Castro ◽  
Victor H. Diaz-Ramirez ◽  
Arnoldo Diaz-Ramirez ◽  
Vitaly Kober
Keyword(s):  
Pattern Recognition ◽  
Correlation Filters
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