Performance measures for pattern recognition in a wavelet joint transform correlator

2011 ◽  
Author(s):  
G. Mestre ◽  
C. Torres
1996 ◽  
Vol 127 (1-3) ◽  
pp. 107-116 ◽  
Author(s):  
Shoude Chang ◽  
Simon Boothroyd ◽  
Paparao Palacharla ◽  
Sethuraman Pachanathan

2014 ◽  
Vol 602-605 ◽  
pp. 1811-1814
Author(s):  
Hong Zhi Liu ◽  
Yu Chen ◽  
Li Qin Zheng

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.


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