The image pattern recognition can accurately identify and locate the target, but image pattern recognition is unable to
accurately recognize the distorted targets (the targets rotated in plane or scale changed), which has restricted the
development of the image pattern recognition. In order to solve the problem of inaccurate recognition for distorted target
in cluttered background among the image pattern recognition, the distorted target images and the training images are
edge extracted by canny operator. The Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter is
synthesized with the edge extracted training images. The low frequency information of the distorted target images and
the filter is enhanced. Then the edge extracted distorted target image is filtered by the OT-MACH filter. Thereby, the
distortion tolerance of the OT-MACH filter is expanded. It can respond higher correlation peaks and have higher
distortion tolerance to recognize various types of distorted targets in cluttered background. By this method, which the
space edge extraction combines with frequency domain filtering, the scale distortion tolerance is 0.72~1.42 times; the
rotation distortion tolerance can reach up to 70 degrees. In order to prove the feasibility of this method, a lot of computer
simulation experiments have been done with the canny operator and the OT-MACH filter.
Target tracking has a wide application in varieties of domains and has a rapid development at home and abroad, so the
research on target tracking is more valuable in recent years. In this paper hybrid optoelectronic joint transform correlator
(HOJTC) is implemented for tracking the target, which is considered as one of the most effective methods.
But in practical application, the low contrast character of the target and the moving distortion problems between the
target and the template may cause the phenomenon of low recognition ratio of HOJTC. In order to solve this problem, a
kind of wavelet-based threshold segmentation method is applied to increase the contrast. Through this algorithm the
histogram of the image is firstly decomposed into wavelet coefficients at every scale with wavelet basis function Sym4.
And then according to segmentation norm and wavelet coefficients, the thresholds can be chosen from the reconstructed
histogram. Finally use these thresholds to segment the image into ideal areas. In addition, for the moving distortion
problem, taking temporal state of the target as the template can realize the template update.
To prove this method, many tracking experiments of low contrast targets have been performed with optical correlation
method. As an example a low contrast target “tank” (the gray contrast is less than 2%) is presented. The tracking result
shows that the brightness of the correlation peaks is enhanced and the target recognition ratio is increased. The
conclusion can be drawn that applying this algorithm in optical correlation method can implement the low contrast target
tracking successfully and this algorithm provides an available solution to low contrast target tracking.
Recognition of low-light level target is attracting more and more concern in modern military areas. However, for the
reason of low contrast, low signal-to-noise ratio and inadequacy information of low-light level target etc, the goal to
detect and recognize the target would not be realized by using photoelectric joint transform correlator. By median
filtering and edge detection with lifting wavelet transform for low-light level target in this paper, the interference of
background noise is reduced and useful information of target and template is enhanced at the same time. Experimental
results show that the brightness and contrast of correlation peaks are both improved obviously after processing the joint
image, which proves the method is very effective in target recognition field by using photoelectric hybrid joint transform