Automatic Target Recognition is one of the most challenging and important requirements in the 21st century
battlefield. Developing an algorithm which is complex enough to recognize targets and simple enough to run in real time
is a challenging problem. Recognizing different targets with different size, orientation and illumination variations
increase the complexity of the problem. This paper proposes a recognition approach, which tries to recognize targets fast
and correctly providing robust performance. The proposed algorithm is based on anisotropic diffusion and edge detection
for image segmentation and discrete cosine transform (DCT) for image classification. First, difference between target
and background is increased by using the anisotropic diffusion filter. In this method, diffusion continues over low
contrast pixels, decreasing the difference between smooth regions. On the other hand, diffusion stops over high contrast
pixels such that the sharper boundaries are preserved. Anisotropic diffusion method controls the directions of diffusion
by an error function which separates low-contrast and high-contrast neighbor pixels. Instead of using partial differential
equations or robust statistical equations as an error function, a simple threshold is used to decrease iteration number and
operation time. Secondly, possible targets are segmented by using "Canny's edge detection" algorithm and "connected
component labeling" algorithm. Finally, possible targets and target database dimensions are reduced and compared by
DCT algorithm. In order to minimize the effect of illumination variations, low frequency coefficients aren't used in this
comparative study. The proposed algorithm is then tested using example pictures, and is able to find targets in less than a
second.
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