New approach in automatic target recognition

A new approach for automatic battle tank recognition and segmentation has been developed. This paper presents the design and implementation for this new algorithm. The main ideas, approaches, limitations, and possible extension for future work are also discussed. This approach consists of three phases. In the first phase (foreground and background separation), It discriminates the foreground targets from background based on the feature data such as gray(or color) levels, and statistical data such as gray levels distribution and histogram. In the second phase (preliminary individual target recognition), each individual target is detected by a region growth algorithm. Each possible target is reconstructed. In the third phase, the targets are recognized by syntactic analysis based on the common basic structure of any military tank. These non-target objects are discarded by syntactic analysis. The syntactic analysis (in last phase) is to extract all basic components of a tank and determine the relative relationship among the components based on the analysis of the waveform of boundary distance function from the centroid. The experiments show very satisfactory result.