Detection Technology, Based On Morphological Theory Of Goal

Automatic target detection in images is of vital importance to modern high-tech warfare. Demand of researching and developing detection algorithms and processing systems with high reliability and high adaptability have been increasing more and more these years, and computer vision and automatic target recognition have been advanced as well. Because vision procedure is actually nonlinear, using nonlinear techniques become one kind of important research tend in image fields. Mathematic morphology is one of nonlinear theories, and has distinguishing features. In this paper key issues of applying the theory to target detection are studied.The core content in realizing target detection with morphological theory is to construct a target detection model and to form a corresponding automatic process. The model consists of two parts: the multi-structuring elements filter formed according to the generalized morphological denotation theorem, and an learning procedure to obtain optimal structural parameters. In designing a multi-structuring elements filter, combination rules and structuring elements of the morphological transform are determined automatically, and one kind of neural networks is taken for the filter, In optimzing structural parameters of the filter, three computation methods are designed respectively, by adopting some priori information in application fields to guide optimal structural parameter learning procedure, which are the BP adaptive learning algorithm, the heuristic genetic learning algorithm and the inductive simulated annealing learning algorithm.The approch developed in this paper is applied to some real image data, and satisfied results are obtained. Both the moving targets in a set of infrared images and the static targets in optical images can be detected automatically. Experimental results indicate that object detection models and relative algorithms have better and robust performance.