Morphologic filter automatic destination detecting method

Abstract

It is morphology filter automatic object detection method, which comprises the following steps: first to collect training samples to form heredity arithmetic for optimization train, wherein, the arithmetic adopts new range discretization code and self-adapting major / minor cross and dissociation operator; to use collected sample to train the structure element value by heredity arithmetic to form morphology filter based on Top-Hat operator; to filter the infrared object image; finally to do the division based on self=adapting threshold to detected multiple weak spots; to detect the object spots with high signal to noise rate by use of fixed threshold division.

Description

technical field [0001] The invention relates to a target detection method used in the technical field of image processing, in particular to a morphological filter automatic target detection method. Background technique [0002] In recent years, with the development of research on morphology, morphological image processing, a special subject of image processing, has gradually developed into a major research field of image processing, and has gradually become a useful tool for weak and small target detection and recognition. Morphological filters can be decomposed into two basic problems of morphological operations and structural elements. Erosion, dilation, opening and closing operators are the four basic operators of morphological operations. Combining these four basic operators can result in morphological operators with different characteristics. When the morphological operation rules are determined, the final filtering performance of the morphological filter only depends ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies in the prior art, and propose a morphological filter automatic target detection method, which optimizes training based on genetic algorithms, adopts interval discretization coding and self-adaptive primary and secondary crossover and mutation operators The genetic algorithm is used to optimize the training structural elements. At the same time, the Top-Hat high-pass filter operator is selected as the morphological filter operator, so as to effectively overcome the optimal performance of the filter in the existing genetic algorithm optimization morphological filter technology. High, the convergence time of the genetic algorithm is long, and the optimization efficiency is not high, etc.

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