Weighted constraint-based star atlas segmentation method

Abstract

The invention discloses a weighted constraint-based star atlas segmentation method, which comprises the following steps of: (1) initializing a segmentation threshold; (2) optimizing an iterative segmentation threshold; (3) weighting the iterative segmentation threshold; and (4) segmenting a star atlas. According to the weighted constraint-based star atlas segmentation method, an optimized iterative algorithm and a weighted iterative algorithm are combined, an ideal segmentation threshold for the star atlas is obtained by adding constraint conditions in a weighting iterative process and using the autonomous adjustments and changes of two weighted factors, then, the star atlas is segmented, and star point targets in a perspective atlas obtained by segmenting can meet the practical requirements on the number of star points; and the weighted constraint-based star atlas segmentation method can effectively eliminate the influence on the identification and location of the star points caused by noises and a background, and the processing speed for the location of the star atlas is greatly increased under the premise that the accuracy in the extraction of the center of mass of following star points is ensured.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a star map segmentation method based on weighted constraints. Background technique [0002] After the original star map is obtained by the star sensor, extracting the position and brightness information of the target star point is a prerequisite for space science observations using star information, such as star map matching of star sensors, space-based optical space target monitoring information processing, etc. The star map positioning and recognition algorithm is the key technology in star map processing. Star map positioning is generally divided into star map preprocessing, star point target recognition, star point centroid extraction and other steps. [0003] Star map preprocessing is to filter and denoise the star map. The recognition of star-point objects consists of two stages: (1) separating the star-point objects from the background (ie, star map ...

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Problems solved by technology

[0009] However, in the process of actual star map processing, it is found that the thresholds obtained by the above two methods cannot effectively segment the star map, and the number of star points obtained by segmentation often does not meet the requirements.

The main disadvantage of the threshold method based on iterative optimization is its scope of application. This method is essentially a global threshold method. In the case of too weak star point target energy and too much noise, the histogram of the image does not have the "double "Peak type" characteristics, using a single threshold for the entire star map cannot obtain effective segmentation

The disadvantage of the adaptive threshold method is that for a star map with a high noise level, the obtained segmentation threshold will have a large deviation, and the star point targets obtained by segmentation are either too many or too few

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