Segmentation of Small-Field-of-Viewstar Images Based on Kittler Minimum Error Algorithm

Compared with conventional images, small-field-of-view star images are more susceptible to the impacts of uneven illumination and measurement noises. Meanwhile, the traditional threshold segmentation algorithm has poor processing effect or efficiency. In terms of the Gaussian distribution of star image gray, the Kittler minimum error segmentation algorithm was used to process star images on the basis of Bayesian minimum error theory. By the use of the video measuring robot, several commonly-used threshold segmentation algorithms and one-dimensional maximum entropy algorithm are compared and analyzed in terms of goodness methods, regional consistency, regional contrast ratio and time complexity. It is indicated that the Kittler minimum error segmentation algorithm can achieve a satisfied segmentation of the small-field-of-view star images, and improves astronomical data processing efficiency significantly withthe average computation timereduced by about 70%. Laboratory experiments for semi-simulation star map and real star mapshow that the proposed algorithm can extract star points and obtain centroid coordinates accurately,whose horizontal and vertical root mean square errors are 0.025 and 0.019 pixels, respectively. Furthermore, extensive field experiments show that the astronomical longitude and latitude calculated by the proposed algorithm can meet first-class astronomical survey accuracy requirements, whose average internal accordant accuracy is better than 0.015 s and 0.22″ respectively, and external accordant accuracy is better than 0.025 s and 0.35″ respectively.