Celestial Object Detection in Astronomical Images Using MSE and Jacobi Moments

Abstract

Astronomical images share a common characteristic, which is low variability. This term refers to images with minimal changes or variations in pixel values or features. In this article, we leverage the low variability characteristic of astronomical images to detect celestial objects. We compute the mean squared error for the reconstructed astronomical image, derived from its Jacobi moments. These moments are calculated multiple times, focusing on different regions within the image. The mean squared error metric serves as the score function for the simulated annealing algorithm, aiding in the identification of regions with the highest information loss. The parameters used to compute Jacobi moments to focus on that region are then interpreted as the coordinates of celestial objects. This method proves effective for preprocessing images since it provides optimal parameters for Jacobi polynomials, which will enhance their feature extraction capability. Additionally, it serves as an object detection method, as we can interpret the Jacobi moments' parameters as coordinates for the objects within the images.