Detection and Segmentation of Cosmic Objects Based on Adaptive Thresholding and Back Propagation Neural Network

TL;DR

This paper introduces an adaptive thresholding segmentation method combined with a back propagation neural network to improve the detection and classification of cosmic objects in large astronomical images, addressing challenges like noise and data volume.

Contribution

It presents a novel integrated approach using adaptive thresholding and neural networks for enhanced cosmic object detection in astronomical images.

Findings

Improved segmentation accuracy over traditional methods

Effective detection of various cosmic objects despite noise

Enhanced processing speed for large datasets

Abstract

Astronomical images provide information about the great variety of cosmic objects in the Universe. Due to the large volumes of data, the presence of innumerable bright point sources as well as noise within the frame and the spatial gap between objects and satellite cameras, it is a challenging task to classify and detect the celestial objects. We propose an Adaptive Thresholding Method (ATM) based segmentation and Back Propagation Neural Network (BPNN) based cosmic object detection including a well-structured series of pre-processing steps designed to enhance segmentation and detection.