Detection and Segmentation of Cosmic Objects Based on Adaptive Thresholding and Back Propagation Neural Network
This addresses the challenge of classifying and detecting celestial objects in large, noisy astronomical data for astronomers or researchers, but it appears incremental as it combines existing methods like adaptive thresholding and BPNN.
The authors tackled the problem of detecting and segmenting cosmic objects in astronomical images by proposing an Adaptive Thresholding Method for segmentation and a Back Propagation Neural Network for detection, with pre-processing steps to enhance performance, but no concrete results or numbers are provided in the 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.