Detection and Segmentation of Cosmic Objects Based on Adaptive
  Thresholding and Back Propagation Neural Network
Samia Sultana Shyla Afroge
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.