Open AccessSUPPORT VECTOR MACHINE METHOD TO IDENTIFY AND CLASSIFY ASTRONOMICAL OBJECTS
Author(s) -
P. Sunitha,
N. Prasanna Venkatesh,
Atreya B. M
Publication year - 2020
Publication title -
international journal of engineering applied science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-2143
DOI - 10.33564/ijeast.2020.v05i04.029
Subject(s) - support vector machine , artificial intelligence , computer science , relevance vector machine , pattern recognition (psychology) , machine learning , data mining
Astronomy is the exploration of celestial objects, for instance, the moon, planets, stars, universes and for the most part everything beyond the Earth’s atmosphere. In the past, astronomy has been used to measure time, mark the seasons, and navigate the vast oceans. It inspires us with attractive images and promises answers to the big questions. It acts as a window into the immense size and complexity of space, putting Earth into perspective and promoting global citizenship and pride in our home planet. Astronomical images hold an abundance of knowledge about the universe and its origin. Distinguishing objects in astronomical pictures is not an effortless task even for experienced astronomers. They are at distance measured at light-years, so it is likely that they will show up as faint bright points or mixed with different objects. This work aims at training a computer to identify and classify astronomical objects with satisfying efficiency. Keywords— Astronomical Objects, Image Processing, Segmentation, Extraction,