Double-Star Detection Using Convolutional Neural Network in Atmospheric Turbulence
Authors
Abstract:
In this paper, we investigate the usage of machine learning in the detection and recognition of double stars. To do this, numerous images including one star and double stars are simulated. Then, 100 terms of Zernike expansion with random coefficients are considered as aberrations to impose on the aforementioned images. Also, a telescope with a specific aperture is simulated. In this work, two kinds of intensity are used, one is in-focus and the other is out-of-focus of the telescope. After these simulations, a convolutional neural network (CNN) is configured and designed and its input is simulated intensity patterns. After learning the network, we could recognize double stars at severe turbulence without needing phase correction with a very high accuracy level of more than 98%.
similar resources
Image Manipulation Detection using Convolutional Neural Network
Using various methods, an image manipulation can be done not only by the image manipulation itself, but also by the criminals of counterfeiters for the purpose of counterfeiting. Digital forensic techniques are needed to detect the tampering and manipulation of images for such illegal purposes. In this paper, we present an image manipulation detection algorithm using deep learning technology, w...
full textAutomated Edge Detection Using Convolutional Neural Network
The edge detection on the images is so important for image processing. It is used in a various fields of applications ranging from real-time video surveillance and traffic management to medical imaging applications. Currently, there is not a single edge detector that has both efficiency and reliability. Traditional differential filter-based algorithms have the advantage of theoretical strictnes...
full textEMG-based wrist gesture recognition using a convolutional neural network
Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...
full textQuad-pixel edge detection using neural network
One of the most fundamental features of digital image and the basic steps in image processing, analysis, pattern recognition and computer vision is the edge of an image where the preciseness and reliability of its results will affect directly on the comprehension machine system made objective world. Several edge detectors have been developed in the past decades, although no single edge detector...
full textQuad-pixel edge detection using neural network
One of the most fundamental features of digital image and the basic steps in image processing, analysis, pattern recognition and computer vision is the edge of an image where the preciseness and reliability of its results will affect directly on the comprehension machine system made objective world. Several edge detectors have been developed in the past decades, although no single edge detector...
full textLipreading using convolutional neural network
In recent automatic speech recognition studies, deep learning architecture applications for acoustic modeling have eclipsed conventional sound features such as Mel-frequency cepstral coefficients. However, for visual speech recognition (VSR) studies, handcrafted visual feature extraction mechanisms are still widely utilized. In this paper, we propose to apply a convolutional neural network (CNN...
full textMy Resources
Journal title
volume 16 issue 2
pages 121- 130
publication date 2022-07
By following a journal you will be notified via email when a new issue of this journal is published.
Hosted on Doprax cloud platform doprax.com
copyright © 2015-2023