Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

Space target motion salient classification using polarimetric retina vision sensing principles

Beninati Anthony, Douard Nicolas , Bauman Gloria, Hoogerhyde Jonathan, Passalaris Anargiros, Giakos Zoe, Nowak Martin, Shrestha Suman, Mohamed Hussam Eldin , Zervakis Michail, Livanos Georgios, Giakos George C.

Full record


URI: http://purl.tuc.gr/dl/dias/93856ECD-BA15-4BD7-882A-1B2077C14954
Year 2018
Type of Item Conference Full Paper
License
Details
Bibliographic Citation A. Beninati, N. Douard, G. Bauman, J. Hoogerhyde, A. Passalaris, Z. Giakos, M. Nowak, S. Shrestha, H. E. Mohamed, M. Zervakis, G. Livanos and G. Giakos, "Space target motion salient classification using polarimetric retina vision sensing principles," in IEEE International Conference on Imaging Systems and Techniques, 2018. doi: 10.1109/IST.2018.8577175 https://doi.org/10.1109/IST.2018.8577175
Appears in Collections

Summary

A new remote sensing retina vision system aimed at classifying rapid moving objects, such as Space debris, based on their motion patterns, is presented. The purpose of this study is to investigate how different types of target complex motion patterns can be detected and discriminated with high accuracy. The remote retina vision sensing system consists of an asynchronous event-based neuromorphic camera coupled with polarization filters enabling improved detection, tracking, and discrimination, with high contrast and dynamic range; a spinning light modulating wheel, operating at varying angular frequency, is placed in front of a static target. The outcome of this study indicates that deep learning combined with Polarimetric Dynamic Vision Sensor p(DVS) principles is well suited to accurately classify targets based on distinct salient features, such as motion patterns, rapidly, at low operational bandwidth, low-power consumption, and storage.

Services

Statistics