Incremental Machine Learning with Holographic Neural Theory for ATD/ATR

Author

Jouan, A. ; Labbé, V.

Author_Institution

Optronic Surveillance, Defence R&D Canada - Valcartier, Val-Belair, Que.

fYear

2006

fDate

10-13 July 2006

Firstpage

1

Lastpage

8

Abstract

Machine learning has been used intensively since the past 30 years to discriminate pixels from background or objects of interest from other classes of objects by training on a set of relevant features. As image sources are now producing more images that we can realistically cope with, the goal is to explore the limits of these approaches for ATD/ATR in order to optimally define the domains in which decisions can be left to automated processes or should require human intervention. With this objective in mind, this paper presents an assessment of the performances of the holographic neural technology (AND Corporation) to support applications that would require incremental learning

Keywords

edge detection; feature extraction; holography; image processing; learning (artificial intelligence); neural nets; object detection; ATD-ATR tool; automated image processing; edge detection; holographic memory; holographic neural theory; image sources; incremental machine learning; training; Filtering algorithms; Holography; Humans; Image processing; Layout; Machine learning; Matched filters; Object detection; Object recognition; Target recognition; ATD/ATR; Machine learning; holographic memory; neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion, 2006 9th International Conference on

Conference_Location

Florence

Print_ISBN

1-4244-0953-5

Electronic_ISBN

0-9721844-6-5

Type

conf

DOI

10.1109/ICIF.2006.301696

Filename

4085982