Recognizing targets from infrared intensity scan patterns using artificial neural networks

Date
2009-01-30
Authors
Ayta̧ç, T.
Barshan, B.
Advisor
Instructor
Source Title
Optical Engineering
Print ISSN
0091-3286
Electronic ISSN
Publisher
S P I E - International Society for Optical Engineering
Volume
48
Issue
1
Pages
017203-1 - 017203-13
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

This study investigates the use of simple, low-cost infrared sensors for the recognition of geometry and surface type of commonly encountered features or targets in indoor environments, such as planes, corners, and edges. The intensity measurements obtained from such sensors are highly dependent on the location, geometry, and surface properties of the reflecting target in a way that cannot be represented by a simple analytical relationship, therefore complicating the localization and recognition process. We employ artificial neural networks to determine the geometry and the surface type of targets and provide experimental verification with three different geometries and three different surface types. The networks are trained with the Levenberg-Marquardt algorithm and pruned with the optimal brain surgeon technique. The geometry and the surface type of targets can be correctly classified with rates of 99 and 78.4%, respectively. An average correct classification rate of 78% is achieved when both geometry and surface type are differentiated. This indicates that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor in recognizing the patterns. The results demonstrate that processing the data from simple infrared sensors through suitable techniques can help us exploit their full potential and extend their usage beyond well-known applications.

Course
Other identifiers
Book Title
Keywords
Artificial neural networks, Optimal brain surgeon, Pattern recognition, Simple infrared detectors, Target classification, Target differentiation
Citation
Published Version (Please cite this version)