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Dealing with uncertain feature assessments in interactive object recognition

 
: Bauer, A.; Jürgens, V.; Angele, S.

:
Postprint urn:nbn:de:0011-n-1433717 (585 KByte PDF)
MD5 Fingerprint: 905c9ce535d67fcc98b7c633ff96487a
Copyright 2010 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Created on: 19.10.2010


Kamerman, G.W. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Electro-optical remote sensing, photonic technologies, and applications IV : SPIE Defense and Security 2010, Toulouse, France, 20 september 2008
Bellingham, WA: SPIE, 2010 (Proceedings of SPIE 7835)
ISBN: 978-0-8194-8353-9
Paper 78350L
Conference "Electro-Optical Remote Sensing, Photonic Technologies, and Applications" <4, 2010, Toulouse>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

Abstract
Object recognition is a typical task of aerial reconnaissance and especially in military applications, to determine the class of an unknown object on the battlefield can give valuable information on its capabilities and its threat. RecceMan (Reconnaissance Manual) is a decision support system for object recognition developed by the Fraunhofer IOSB. It supports object recognition by automating the tedious task of matching the object features with the set of possible object classes, while leaving the assessment of features to the trained human interpreter. The quality of the features assessed by the user is influenced by several factors such as the quality of the image of the object. These factors are potential sources of error, which can lead to an incorrect classification and therefore have to be considered by the system. To address this issue, two methods for consideration of uncertainty in human feature assessment - a probabilistic and a heuristic approach - are presented and compared based on an experiment in the exemplary domain of flower recognition.

: http://publica.fraunhofer.de/documents/N-143371.html