Paper
18 September 2001 Minimum description length principle applied to camouflage assessment
Georg S. Ruppert, Andreas Wimmer, Horst Bischof, Floris M. Gretzmacher, Guenter Wendner
Author Affiliations +
Abstract
A robust computer based camouflage assessment approach was presented at the AeroSense 2000 conference. Based on experiments with human observers a separability measure was developed. The method was classifier based and best results could be obtained using the C4.5 classifier as a separability measure. Using this method makes camouflage assessment transparent and deterministic presuming correctly specified regions of interest. This paper describes our effort to overcome the drawback of the need of user input at such a critical step within the method. We used unsupervised learning along with an optimizing method to derive information about the number of clusters and other performance measurements. All these measurements coming from the optimization step were adopted to camouflage assessment.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Georg S. Ruppert, Andreas Wimmer, Horst Bischof, Floris M. Gretzmacher, and Guenter Wendner "Minimum description length principle applied to camouflage assessment", Proc. SPIE 4370, Targets and Backgrounds VII: Characterization and Representation, (18 September 2001); https://doi.org/10.1117/12.440093
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Camouflage

Computer programming

Machine learning

Neural networks

Quantization

Algorithm development

Evolutionary algorithms

RELATED CONTENT


Back to Top