Please use this identifier to cite or link to this item: https://une.intersearch.com.au/unejspui/handle/1959.11/414
Title: Evolution of Spatial Data Templates for Object Classification
Contributor(s): DeRaadt, M (author); Dunstan, N  (author)
Publication Date: 2002
Handle Link: https://hdl.handle.net/1959.11/414
Abstract: Sensing devices are commonly used for the detection and classification of subsurface objects, particularly for the purpose of eradicating Unexploded Ordnance (UXI) from military sites. UXO detection and classification is inherently different to pattern recognition in image processing in that signal responses for the same object will differ greatly when the object is at different depths and orientations. That is, subsurface objects span a multidimensional space with dimensions including depth, azimuth and declination. Thus the search for identifying an instance of an object is extremely large. Our approach is to use templates of actual responses from scans of known objects to model object categories. We intend to justify a method whereby Genetic Algorithms are used to improve the template libraries with respect to their classification characteristics. This chapter describes the application, key features of the Genetic Algorithms tested and the results achieved.
Publication Type: Book Chapter
Source of Publication: Data Mining: A Heuristic Approach, p. 143-156
Publisher: Idea Group Publishing
Place of Publication: Hershey
ISBN: 1930708254
Field of Research (FOR): 080109 Pattern Recognition and Data Mining
HERDC Category Description: B1 Chapter in a Scholarly Book
Other Links: https://igi-pub.com/books/details.asp?id=271
http://books.google.com.au/books?id=vmV2B_bzyD8C&printsec=frontcover#PPA143,M1
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Appears in Collections:Book Chapter
School of Science and Technology

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