Learning to detect texture objects by artificial immune approaches

Zheng, Hong, Zhang, Jingxin and Nahavandi, Saeid 2004, Learning to detect texture objects by artificial immune approaches, Future generation computer systems, vol. 20, no. 7, pp. 1197-1208, doi: 10.1016/j.future.2003.11.009.

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Title Learning to detect texture objects by artificial immune approaches
Author(s) Zheng, Hong
Zhang, Jingxin
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Future generation computer systems
Volume number 20
Issue number 7
Start page 1197
End page 1208
Publisher Elsevier BV
Place of publication Amsterdam, The Netherlands
Publication date 2004-10-01
ISSN 0167-739X
1872-7115
Keyword(s) texture object detection
artificial immune system
clonal selection
Summary This paper introduces a novel method to detect texture objects from satellite images. First, a hierarchical strategy is developed to extract texture objects according to their roughness. Then, an artificial immune approach is presented to automatically generate segmentation thresholds and texture filters, which are used in the hierarchical strategy. In this approach, texture objects are regarded as antigens, and texture object filters and segmentation thresholds are regarded as antibodies. The clonal selection algorithm inspired by human immune system is employed to evolve antibodies. The population of antibodies is iteratively evaluated according to a statistical performance index corresponding to object detection ability, and evolves into the optimal antibody using the evolution principles of the clonal selection. Experimental results of texture object detection on satellite images are presented to illustrate the merit and feasibility of the proposed method.


Notes Available online 25 February 2004.
Language eng
DOI 10.1016/j.future.2003.11.009
Field of Research 091007 Manufacturing Robotics and Mechatronics (excl Automotive Mechatronics)
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2003, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30008760

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