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Proceedings Paper

Object detection and segmentation in camouflaged environments
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Paper Abstract

The detection and classification of objects in complicated backgrounds represents a difficult image analysis problem. Previous methods have employed additional information from dynamic scene processing to extract the object of interest from its environment and have produced efficient results. However, the study of object detection based on the information provided uniquely by still images has not been comprehensively studied. In this work, a different approach is proposed, when dynamic information is not available for detection. The presented scheme consists of two main stages. The first one includes a still image segmentation approach that makes use of multi-scale information and graph-based grouping to partition the image scene into meaningful regions. This is followed by a texture-based classification algorithm, in which correspondence analysis is used for feature selection and optimisation purposes. The outcomes of this methodology provide representative results at each stage of the study, to indicate the efficiency and potential of this approach for classification/detection in the difficult task of object detection in camouflaged environments.

Paper Details

Date Published: 15 September 2005
PDF: 8 pages
Proc. SPIE 5909, Applications of Digital Image Processing XXVIII, 59091K (15 September 2005); doi: 10.1117/12.618357
Show Author Affiliations
S. Makrogiannis, Wright State Univ. (United States)
M. Trujillo San-Martin, Wright State Univ. (United States)


Published in SPIE Proceedings Vol. 5909:
Applications of Digital Image Processing XXVIII
Andrew G. Tescher, Editor(s)

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