Object detection and segmentation in camouflaged environments

S. Makrogiannis; M. Trujillo San-Martin

doi:10.1117/12.618357

15 September 2005 Object detection and segmentation in camouflaged environments

Proceedings Volume 5909, Applications of Digital Image Processing XXVIII; 59091K (2005) https://doi.org/10.1117/12.618357
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

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.

Citation Download Citation

S. Makrogiannis and M. Trujillo San-Martin "Object detection and segmentation in camouflaged environments", Proc. SPIE 5909, Applications of Digital Image Processing XXVIII, 59091K (15 September 2005); https://doi.org/10.1117/12.618357

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