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Proceedings Paper

Detection of local objects of interest in images by using multiscale relevance function
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Paper Abstract

The reliable detection of objects of interest on inhomogeneous background base don image data is a typical detection and recognition problem in many practical applications. In this paper, an algorithm of local object detection is described in the context of change detection based on the difference between two images obtained from the same scene. The proposed detection method using multi-scale relevance function is a model based-approach which takes into account the planar shape model of objects of interest and the regression model of intensity function with respect to objects and background. The image relevance function is an image local operator whose local extrema indicate on the locations of objects or their salient parts termed as primitive patterns. The image fragment centered at the maximum point of the relevance function represents a region of attention. A structure-adaptive binarizaiton is performed within each region of attention by using variable threshold. The comparative testing of the proposed algorithm and the known techniques have shown better performance of the relevance function approach at the approximately same dely of detection.

Paper Details

Date Published: 4 August 2000
PDF: 9 pages
Proc. SPIE 4052, Signal Processing, Sensor Fusion, and Target Recognition IX, (4 August 2000); doi: 10.1117/12.395084
Show Author Affiliations
Roman M. Palenichka, Institute of Physics and Mechanics (Canada)
Yuri B. Rytsar, Institute of Physics and Mechanics (Switzerland)


Published in SPIE Proceedings Vol. 4052:
Signal Processing, Sensor Fusion, and Target Recognition IX
Ivan Kadar, Editor(s)

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