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Proceedings Paper

Detection of local objects in images with textured background by using multiscale relevance function
Author(s): Roman M. Palenichka; Peter Zinterhof; Maxim Volgin
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Paper Abstract

The reliable detection of objects of interest in images with inhomogeneous or textured background is a typical detection and recognition problem in many practical applications such as the medical and industrial diagnostic imaging. In this paper, a method for object detection is described in the framework of a visual attention mechanism based on the concept of multi-scale relevance function. The relevance function is an image local operator that has local maxima at centers of location of supposed objects of interest or their relevant parts termed as primitive objects. The visual attention mechanism based on the relevance function provides the following advantageous features in object detection. The model-based approach is used which exploits multi- scale morphological representation of objects (as object support regions in images) and regression representation of their intensity in order to perform time-effective image analysis. The introduced multi-scale relevance function in application to object detection provides a quick location of local objects of interest invariantly to object size and orientation.

Paper Details

Date Published: 4 October 2000
PDF: 12 pages
Proc. SPIE 4121, Mathematical Modeling, Estimation, and Imaging, (4 October 2000); doi: 10.1117/12.402436
Show Author Affiliations
Roman M. Palenichka, Univ. of Salzburg (Canada)
Peter Zinterhof, Univ. of Salzburg (Austria)
Maxim Volgin, Institute of Physics and Mechanics (Ukraine)


Published in SPIE Proceedings Vol. 4121:
Mathematical Modeling, Estimation, and Imaging
David C. Wilson; Hemant D. Tagare; Fred L. Bookstein; Francoise J. Preteux; Edward R. Dougherty, Editor(s)

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