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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/2848

Title: Global image feature extraction using slope pattern spectra
Authors: Toudjeu, IT
Van Wyk, BJ
Van Wyk, MA
Van Den Bergh, F
Keywords: Pattern Spectra
Feature extraction
Texture analysis
Integral image
Issue Date: Jun-2008
Publisher: Springer-Verlag Berlin Heidelberg
Citation: Toudjeu, IT, Van Wyk, BJ, Van Wyk, MA and Van Den Bergh, F. 2008. Global image feature extraction using slope pattern spectra. 5th International Conference on Image Analysis and Recognition (ICIAR), Povoa de Varzim, Portugal, 25-27 June 2008, pp 640-649.
Abstract: Traditionally, granulometries are obtained using a series of openings or closings with convex structuring elements of increasing size. Granulometries constitute a useful tool for texture and image analysis since they are used to characterize size distributions and shapes [1], [2]. The granulometric analysis of an image results in a signature of the image with respect to the granulometry used which is referred to as a granulometric curve or pattern spectrum. Granulometric curves are used as feature vectors [3] for applications such as segmentation [4] and texture classification [5]. For example [6], granulometries based on openings with squares of increasing size as structuring elements, were used to extract dominant bean diameter from binary images of coffee beans. Granulometries were also used to estimate the dominant width of the white patterns in the X-ray images of welds [7]. Due to the computational load associated with the calculation of granulometries, Vincent [6], building on the work of Haralick et al. [2] and proposed fast and efficient granulometric techniques using linear openings.
Description: The copyright for this contribution is held by Springer
URI: http://www.springer.de/comp/lncs/index.html
Appears in Collections:General science, engineering & technology

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