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Content-adaptive feature extraction using image variance

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Coleman, SA, Scotney, BW and Herron, MG (2005) Content-adaptive feature extraction using image variance. Pattern Recognition, 38 (12). pp. 2426-2436. [Journal article]

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DOI: 10.1016/j.patcog.2005.05.006

Abstract

The problem of scale is of fundamental interest in image processing, as the features that we visually perceive and find meaningful vary significantly depending on their size and extent. It is well known that the strength of a feature in an image may depend on the scale at which the appropriate detection operator is applied. It is also the case that many features in images exist significantly over a limited range of scales, and, of particular interest here, that the most salient scale may vary spatially over the feature. Hence, when designing feature detection operators, it is necessary to consider the requirements for both the systematic development and adaptive application of such operators over scale- and image-domains.We present a new approach to the design of scalable derivative edge detectors, based on the finite element method, that addresses the issues of method and scale adaptability. The finite element approach allows us to formulate scalable image derivative operators that can be implemented using a combination of piecewise-polynomial and Gaussian basis functions. The issue of scale is addressed by partitioning the image in order to identify local key scales at which significant edge points may exist. This is achieved by consideration of empirically designed functions of local image variance.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Information and Communication Engineering
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:36
Deposited By:Dr Sonya Coleman
Deposited On:08 Mar 2010 10:04
Last Modified:15 Jun 2011 11:07

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