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A Feature Clustering Algorithm for Scale-space Analysis of Image Structures

Lakemond, Ruan, McKinnon, David N., Fookes, Clinton B., & Sridharan, Sridha (2007) A Feature Clustering Algorithm for Scale-space Analysis of Image Structures. In International Conference on Signal Processing and Communication Systems 2007, 17-19 December 2007, Gold Coast, Australia. (In Press)

Abstract

In describing image features it is important to consider the fact that the appearance of a feature depends on the scale or resolution at which it is observed. Existing robust image feature detectors address the issue by selecting a characteristic scale for each detected feature and subsequently describing the feature as it appears at its characteristic scale. A new method is presented for the multi-scale analysis of derivative based image features that represents a 2D image feature by its locus in scale-space. An algorithm is also presented for efficiently producing the discrete loci representations of image features through clustering features detected at multiple scales. This new method provides an entry point to potential multi-scale descriptions of image features, as well as new possibilities for feature set reduction and filtering.

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307 since deposited on 08 Jan 2008
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ID Code: 11330
Item Type: Conference Paper
Keywords: image processing, computer vision, scale, space, local image features, feature clustering
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2007 (please consult author)
Deposited On: 08 Jan 2008
Last Modified: 29 Feb 2012 23:48

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