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Combining Different Types of Scale Space Interest Points Using Canonical Sets
Book chapter   Peer reviewed

Combining Different Types of Scale Space Interest Points Using Canonical Sets

Frans Kanters, Trip Denton, Ali Shokoufandeh, Luc Florack and Bart ter Haar Romeny
Scale Space and Variational Methods in Computer Vision, pp 374-385
2007
DOI: https://doi.org/10.1007/978-3-540-72823-8_32
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

Corner Point Edge Point Image Reconstruction Interest Point Scale Space
Scale space interest points capture important photometric and deep structure information of an image. The information content of such points can be made explicit using image reconstruction. In this paper we will consider the problem of combining multiple types of interest points used for image reconstruction. It is shown that ordering the complete set of points by differential (quadratic) TV-norm (which works for single feature types) does not yield optimal results for combined point sets. The paper presents a method to solve this problem using canonical sets of scale space features. Qualitative and quantitative analysis show improved performance over simple ordering of points using the TV-norm.

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1 citations in Web of Science
2 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Imaging Science & Photographic Technology
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