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Symmetry Sensitivities of Derivative-of-Gaussian Filters
June 2010 (vol. 32 no. 6)
pp. 1072-1083
Lewis D. Griffin, University College London, London
Martin Lillholm, University College London, London
We consider the measurement of image structure using linear filters, in particular derivative-of-Gaussian (DtG) filters, which are an important model of V1 simple cells and widely used in computer vision, and whether such measurements can determine local image symmetry. We show that even a single linear filter can be sensitive to a symmetry, in the sense that specific responses of the filter can rule it out. We state and prove a necessary and sufficient, readily computable, criterion for filter symmetry-sensitivity. We use it to show that the six filters in a second order DtG family have patterns of joint sensitivity which are distinct for 12 different classes of symmetry. This rich symmetry-sensitivity adds to the properties that make DtG filters well-suited for probing local image structure, and provides a set of landmark responses suitable to be the foundation of a nonarbitrary system of feature categories.

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Index Terms:
Group theory, invariance, pattern analysis
Lewis D. Griffin, Martin Lillholm, "Symmetry Sensitivities of Derivative-of-Gaussian Filters," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1072-1083, June 2010, doi:10.1109/TPAMI.2009.91
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