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Do we really need Gaussian filters for feature point detection?

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3 Author(s)
Lee-kang Liu ; Univ. of California at San Diego, La Jolla, CA, USA ; Truong Nguyen ; Chan, S.H.

This paper studies the issue of which filters should be used for feature point detection. Classical feature point detection methods, e.g., SIFT, are based on the scale-space theory in which Gaussian filters are proven to be optimal under the scale-space axiom. However, the recent method SURF demonstrates empirically that a box filter can also achieve good performance even though it violates the scale-space axiom. This leads to the question: Is Gaussian filters necessary for feature point detection? Based on the analysis using filter bank and detection theory, we show that theoretically it is possible for a box filter to perform better than the Gaussian filter. Additionally, we show that a new filter, pyramid filter, performs better than both box and Gaussian filters in some situations.

Published in:

Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European

Date of Conference:

27-31 Aug. 2012