Alpha-rooted phase correlation (ARPC) is a recently-developed variant of classical phase correlation that includes a Fourier domain image enhancement operation. ARPC combines classical phase correlation with alpha-rooting to provide tunable image enhancement. The alpha-rooting parameters may be adjusted to provide a tradeoff between height and width of the ARPC main lobe. A high narrow main lobe peak provides high matching accuracy for aligned images, but reduced matching performance for misaligned logos. A lower, wider peak trades matching accuracy on aligned logos, for improved matching performance on misaligned imagery. Previously, we developed ARPC and used it in the spatial domain for logo recognition as part of an overall automated document analysis problem. However, spatial domain ARPC performance can be sensitive to logo misalignments, including rotational misalignment. In this paper we use ARPC as a match metric in the radon transform domain for logo recognition. In the radon transform domain, rotational misalignments correspond to translations in the radon transform angle parameter. These translations are captured by ARPC, thereby producing rotation-invariant logo matching. In the paper, we first present an overview of ARPC, and then describe the logo matching algorithm. We present numerical performance results demonstrating matching tolerance to rotational misalignments. We demonstrate robustness of the radon transform domain rotation estimation to noise. We present logo verification and recognition performance results using the proposed approach on a public domain logo database. We compare performance results to performance obtained using spatial domain ARPC, and state-of-the-art SURF features, for logos in salt-and-pepper noise.

Date Published: 2 September 2009
PDF: 12 pages
Proc. SPIE 7443, Applications of Digital Image Processing XXXII, 744305 (2 September 2009); doi: 10.1117/12.824692

Published in SPIE Proceedings Vol. 7443:
Applications of Digital Image Processing XXXII
Andrew G. Tescher, Editor(s)