In computer vision applications, image matching performed on quality-degraded imagery is difficult due to image
content distortion and noise effects. State-of-the art keypoint based matchers, such as SURF and SIFT, work very well
on clean imagery. However, performance can degrade significantly in the presence of high noise and clutter levels.
Noise and clutter cause the formation of false features which can degrade recognition performance. To address this
problem, previously we developed an extension to the classical amplitude and phase correlation forms, which provides
improved robustness and tolerance to image geometric misalignments and noise. This extension, called Alpha-Rooted
Phase Correlation (ARPC), combines Fourier domain-based alpha-rooting enhancement with classical phase correlation.
ARPC provides tunable parameters to control the alpha-rooting enhancement. These parameter values can be optimized
to tradeoff between high narrow correlation peaks, and more robust wider, but smaller peaks. Previously, we applied
ARPC in the radon transform domain for logo image recognition in the presence of rotational image misalignments. In
this paper, we extend ARPC to incorporate quaternion Fourier transforms, thereby creating Alpha-Rooted Quaternion
Phase Correlation (ARQPC). We apply ARQPC to the logo image recognition problem. We use ARQPC to perform
multiple-reference logo template matching by representing multiple same-class reference templates as quaternion-valued
images. We generate recognition performance results on publicly-available logo imagery, and compare recognition
results to results generated from standard approaches. We show that small deviations in reference templates of sameclass
logos can lead to improved recognition performance using the joint matching inherent in ARQPC.