Quaternion Harris for Multispectral Keypoint Detection

We present a new keypoint detection method that generalizes Harris corners for multispectral images by considering the input as a quaternionic matrix. Standard keypoint detectors run on scalar-valued inputs, neglecting input multimodality and potentially missing highly distinctive features. The proposed detector uses information from all channel inputs by defining a quaternionic autocorrelation matrix that possesses quaternionic eigenvectors and real eigenvalues, for the computation of which channel cross-correlations are also taken into account. We have tested the proposed detector on a variety of multispectral images (color, near-infrared), where we have validated its usefulness.