  This paper proposes a novel feature called spectrum congruency for describing
edges in images. The spectrum congruency is a generalization of the phase
congruency, which depicts how much each Fourier components of the image are
congruent in phase. Instead of using fixed bases in phase congruency, the
spectrum congruency measures the congruency of the energy distribution of
multiscale patches in a data-driven transform domain, which is more adaptable
to the input images. Multiscale image patches are used to acquire different
frequency components for modeling the local energy and amplitude. The spectrum
congruency coincides nicely with human visions of perceiving features and
provides a more reliable way of detecting edges. Unlike most existing
differential-based multiscale edge detectors which simply combine the
multiscale information, our method focuses on exploiting the correlation of the
multiscale patches based on their local energy. We test our proposed method on
synthetic and real images, and the results demonstrate that our approach is
practical and highly robust to noise.
