Spectrum Congruency of Multiscale Local Patches for Edge Detection

TL;DR

This paper introduces spectrum congruency, a new feature for edge detection that generalizes phase congruency by measuring energy distribution congruence in multiscale image patches, improving robustness and alignment with human perception.

Contribution

It presents a data-driven, multiscale approach to edge detection based on spectrum congruency, enhancing adaptability and noise robustness over traditional methods.

Findings

Effective in synthetic and real images

Highly robust to noise

Aligns well with human visual perception

Abstract

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.