Accurate Edge Location Identification Based on Location-Directed Image Modeling

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Abstract

This paper introduces a new approach for determining accurate locations of edges in natural images based on a location-directed image modeling framework. The inability to identify accurate locations follows from the inability to characterize the continuous location variation of edges. We exploit the linear phase-location characteristics in a complex-valued image representation, which guarantees even minuscule location variation be captured by changes in phases. In each band of complex-valued coefficients, the fields of phases represent the locations of edges with particular resolution and along particular direction, and thereby the representation offers a nonparametric framework for gathering multiresolution and multi-directional pieces of evidence about an edge’s locations to jointly locate the edge. Our approach quantifies an edge’s spatial shift that is less than 0.01 pixel and demonstrates its periodic movement within 0.35-pixel range in a series of natural images. This remarkable performance verifies our framework’s ability in identifying accurate locations of edges and opens the door to unveil imperceptible phenomena that are not previously detected.