Subpixel Edge Localization Based on Converted Intensity Summation under Stable Edge Region

TL;DR

This paper introduces a novel subpixel edge localization method called Converted Intensity Summation (CIS), which interprets pixel intensity as a local integral, achieving higher accuracy and robustness than existing methods, especially in noisy and complex scenarios.

Contribution

The paper proposes a new subpixel edge localization approach based on intensity integral interpretation and introduces a Stable Edge Region (SER) algorithm for enhanced robustness and precision.

Findings

CIS outperforms state-of-the-art methods in accuracy.

CIS requires less computation time.

Integration of SER improves anti-interference and localization accuracy.

Abstract

To satisfy the rigorous requirements of precise edge detection in critical high-accuracy measurements, this article proposes a series of efficient approaches for localizing subpixel edge. In contrast to the fitting based methods, which consider pixel intensity as a sample value derived from a specific model. We take an innovative perspective by assuming that the intensity at the pixel level can be interpreted as a local integral mapping in the intensity model for subpixel localization. Consequently, we propose a straightforward subpixel edge localization method called Converted Intensity Summation (CIS). To address the limited robustness associated with focusing solely on the localization of individual edge points, a Stable Edge Region (SER) based algorithm is presented to alleviate local interference near edges. Given the observation that the consistency of edge statistics exists in the local region, the algorithm seeks correlated stable regions in the vicinity of edges to facilitate the acquisition of robust parameters and achieve higher precision positioning. In addition, an edge complement method based on extension-adjustment is also introduced to rectify the irregular edges through the efficient migration of SERs. A large number of experiments are conducted on both synthetic and real image datasets which cover common edge patterns as well as various real scenarios such as industrial PCB images, remote sensing and medical images. It is verified that CIS can achieve higher accuracy than the state-of-the-art method, while requiring less execution time. Moreover, by integrating SER into CIS, the proposed algorithm demonstrates excellent performance in further improving the anti-interference capability and positioning accuracy.