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.
Abstract (translated)
为了满足在高精度测量中精确边缘检测的严格要求，本文提出了一系列用于亚像素级边缘定位的有效方法。与基于拟合的方法不同，后者将像素强度视为来自特定模型的样本值，我们采取了一个创新的角度，假设在像素级别的强度可以解释为局部积分映射，在该映射下进行亚像素定位。因此，我们提出了一种简单的称为转换强度累加（Converted Intensity Summation, CIS）的亚像素边缘定位方法。 为了克服仅关注单一边缘点定位所导致的有限鲁棒性问题，本文还提出了一种基于稳定边缘区域（Stable Edge Region, SER）的方法，以减轻边缘附近的局部干扰。鉴于边缘统计在局部区域内的一致性存在，该算法寻找边缘附近的相关稳定区域，以便获取稳健参数并实现更精确的位置定位。 此外，我们还引入了一种基于扩展调整的边缘补充方法，通过有效迁移SER来纠正不规则边缘。 在包含常见边缘模式及各种实际场景（如工业PCB图像、遥感和医学图像）的人工合成与真实图像数据集上进行了大量实验。结果验证了CIS方法比当前最优方法具有更高的精度，并且需要更少的执行时间。此外，通过将SER集成到CIS中，所提出的算法在进一步提升抗干扰能力和定位准确性方面表现出了卓越性能。
URL
https://arxiv.org/abs/2502.16502
