A unified approach to autofocus and alignment for pattern localization using hybrid weighted Hausdorff distance

Pattern localization is a fundamental task in machine vision, and autofocus is a requirement for any automated inspection system by allowing greater variation in the distance from the camera to the object being imaged. In this paper, we propose a unified approach to simultaneous autofocus and alignment for pattern localization by extending the idea of image reference approach. Under the least trimmed squares (LTS) scheme, the proposed hybrid weighted Hausdorff distance (HWHD) is a robust similarity metric that combines the Hausdorff distance (HD) with the edge-amplitude normalized gradient (EANG) matching. The EANG is designed to characterize the different degrees of blur at the edge points for focus cues, immune to illumination variations between the reference and the target image. We experimentally illustrate its performance on simulated as well as real data.

► We present a unified approach to achieve simultaneous autofocus and alignment.
► Robust hybrid weighted Hausdorff distance (HWHD) is used as similarity metric.
► HWHD works well under illumination variations, partial occlusions, and outliers.
► HWHD exhibits good robustness to noisy images.