Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
