Parallel Grayscale Connected Component Analysis via Neighborhood Labeling and Global Region Mapping

This work focuses on parallel grayscale connected component analysis via neighborhood labeling and global region mapping, contributing to Wei Pan’s research thread in 3D vision, optical metrology, geometry processing, and industrial inspection.

In the broader publication record, this work sits in SSRN preprint 5854925 and connects to practical problems in 3D sensing, computational geometry, and industrial machine vision.

Algorithm principle

The method is built around an image transformation model that preserves useful visual information while changing the representation. Its core is to encode structure, intensity, or color relationships in a way that downstream reconstruction, inspection, or reversible conversion can still recover the important content.

Visual material

The local archive does not currently include a matched PDF for this entry, so additional method and result figures are pending. Once the PDF is added, the page can be regenerated with representative visuals.

Results and impact

The reported evaluation in SSRN preprint 5854925 positions the method as a practical contribution to the surrounding 3D vision and industrial inspection workflow. The experiments are used to show whether the proposed representation or pipeline improves robustness, accuracy, speed, or deployability compared with the relevant baseline methods.