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

This manuscript targets connected-component analysis for grayscale images, where region extraction must preserve intensity relationships while remaining efficient on large images.

This work appears in SSRN preprint 5854925.

Algorithm principle

The method decomposes the problem into local neighborhood labeling and global region mapping. Local labels expose candidate connected structures, while the mapping stage merges them into consistent grayscale regions.

Visual material

Detailed method and result figures will be added when the original paper assets are available.

Results and impact

The work is relevant to industrial image-analysis pipelines where region labeling is a core primitive for defect segmentation, measurement, and downstream inspection.