A New Run-based Connected Component Labeling for Efficiently Analyzing and Processing Holes
This addresses the need for faster image processing in computer vision, but it is incremental as it builds on existing labeling methods.
The paper tackles the problem of connected component labeling and analysis for images, introducing a new algorithm that computes adjacency trees and features on-the-fly, enabling efficient hole processing without rescanning. The result shows it performs these computations faster than existing algorithms for black and white components.
This article introduces a new connected component labeling and analysis algorithm for foreground and background labeling that computes the adjacency tree. The computation of features (bounding boxes, first statistical moments, Euler number) is done on-the-fly. The transitive closure enables an efficient hole processing that can be filled while their features are merged with the surrounding connected component without the need to rescan the image. A comparison with existing algorithms shows that this new algorithm can do all these computations faster than algorithms processing black and white components.