Labelling is a computer-vision task that identifies all the connected pixels of an object with the same label. It is often followed by the feature extraction, characterization, for the further classification of objects.

Labelling algorithms basically differ in either a recursive or iterative nature. The classical recursive algorithm fits well for time constrained applications because it doesn’t only allow real-time labelling, but characterization at the same time. Both recursive and iterative algorithms use the stack--either the system stack for recursive calls, or the custom stack to allocate data structures. The paper focuses on the risk of stack overflow in labelling algorithms--a fact that can have drastic consequences in real-time applications. The author studies the performance of a very simple labelling algorithm that is a denoted hybrid algorithm, because it combines recursive and iterative approaches, and with the aim of reducing recursivity while having its advantages at the same time.

The paper is easy to read. It exhaustively provides the tests results, which include comparisons of stack use, and time performance versus classical recursive and iterative solutions. Tests on synthetic images of different sizes, both purely random and block structured with several densities, allow probing the robustness of the algorithm. The author also shows the test results of real-time, two-dimensional (2D) and three-dimensional (3D), machine vision (MV) applications. Furthermore, the paper demonstrates how to apply the algorithm to detect spanning objects.

After reading the paper, the reader is persuaded to either choose or replace any labelling implementation with this labelling hybrid algorithm.