Hybrid object labelling in digital images

The application of a technique for labelling connected components based on the classical recursive technique is studied. The recursive approach permits labelling, counting, and characterizing objects with a single pass. Its main drawback lies on its very nature: Big objects require a high number of recursive calls, which require a large stack to store local variables and register values. Thus, the risk of stack overflow imposes an impractical limit on image size. The hybrid alternative combines recursion with iterative scanning and can be directly substituted into any program already using the recursive technique. I show how this alternative drastically reduces the number of consecutive recursive calls, and thus the required stack size, while improving overall performance. The method is tested on sets of uniform random binary images and binary images with a random distribution of overlapping square blocks. These test sets provide insight on the adequacy of the algorithm for different applications. The performance of the proposed technique is compared with the classical recursive technique and with an iterative two-pass algorithm using the Union-Find data structure, and the results show an overall increase of speed. The performance of the algorithm in real world machine vision applications is also shown.