Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial intelligence (2nd ed.): structures and strategies for complex problem-solving
Artificial intelligence (2nd ed.): structures and strategies for complex problem-solving
A general approach to connected-component labeling for arbitrary image representations
Journal of the ACM (JACM)
Parallel Architectures and Algorithms for Image Component Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the application of the enhanced Hoshen-Kopelman algorithm for image analysis
Pattern Recognition Letters
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Efficiency of a Good But Not Linear Set Union Algorithm
Journal of the ACM (JACM)
The C++ Programming Language, Third Edition
The C++ Programming Language, Third Edition
Computer Vision
Digital Picture Processing
Coherence-Enhancing Diffusion Filtering
International Journal of Computer Vision
A SOFM Improves a Real Time Quality Assurance Machine Vision System
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
A novel Fourier descriptor based image alignment algorithm for automatic optical inspection
Journal of Visual Communication and Image Representation
A Run-Based One-Scan Labeling Algorithm
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
An efficient first-scan method for label-equivalence-based labeling algorithms
Pattern Recognition Letters
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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.