A novel Fourier descriptor based image alignment algorithm for automatic optical inspection
Journal of Visual Communication and Image Representation
Fast connected-component labeling
Pattern Recognition
A Run-Based One-Scan Labeling Algorithm
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Connected Component Labeling Techniques on Modern Architectures
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
An efficient first-scan method for label-equivalence-based labeling algorithms
Pattern Recognition Letters
Real-Time Object-Based Video Segmentation Using Colour Segmentation and Connected Component Labeling
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Fast block based connected components labeling
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Optimized block-based connected components labeling with decision trees
IEEE Transactions on Image Processing
Fast and memory efficient 2-D connected components using linked lists of line segments
IEEE Transactions on Image Processing
A comparative review of two-pass connected component labeling algorithms
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
Pixel-based machine learning in medical imaging
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Journal of Intelligent and Robotic Systems
Simulation of face/hairstyle swapping in photographs with skin texture synthesis
Multimedia Tools and Applications
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We present an efficient run-based two-scan algorithm for labeling connected components in a binary image. Unlike conventional label-equivalence-based algorithms, which resolve label equivalences between provisional labels, our algorithm resolves label equivalences between provisional label sets. At any time, all provisional labels that are assigned to a connected component are combined in a set, and the smallest label is used as the representative label. The corresponding relation of a provisional label and its representative label is recorded in a table. Whenever different connected components are found to be connected, all provisional label sets concerned with these connected components are merged together, and the smallest provisional label is taken as the representative label. When the first scan is finished, all provisional labels that were assigned to each connected component in the given image will have a unique representative label. During the second scan, we need only to replace each provisional label by its representative label. Experimental results on various types of images demonstrate that our algorithm outperforms all conventional labeling algorithms.