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A labeling algorithm based on a forest of decision trees

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Abstract

Connected component labeling (CCL) is one of the most fundamental operations in image processing. CCL  is a procedure for assigning a unique label to each connected component. It is a mandatory step between low-level and high-level image processing. In this work, a general method is given to improve the neighbourhood exploration in a two-scan labeling. The neighbourhood values are considered as commands of a decision table. This decision table can be represented as a decision tree. A block-based approach is proposed so that values of several pixels are given by one decision tree. This block-based approach can be extended to multiple connectivities, 2D and 3D. In a raster scan, already seen pixels can be exploited to generate smaller decision trees. New decision trees are automatically generated from every possible command. This process creates a decision forest that minimises the number of memory accesses. Experimental results show that this method is faster than the state-of-the-art labelling algorithms and require fewer memory accesses. The whole process can be generalised to any given connectivity.

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Author notes

  1. T. Chabardès

    Present address: Terra3D, 36 Boulevard de la Bastille, 75012, Paris, France

Authors and Affiliations

  1. PSL Research University-MINES ParisTech-CMM, Center for Mathematical Morphology, 35 rue Saint Honoré, Fontainebleau, France

    T. Chabardès, P. Dokládal & M. Bilodeau

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  1. T. Chabardès
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  2. P. Dokládal
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  3. M. Bilodeau
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Correspondence to T. Chabardès.

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Chabardès, T., Dokládal, P. & Bilodeau, M. A labeling algorithm based on a forest of decision trees. J Real-Time Image Proc 17, 1527–1545 (2020). https://doi.org/10.1007/s11554-019-00912-8

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  • DOI: https://doi.org/10.1007/s11554-019-00912-8

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