The problem of labeling the connected components (CCL) of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists and should be mandatory provided as output, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for three kinds of test, which analyze the methods from different perspectives. The fairness of the comparisons is guaranteed by running on the same system and over the same datasets. Examples of usage and the corresponding comparisons among state-of-the-art techniques are reported to confirm the potentiality of the benchmark.

YACCLAB - Yet Another Connected Components Labeling Benchmark / Grana, Costantino; Bolelli, Federico; Baraldi, Lorenzo; Vezzani, Roberto. - (2016), pp. 3109-3114. ((Intervento presentato al convegno 2016 23rd International Conference on Pattern Recognition (ICPR) tenutosi a Cancun, Mexico nel Dec 4-8 [10.1109/ICPR.2016.7900112].

YACCLAB - Yet Another Connected Components Labeling Benchmark

GRANA, Costantino;BOLELLI, FEDERICO;BARALDI, LORENZO;VEZZANI, Roberto
2016-01-01

Abstract

The problem of labeling the connected components (CCL) of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists and should be mandatory provided as output, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for three kinds of test, which analyze the methods from different perspectives. The fairness of the comparisons is guaranteed by running on the same system and over the same datasets. Examples of usage and the corresponding comparisons among state-of-the-art techniques are reported to confirm the potentiality of the benchmark.
2016 23rd International Conference on Pattern Recognition (ICPR)
Cancun, Mexico
Dec 4-8
3109
3114
Grana, Costantino; Bolelli, Federico; Baraldi, Lorenzo; Vezzani, Roberto
YACCLAB - Yet Another Connected Components Labeling Benchmark / Grana, Costantino; Bolelli, Federico; Baraldi, Lorenzo; Vezzani, Roberto. - (2016), pp. 3109-3114. ((Intervento presentato al convegno 2016 23rd International Conference on Pattern Recognition (ICPR) tenutosi a Cancun, Mexico nel Dec 4-8 [10.1109/ICPR.2016.7900112].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1103793
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