The problem of labeling the connected components of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists, 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 many kinds of tests, which analyze the methods from different perspectives. An extensive set of experiments among state-of-the-art techniques is reported and discussed.
Towards Reliable Experiments on the Performance of Connected Components Labeling Algorithms / Bolelli, Federico; Cancilla, Michele; Baraldi, Lorenzo; Grana, Costantino. - In: JOURNAL OF REAL-TIME IMAGE PROCESSING. - ISSN 1861-8200. - 17:2(2020), pp. 229-244. [10.1007/s11554-018-0756-1]
Towards Reliable Experiments on the Performance of Connected Components Labeling Algorithms
Bolelli, Federico
;Cancilla, Michele;Baraldi, Lorenzo;Grana, Costantino
2020
Abstract
The problem of labeling the connected components of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists, 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 many kinds of tests, which analyze the methods from different perspectives. An extensive set of experiments among state-of-the-art techniques is reported and discussed.File | Dimensione | Formato | |
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