|Description (include details on usage, files and paper references)||The YACCLAB dataset includes both synthetic and real binary images and is suitable for a wide range of applications, ranging from document processing to survaillance, and features a significant variability in terms of resolution, image density and number of components. All images are provided in 1 bit per pixel PNG format, with 0 (black) being background and 1 (white) being foreground.
Please include the following references when citing the YACCLAB database:
Bolelli, Federico; Cancilla, Michele; Baraldi, Lorenzo; Grana, Costantino "Toward reliable experiments on the performance of Connected Components Labeling algorithms" Journal of Real-Time Image Processing, 1-16.
Grana, Costantino; Bolelli, Federico; Baraldi, Lorenzo; Vezzani, Roberto "YACCLAB - Yet Another Connected Components Labeling Benchmark" Proceedings of the 23rd International Conference on Pattern Recognition , Cancun, Mexico, 4-8 Dec 2016, 2016.