In this paper, we design neural networks to estimate the perimeter of simple and more complex discrete sets from their horizontal and vertical projections. The information extracted this way can be useful to simplify the problem of reconstructing the discrete set from its projections, which task is in focus of discrete tomography. Beside presenting experimental results with neural networks, we also reveal some statistical properties of the perimeter of the studied discrete sets.

JF - IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA) PB - IASTED ACTA Press CY - Crete, Greek N1 - ScopusID: 84864772360doi: 10.2316/P.2012.778-017 ER -