@conference {941, title = {Isthmus-based Order-Independent Sequential Thinning}, booktitle = {IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SSPRA)}, year = {2012}, note = {doi: 10.2316/P.2012.778-025}, month = {June 2012}, pages = {28 - 34}, publisher = {IASTED ACTA Press}, organization = {IASTED ACTA Press}, type = {Conference paper}, address = {Crete, Greek}, abstract = {

Thinning as a layer-by-layer reduction is a frequently used technique for skeletonization. Sequential thinning algorithms usually suffer from the drawback of being order-dependent, i.e., their results depend on the visiting order of object points. Earlier order-independent sequential methods are based on the conventional thinning schemes that preserve endpoints to provide relevant geometric information of objects. These algorithms can generate centerlines in 2D and medial surfaces in 3D. This paper presents an alternative strategy for order-independent thinning which follows an approach, proposed by Bertrand and Couprie, which accumulates so-called isthmus points. The main advantage of this order-independent strategy over the earlier ones is that it makes also possible to produce centerlines of 3D objects.

}, doi = {10.2316/P.2012.778-025}, url = {http://www.actapress.com/Content_of_Proceeding.aspx?proceedingID=736}, author = {P{\'e}ter Kardos and K{\'a}lm{\'a}n Pal{\'a}gyi}, editor = {M Petrou and A D Sappa and A G Triantafyllidis} } @conference {1132, title = {Perimeter estimation of some discrete sets from horizontal and vertical projections}, booktitle = {IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA)}, year = {2012}, note = {ScopusID: 84864772360doi: 10.2316/P.2012.778-017}, month = {June 2012}, pages = {174 - 181}, publisher = {IASTED ACTA Press}, organization = {IASTED ACTA Press}, type = {Conference paper}, address = {Crete, Greek}, abstract = {

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.

}, doi = {10.2316/P.2012.778-017}, author = {Tam{\'a}s S{\'a}muel Tasi and M Heged{\H u}s and P{\'e}ter Bal{\'a}zs}, editor = {M Petrou and A D Sappa and A G Triantafyllidis} }