A reduction operator transforms a binary picture only by changing some black points to white ones, which is referred to as deletion. Sequential reductions may delete just one point at a time, while parallel reductions can alter a set of points simultaneously. Two reductions are called equivalent if they produce the same result for each input picture. This work lays a bridge between the parallel and the sequential strategies. A class of deletion rules are proposed that provide 2D parallel reductions being equivalent to sequential reductions. Some new sufficient conditions for topology-preserving parallel reductions are also reported.

%B Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications %S Lecture Notes in Computer Science %I Springer %C Berlin; Heidelberg %P 17 - 24 %8 Nov 2013 %@ 978-3-642-41821-1 %G eng %U http://link.springer.com/chapter/10.1007%2F978-3-642-41822-8_3 %9 Conference paper %! Conference Paper %R 10.1007/978-3-642-41822-8_3 %0 Book Section %B Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications %D 2013 %T Directional Convexity Measure for Binary Tomography %A Tamás Sámuel Tasi %A László Gábor Nyúl %A Péter Balázs %E Gabriella Sanniti di Baja %E Jose Ruiz-Shulcloper %XThere is an increasing demand for a new measure of convexity fordiscrete sets for various applications. For example, the well- known measures for h-, v-, and hv-convexity of discrete sets in binary tomography pose rigorous criteria to be satisfied. Currently, there is no commonly accepted, unified view on what type of discrete sets should be considered nearly hv-convex, or to what extent a given discrete set can be considered convex, in case it does not satisfy the strict conditions. We propose a novel directional convexity measure for discrete sets based on various properties of the configuration of 0s and 1s in the set. It can be supported by proper theory, is easy to compute, and according to our experiments, it behaves intuitively. We expect it to become a useful alternative to other convexity measures in situations where the classical definitions cannot be used.

%B Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications %I Springer Verlag %C Berlin; Heidelberg %P 9 - 16 %8 2013 %G eng %U http://link.springer.com/chapter/10.1007%2F978-3-642-41827-3_2 %9 Conference paper %R 10.1007/978-3-642-41827-3_2 %0 Book Section %B Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP) %D 2013 %T Reconstruction and Enumeration of hv-Convex Polyominoes with Given Horizontal Projection %A Norbert Hantos %A Péter Balázs %E Jose Ruiz-Shulcloper %E Gabriella Sanniti di Baja %B Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP) %S Lecture Notes in Computer Science %I Springer %C Heidelberg; London; New York %P 100 - 107 %8 Nov 2013 %@ 978-3-642-41821-1 %G eng %9 Conference paper %R 10.1007/978-3-642-41822-8_13 %0 Book Section %B Discrete Geometry for Computer Imagery %D 2003 %T A fast algorithm for reconstructing hv-convex 8-connected but not 4-connected discrete sets %A Péter Balázs %A Emese Balogh %A Attila Kuba %E Ingela Nyström %E Gabriella Sanniti di Baja %E Stina Svensson %B Discrete Geometry for Computer Imagery %I Springer Verlag %C Berlin; Heidelberg; New York; London; Paris; Tokyo %P 388 - 397 %8 2003/// %G eng