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Probabilistic Reconstruction of hv-convex Polyominoes from Noisy Projection Data

Goupy A., Pagani S. M. C.

Fundamenta Informaticae

2014

Vol. 135, nr 1/2

117--134

In this paper the well-known problem of reconstructing hv-convex polyominoes is considered from a set of noisy data. Differently from the usual approach of Binary Tomography, this leads to a probabilistic evaluation in the reconstruction algorithm, where different pixels assume different probabilities to be part of the reconstructed image. An iterative algorithm is then applied, which, starting from a random choice, leads to an explicit reconstruction matching the noisy data.

Detecting Irrelevant Subtrees to Improve Probabilistic Learning from Tree-structured Data

Habrard A., Bernard M., Sebban M.

Fundamenta Informaticae

2005

Vol. 66, nr 1,2

103--130

In front of the large increase of the available amount of structured data (such as XML documents), many algorithms have emerged for dealing with tree-structured data. In this article, we present a probabilistic approach which aims at a priori pruning noisy or irrelevant subtrees in a set of trees. The originality of this approach, in comparison with classic data reduction techniques, comes from the fact that only a part of a tree (i.e. a subtree) can be deleted, rather than the whole tree itself. Our method is based on the use of confidence intervals, on a partition of subtrees, computed according to a given probability distribution. We propose an original approach to assess these intervals on tree-structured data and we experimentally show its interest in the presence of noise.

Architectures of Granular Information and Their Robustness Properties: a Shadowed Sets Approach

Pedrycz W.

International Journal of Applied Mathematics and Computer Science

1999

Vol. 9, no 2

435-455

This paper addresses an important issue of information of granulation and relationships between the size of information granules and the ensuing robustness aspects. The use of shadowed sets helps identify and quantify absorption properties of set-based information granules. Discussed is also a problem of determining an optimal level of information granulation arising in the presence of noisy data. The study proposes a new architecture of granular computing involving continuous and granulated variables. Numerical examples are also included.