C4.5: programs for machine learning
C4.5: programs for machine learning
Reconstructing convex polyominoes from horizontal and vertical projections
Theoretical Computer Science
Reconstructing hv-convex polyominoes from orthogonal projections
Information Processing Letters
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
The reconstruction of polyominoes from their orthogonal projections
Information Processing Letters
Machine Learning
Reconstruction of convex 2D discrete sets in polynomial time
Theoretical Computer Science
Reconstruction in Different Classes of 2D Discrete Sets (Invited Paper)
DCGI '99 Proceedings of the 8th International Conference on Discrete Geometry for Computer Imagery
An algorithm reconstructing convex lattice sets
Theoretical Computer Science
Reconstruction of hv-convex binary matrices from their absorbed projections
Discrete Applied Mathematics - The 2001 international workshop on combinatorial image analysis (IWCIA 2001)
Reconstruction of 8-connected but not 4-connected hv-convex discrete sets
Discrete Applied Mathematics - Special issue: Advances in discrete geometry and topology (DGCI 2003)
Advances in Discrete Tomography and Its Applications (Applied and Numerical Harmonic Analysis)
Advances in Discrete Tomography and Its Applications (Applied and Numerical Harmonic Analysis)
Stability results for the reconstruction of binary pictures from two projections
Image and Vision Computing
Binary tomography by iterating linear programs from noisy projections
IWCIA'04 Proceedings of the 10th international conference on Combinatorial Image Analysis
Binary tomography with deblurring
IWCIA'06 Proceedings of the 11th international conference on Combinatorial Image Analysis
An Evolutionary Approach for Object-Based Image Reconstruction Using Learnt Priors
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Reconstruction of quantitative properties from x-rays
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
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In binary tomography, several algorithms are known for reconstructing binary images having some geometrical properties from their projections. In order to choose the appropriate reconstruction algorithm it is necessary to have a priori information of the image to be reconstructed. In this way we can improve the speed and reduce the ambiguity of the reconstruction. Our work is concerned with the problem of retrieving geometrical information from the projections themselves. We investigate whether it is possible to determine geometric features of binary images if only their projections are known. Most of the reconstruction algorithms based on geometrical information suppose hv -convexity or connectedness about the image to be reconstructed. We investigate those properties in detail, and also the task of separating 4- and 8-connected images. We suggest decision trees for the classification, and show some preliminary experimental results of applying them for the class of hv -convex and connected discrete sets.