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Open AccessJournal ArticleDOI

Reconstructing hv-convex polyominoes from orthogonal projections

TLDR
In this paper, the problem of reconstructing a discrete 2D object, represented by a set of grid cells, from its orthogonal projections is addressed, and a simple O(mn min(m2,n2))-time algorithm for reconstructing hv-convex polyominoes is presented.
About
This article is published in Information Processing Letters.The article was published on 1999-03-26 and is currently open access. It has received 134 citations till now. The article focuses on the topics: Discrete tomography.

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Citations
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Journal ArticleDOI

The reconstruction of polyominoes from their orthogonal projections

TL;DR: It will be proved that it is NP-complete to reconstruct a two-dimensional pattern from its two orthogonal projections H and V, if (1) the pattern has to be connected (and hence forms a so-called polyomino), or if (2) the patterns have to be horizontally and vertically convex.
Journal ArticleDOI

Reconstruction of 4- and 8-connected convex discrete sets from row and column projections

TL;DR: The problem of reconstructing a discrete two-dimensional set from its two orthogonal projection (H,V) when the set satisfies some convexity conditions can be solved in polynomial time also in a class of discrete sets which is larger than the class of convex polyominoes, namely, in theclass of 8-connected convex sets.
Book ChapterDOI

Discrete Tomography: A Historical Overview

TL;DR: This chapter gives the details of the classical special case (namely, two-dimensional discrete sets — i.e.,binary matrices — and two orthogonal projections) including a polynomial time reconstruction algorithm.
Journal ArticleDOI

An algorithm reconstructing convex lattice sets

TL;DR: The main result of this paper is a polynomial-time algorithm solving the reconstruction problem for the “Q-convex” sets, a new class of subsets of Z2 having a certain kind of weak connectedness.
Journal ArticleDOI

Comparison of algorithms for reconstructing hv-convex discrete sets

TL;DR: Three reconstruction algorithms to be used for reconstructing hv-convex discrete sets from their row and column sums are compared and it is shown that the algorithm which was better from the viewpoint of worst time complexity was the worse from the viewpoints of average time complexity and memory requirements.
References
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Journal ArticleDOI

On the Complexity of Timetable and Multicommodity Flow Problems

TL;DR: A very primitive version of Gotlieb’s timetable problem is shown to be NP-complete, and therefore all the common timetable problems areNP-complete.
Journal ArticleDOI

A linear-time algorithm for testing the truth of certain quantified boolean formulas☆

TL;DR: A simple constructive algorithm for the evaluation of formulas having two literals per clause, which runs in linear time on a random access machine.
Journal ArticleDOI

Reconstructing convex polyominoes from horizontal and vertical projections

TL;DR: Some operations for recontructing convex polyominoes by means of vectors H's and V's partial sums allows a new algorithm to be defined whose complexity is less than O(n2m2).
Journal ArticleDOI

Matrices of zeros and ones with fixed row and column sum vectors

TL;DR: In this paper, the combinational properties of all m × n matrices of 0's and 1's having r i 1's in row i and s i 1s in column j were studied.
Journal ArticleDOI

On the computational complexity of reconstructing lattice sets from their x-rays

TL;DR: It turns out that for all d ⩾ 2 and for a prescribed but arbitrary set of m ⩽ 2 pairwise nonparallel lattice directions, the problems are solvable in polynomial time if m = 2 and are NP-complete (or NP-equivalent) otherwise.
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