## Abstract A vector entropy optimizationβbased neural network approach is presented to handle image reconstructions from two orthogonal projections. An accurate and parallel reconstruction is attained with this method allowing parallel implementation. This is an attempt to extract the image inform
Reconstruction of two-valued matrices from their two projections
β Scribed by J. H. B. Kemperman; A. Kuba
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 120 KB
- Volume
- 9
- Category
- Article
- ISSN
- 0899-9457
No coin nor oath required. For personal study only.
β¦ Synopsis
A matrix is said to be two-valued if its elements assume unknown subset S of Q from the following two projections of its at most two different values. We studied the problem of recondensity function g(i, j) on M 1 N: structing a two-valued matrix from its marginals-that is, from its row sums and column sums-but without any knowledge of the value pair on hand. Provided at least one of these marginals is nonconstant,
only finitely many (though possibly many) value pairs can lead to a valid reconstruction. Our considerations lead to an efficient algorithm
(
for calculating all possible solutions, each with its own value pair. Special attention is given to uniqueness pairs-that is, value pairs to which there corresponds precisely one matrix having the correct Here, marginals. Unless both marginals are constant, there can be no more than two uniqueness pairs.
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