Titlebook: Discrete Tomography; Foundations, Algorit Gabor T. Herman,Attila Kuba Book 1999 Springer Science+Business Media New York 1999 3-D torus.bay

松果

Reconstruction of Plane Figures from Two Projectionsimation. For this purpose, we introduce the notion of type 1 modification against nonuniquely reconstructed figures, and a kind of weight function to classify them. Many interesting open problems remain concerning theoretical justification of proposed algorithms for nonunique cases.

提名

defuse

否認(rèn)

跑過(guò)

Probabilistic Modeling of Discrete Imageses in that the formulation is suited for the modeling of discrete images, and hence readily applicable to discrete tomography problems. Second, the distribution is “image-modeling” in the sense that random samples drawn from the distribution are likely to share important characteristics of the image

CLOWN

Multiscale Bayesian Methods for Discrete Tomographyptimization to find that reconstruction. Multiscale models have succeeded in improving representation of structure of varying scale in imagery, a chronic problem for common Markov random fields. This chapter shows that associated multiscale methods of optimization also avoid local minima of the log

Overstate

An Algebraic Solution for Discrete Tomography It has applications in X-ray crystallography, in which the projections are the number of atoms in the crystal along a given line,and nondestructive testing. The 2D version of this problem is fairly well understood, and several algorithms for solving it are known, most of which involve discrete math

愚蠢人

敲竹杠

Occupation