ABSTRACT
The usual propagation transform of diffraction tomography is generalized into higher-order (nonlinear) propagation transforms via use of the Born series as the data-generating model in scattering experiments. Nonlinear tomographic reconstruction algorithms are developed for inversion of scattered field data modeled up to an arbitrarily large (possibly infinite) number of terms in the Born series. A computer simulation study is included to illustrate the performance of the algorithms for the case of scattering objects with cylindrical symmetry.
ABSTRACT
We present a time domain algorithm for computation of the maximum likelihood estimate of the location of a known scattering object from wide-band scattering data acquired in a suite of scattering experiments. The algorithm consists of a three-step procedure: (1) data filtering, (2) time-domain backpropagation, and (3) coherent summation and is implemented via a number of forward and inverse Radon transforms integrated into a tomographic scheme. A computer simulation is included for illustration purposes.
ABSTRACT
We previously formulated a new approach for computing invariant features from infrared (IR) images. That approach is unique in the field since it considers not just surface reflection and surface geometry in the specification of invariant features, but it also takes into account internal object composition and thermal state that affect images sensed in the nonvisible spectrum. In this paper, we extend the thermophysical algebraic invariance (TAI) formulation for the interpretation of uncalibrated infrared imagery and further reduce the information that is required to be known about the environment. Features are defined such that they are functions of only the thermophysical properties of the imaged objects. In addition, we show that the distribution of the TAI features can be accurately modeled by symmetric alpha-stable models. This approach is shown to yield robust classifier performance. Results on ground truth data and real infrared imagery are presented. The application of this scheme for site change detection is discussed.
ABSTRACT
A recently proposed approach to the inverse problem of detecting the presence and estimating the location of a known object from data collected in a set of diffraction tomographic experiments is evaluated. Experimental data are used to validate of the filtered backpropagation algorithms used, and their robustness to modeling errors and to severe limitations in the angular coverage of the tomographic data is demonstrated. A potential application to medical imaging of soft tissue is illustrated.
