Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 2014

Powiadomienia systemowe

Sesja wygasła!
Sesja wygasła!
Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: prior knowledge

Sortuj według:

Ogranicz wyniki do:

Estymacja wartości parametrów sygnału na podstawie dostępnej wiedzy

Smołalski G.

Pomiary Automatyka Kontrola

2014

R. 60, nr 12

1140--1143

Jako menzurand przyjęto jeden z możliwych parametrów segmentu sygnału. Przedstawiono metodę jednolitego uwzględniania wszelkich dostępnych informacji o sygnale. Wykorzystano reprezentację sygnału w skończenie-wymiarowej przestrzeni liczbowej. Rozważono przypadek ogólny, gdy parametr mierzony nie jest równy parametrowi estymowanemu. Przeanalizowano użyteczność danej informacji o sygnale w estymacji określonego jego parametru. Przedstawiono przykład ilustrujący stosowanie zaproponowanej metody.

In this paper a measurand is assumed to be some particular parameter of the signal segment - the estimated parameter E (Section 2). A model of measurement is formulated in which all the information items available about the investigated signal (both those available a priori and those provided by measurement) are uniformly taken into account during the evaluation of the uncertainty of E. All information items are assumed to be certain. It is shown (Section 3) that the investigated signal segment can be interpreted as a point in a finite-dimensional numerical space. With each available information item corresponds in that space a specific constraint (1) of possible signals. Finding out the global extremes of the estimated parameter over the resulting set of possible signals, gives the prior uncertainty interval (3) according to E (Section 4). Measurement of some additional parameter M continues restricting the prior set of possible signals. The uncertainty (4), remaining after the measurement of M, results from a structural discrepancy between parameters E and M. One of measurability necessary conditions is presented in Section 6. It enables an easy rejection of such measured parameters M that cannot be useful for the estimation of E. Finally, an illustrative example for the method has been provided in Section 7.

Aligning Projection Images from Binary Volumes

Bleichrodt F., de Beenhouwer J., Sijbers J., Batenburg K. J.

Fundamenta Informaticae

2014

Vol. 135, nr 1/2

21--42

In tomography, slight differences between the geometry of the scanner hardware and the geometric model used in the reconstruction lead to alignment artifacts. To exploit high-resolution detectors used in many applications of tomography, alignment of the projection data is essential. Markerless alignment algorithms are the preferred choice over alignment with markers, in case a fully automatic tomography pipeline is required. Moreover, marker based alignment is often not feasible or even possible. At the same time, markerless alignment methods often fail in scenarios where only a small number of projections are available. In this case, the angular separation between projection images is large and therefore the correlation between them is low. This is a property that most markerless algorithms rely on. The intermediate reconstruction problem of alignment by projection matching is highly underdetermined in the limited data case. Therefore, we propose a projection matching method that incorporates prior knowledge of the ground truth. We focus on reconstructing binary volumes. A discrete tomography algorithm is employed to generate intermediate reconstructions. This type of reconstruction algorithm does not rely heavily on correlated projection images. Our numerical results suggest that alignment using discrete tomography projection matching produces much better results in the limited angle case, when compared to a projection matching method that employs an algebraic reconstruction method.