Serwis Infona wykorzystuje pliki cookies (ciasteczka). Są to wartości tekstowe, zapamiętywane przez przeglądarkę na urządzeniu użytkownika. Nasz serwis ma dostęp do tych wartości oraz wykorzystuje je do zapamiętania danych dotyczących użytkownika, takich jak np. ustawienia (typu widok ekranu, wybór języka interfejsu), zapamiętanie zalogowania. Korzystanie z serwisu Infona oznacza zgodę na zapis informacji i ich wykorzystanie dla celów korzytania z serwisu. Więcej informacji można znaleźć w Polityce prywatności oraz Regulaminie serwisu. Zamknięcie tego okienka potwierdza zapoznanie się z informacją o plikach cookies, akceptację polityki prywatności i regulaminu oraz sposobu wykorzystywania plików cookies w serwisie. Możesz zmienić ustawienia obsługi cookies w swojej przeglądarce.
In this paper, we present a method for microcalcification detection on filtered back-projection reconstructed slices of breasts acquired on a Digital Breast Tomosynthesis system. Performance of the algorithm has been evaluated on a ground-truth database composed of 50 cases (13 lesions, 37 normals). Since the method is derived from an algorithm developed to detect microcalcifications on 2D digital...
We present a novel algorithm — ProSparse Denoise — that can solve the sparsity recovery problem in the presence of noise when the dictionary is the union of Fourier and identity matrices. The algorithm is based on a proper use of Cadzow routine and Prony's method and exploits the duality of Fourier and identity matrices. The algorithm has low complexity compared to state of the art algorithms for...
Fast and accurate detection of action potentials from neurophysiological data is key to the study of information processing in the nervous system. Previous work has shown that finite rate of innovation (FRI) theory can be used to successfully reconstruct spike trains from noisy calcium imaging data. This is due to the fact that calcium imaging data can be modeled as streams of decaying exponentials...
The problem of finding the sparse representation of a signal has attracted a lot of attention over the past years. In particular, uniqueness conditions and reconstruction algorithms have been established by relaxing a non-convex optimisation problem. The finite rate of innovation (FRI) theory is an alternative approach that solves the sparsity problem using algebraic methods based around Prony's algorithm...
The current methods used to convert analogue signals into discrete-time sequences have been deeply influenced by the classical Shannon–Whittaker–Kotelnikov sampling theorem. This approach restricts the class of signals that can be sampled and perfectly reconstructed to bandlimited signals. During the last few years, a new framework has emerged that overcomes these limitations and extends sampling...
Traditional Finite Rate of Innovation (FRI) theory has considered the problem of sampling continuous-time signals. This framework can be naturally extended to the case where the input is a discrete-time signal. Here we present a novel approach which uses both the traditional FRI sampling scheme, based on the annihilating filter method, and the fact that in this new setup the null space of the problem...
The theory of sampling signals with finite rate of innovation (FRI) has shown that it is possible to perfectly recover classes of non-bandlimited signals such as streams of Diracs from uniform samples. Most of previous papers, however, have to some extent only focused on the sampling of periodic or finite duration signals. In this paper we propose a novel method that is able to reconstruct infinite...
In [1,2] we presented a multiview image coding scheme. The approach is based on extracting depth layers from multiview images. Each layer is related to an object in the scene and is highly redundant. We exploit this redundancy by using a properly disparity compensated Wavelet Transform, followed by quantisation and entropy coding of the transform coefficients. In addition, to reconstruct the data...
Podaj zakres dat dla filtrowania wyświetlonych wyników. Możesz podać datę początkową, końcową lub obie daty. Daty możesz wpisać ręcznie lub wybrać za pomocą kalendarza.