in: Signal Processing. See also BIBT E X entry below.
BIBT E X:@article{AAC93a, author = {Til Aach and Andr\'e Kaup and Rudolf Mester}, title = {Statistical Model-Based Change Detection in Moving Video}, journal = {Signal Processing}, publisher = {Elsevier}, volume = {31}, number = {2}, year = {1993}, pages = {165--180}}This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by the authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder. Abstract. A major issue with change detection in video sequences is to guarantee robust detection results in the presence of noise. In this contribution, we first compare different test statistics in this respect. The distributions of these statistics for the null hypothesis are given, so that significance tests can be carried out. An objective comparison between the different statistics can thus be based on identical false alarm rates. However, it will also be pointed out that the global thresholding methods resulting from the significance approach exhibit certain weaknesses. Their shortcomings can be overcome by the Markov random field based refining method derived in the second part ofthis paper. This method serves three purposes: it accurately locates boundaries between changed and unchanged areas, it brings to bear a regularizing effect on these boundaries in order to smooth them, and it eliminates small regions if the original data permits this.Zusammenfassung. Ein wichtiger punkt bei der Anderungsdetektion in Videosequenzen ist die Robustheit der Detektionsresultate gegenüber Rauschen. In diesem Beitrag werden zuerst verschiedene Teststatistiken in dieser Hinsicht miteinander verglichen. Die Verteilungen dieser Teststatistiken bei gegebener Nullhypothese werden angegeben, so daß Signifikanztests durchgeführt werden können. Ein objektiver Vergleich der verschiedenen Teststatistiken kann dann auf der Basis gleicher Fehldetektionsraten vorgenommen werden. Es werden aber auch einige Unzulänglichkeiten der aus dem Signifikanzansatz resultierenden globalen Schwellwertmethode aufgezeigt. Diese Schwächen können durch die im zweiten Teil beschriebene, auf Markov-Zufallsfeldern basierende Methode zur Verfeinerung von Anderungsmasken ausgeglichen werden. Diese Methode verfolgt drei Ziele: die Grenzen zwischen geänderten und ungeänderten Regionen werden genau lokalisiert, durch Regulari sierung werden die Grenzen gegebenenfalls geglättet, und kleine Regionen werden, falls sie durch Fehldetektionen zustandegekommen sind, entfernt.
Even though image signals are typically defined on a regular 2D grid, there also exist many scenarios where this is not the case and the amplitude of the image signal only is available for a non-regular subset of pixel positions. In such a case, a resampling of the image to a regular grid has to be carried out. This is necessary since almost all algorithms and technologies for processing, transmitting or displaying image signals rely on the samples being available on a regular grid. Thus, it is of great importance to reconstruct the image on this regular grid, so that the reconstruction comes closest to the case that the signal has been originally acquired on the regular grid. In this paper, Frequency Selective Reconstruction is introduced for solving this challenging task. This algorithm reconstructs image signals by exploiting the property that small areas of images can be represented sparsely in the Fourier domain. By further considering the basic properties of the optical transfer function of imaging systems, a sparse model of the signal is iteratively generated. In doing so, the proposed algorithm is able to achieve a very high reconstruction quality, in terms of peak signal-to-noise ratio (PSNR) and structural similarity measure as well as in terms of visual quality. The simulation results show that the proposed algorithm is able to outperform state-of-the-art reconstruction algorithms and gains of more than 1 dB PSNR are possible.
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