By Michael C. Curtis, Victoria J. D. Sivess (auth.), Dimitris Christodoulakis (eds.)
The annual Ada-Europe convention is an important occasion in Europe for the promoting and improvement of the Ada programming language and of Ada-based software program engineering method. a complete of 25 papers have been chosen for the 1991 convention. Technical excellence and originality have been the central choice standards, yet clarity and relevance to the convention topic have been additionally taken into consideration. The papers have been equipped into 8 sections, on language concerns, metrics, concurrency and parallelism in Ada, non-stop structures, environments and instruments, distribution, faces of reuse, and venture reports.
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Extra info for Ada: The Choice for '92: Ada-Europe International Conference Athens, Greece, May 13–17, 1991 Proceedings
In the transformation domain disks yield impulse responses. This transformation can be viewed as a spectral domain, with somewhat similar intuition of classical Fourier analysis. A simple reconstruction formula from the TV spectral domain to the spatial domain is given. We can then design low-pass, high-pass and band-pass TV ﬁlters and obtain a TV spectrum of signals and images. Keywords: Scale Space, total variation, image analysis. 1 Introduction The total variation (TV) functional is today a fundamental regularizing tool in image processing.
IJCV 63(1), 85–104 (2005) 46 G. Gilboa 4. : Structure-texture image decomposition – modeling, algorithms, and parameter selection. International Journal of Computer Vision 67(1), 111–136 (2006) 5. : Discrete total variation ﬂows without regularization. 1137 (2012) 6. : The total variation ﬂow in RN . Journal of Diﬀerential Equations 184(2), 475–525 (2002) 7. : Cartoon extraction based on anisotropic image classiﬁcation. In: Vision, Modeling, and Visualization Proceedings, pp. 293–300 (2006) 8. : High accuracy optical ﬂow estimation based on a theory for warping.
They show that its minimization is connected to tensor-based anisotropic diffusions. In this work, to overcome the limitations of TV we adopt more sophisticated descriptors of image variations that generalize the gradient magnitude. We build upon the work of  and propose a generic convex energy functional that is based on the eigenvalues of the structure tensor. However, the current work departs from  in several ways. First, we provide more intuition about why the usage of the structure tensor’s eigenvalues leads to effective generalizations of Total Variation.