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We propose a powerful video filtering algorithm that exploits temporal and spatial redundancy characterizing natural video sequences. The algorithm implements the paradigm of nonlocal grouping and collaborative filtering, where a higher dimensional transform-domain representation of the observations is leveraged to enforce sparsity, and thus regularize the(More)
Nonlocal image …lters suppress noise and other distortions by searching for similar patches at di¤erent locations within the image, thus exploiting the self-similarity present in natural images. This similarity is typically assessed by a windowed distance of the patches pixels. Inspired by the human visual system, we introduce a patch foveation operator and(More)
Time-series data are often characterized by a large degree of self-similarity, which arises in application domains featuring periodicity or seasonality. While self-similarity has shown to be an effective prior for modeling real data in the signal and image-processing literature, it has received much less attention in time-series literature, where only few(More)
When dealing with motion blur, there is an inevitable tradeoff between the amount of blur and the amount of noise in the acquired images. The effectiveness of any restoration algorithm typically depends on these amounts, and it is difficult to find their best balance in order to ease the restoration task. To face this problem, we provide a methodology for(More)
The pervasiveness of mobile devices increases the risk of exposing sensitive information on the go. In this paper, we arise this concern by presenting an automatic attack against modern touchscreen keyboards. We demonstrate the attack against the Apple iPhone — 2010's most popular touchscreen device — although it can be adapted to other(More)
We present a single-image algorithm for reconstructing the 3D velocity, the 3D spin axis, and the angular speed of a moving ball. Peculiarity of the proposed algorithm is that this reconstruction is achieved by accurately analyzing the blur produced by the ball motion during the exposure. We combine image analysis techniques in order to obtain 3D estimates,(More)
Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over time by exploiting additional supervised information coming from the field. In nonstationary conditions, however, the classifier reacts as soon as concept drift is(More)
Classification systems meant to operate in nonstationary environments are requested to adapt when the process generating the observed data changes. A straightforward form of adaptation implementing the instance selection approach suggests releasing the obsolete data onto which the classifier is configured by replacing it with novel samples before(More)
Design of applications working in nonstationary environments requires the ability to detect and anticipate possible behavioral changes affecting the system under investigation. In this direction, the literature provides several tests aiming at assessing the stationarity of a data generating process; of particular interest are nonparametric sequential(More)
A classifier expected to work in a non-stationary environment has to: (i) detect changes in the process generating the data; (ii) suitably react to the change by adapting to the new working condition. Just-In-Time Adaptive classifiers, a classification structure addressing stationary and nonstationary conditions, have been recently presented to the(More)