Giacomo Boracchi

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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)
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)
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)
Designing tests able to effectively detect changes in the stationarity of a process generating data is a challenging problem, in particular when the process is unknown, and the only information available has to be extracted from a set of observations. This work proposes a novel approach for detecting changes in a process generating data whose distribution(More)
Nonlocal image filters suppress noise and other distortions by searching for similar patches at different 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(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)
Classification systems designed to work in nonstationary conditions rely on the ability to track the monitored process by detecting possible changes and adapting their knowledge-base accordingly. Adaptive classifiers present in the literature are effective in handling abrupt concept drifts (i.e., sudden variations), but, unfortunately, they are not able to(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)
Data streams from remote monitoring systems such as wireless sensor networks show immediately that the “you sample you get” statement is not always true. Not rarely, the data stream is interrupted by intermittent communication or sensors faults, resulting in missing data in the received sequence. This has a negative impact in many algorithms(More)
Assessing the quality of images acquired by nodes of a wireless multimedia sensor network (WMSN) is a critical issue, particularly in outdoor applications where external disturbances such as the presence of water, dust, snow, or tampering on the camera lens may seriously corrupt the acquired images. In this paper, we address the problem of determining when(More)