To avoid grabbing the unintentional user motion in a video sequence, video stabilization techniques are used to obtain better-looking video for the final user. We present a low power rototranslational solution, extending our previous work specifically addressed for translational motion only. The proposed technique achieves a high degree of robustness with respect to common difficult conditions like noise perturbations, illumination changes, and motion blurring. Moreover, it is also able to cope with regular patterns, moving objects and it is very precise, reaching about 7% of improvement in jitter attenuation, compared to previous results. Overall performances are competitive also in terms of computational cost: it runs at more than 30 frames / s with VGA sequences, with a CPU ARM926EJ-S at just 100 MHz clock frequency.
The first results concerning the possibility to use Delayed Luminescence spectroscopy to evaluate the in vitro induction of cytotoxic effects on human glioblastoma cells of nanostructured lipid carrier and drug-loaded nanostructured lipid carrier are showed in this contribution. We tested the effects of nanostructured lipid carrier, ferulic acid and ferulic acidloaded nanostructured lipid carrier on U-87MG cell line. The study seems to confirm the ability of Delayed Luminescence to be sensible indicator of alterations induced on functionality of the mitochondrial respiratory chain complex I in U-87MG cancer cells when treated with nanostructured lipid carriers.
The output quality of an image filter for reducing noise without damaging the underlying signal, strongly depends on the
accuracy of the noise model in characterizing the noise introduced by the acquisition device. In this paper we provide
a solution for characterizing signal dependent noise injected at shot time by the image sensor. Different fitting models
describing the behavior of noise samples are analyzed, with the aim of finding a model that offers the most accurate
coverage of the sensor noise under any of its operating conditions. The noise fitting equation minimizing the residual error
is then identified. Moreover, a novel algorithm able to obtain the noise profile of a generic image sensor without the need of
a controlled environment is proposed. Starting from a set of heterogeneous CFA images, by using a voting based estimator,
the parameters of the noise model are estimated.
Computer Vision enables mobile devices to extract the meaning of the observed scene from the information acquired with
the onboard sensor cameras. Nowadays, there is a growing interest in Computer Vision algorithms able to work on mobile
platform (e.g., phone camera, point-and-shot-camera, etc.). Indeed, bringing Computer Vision capabilities on mobile
devices open new opportunities in different application contexts. The implementation of vision algorithms on mobile
devices is still a challenging task since these devices have poor image sensors and optics as well as limited processing
power. In this paper we have considered different algorithms covering classic Computer Vision tasks: keypoint extraction,
face detection, image segmentation. Several tests have been done to compare the performances of the involved mobile
platforms: Nokia N900, LG Optimus One, Samsung Galaxy SII.
Digital video stabilization allows to acquire video sequences without disturbing jerkiness by removing from the image
sequence the effects caused by unwanted camera movements. One of the bottlenecks of these approaches is the local motion
estimation step. In this paper we propose a Block Selector able to speed-up the block matching based video stabilization
techniques without considerably degrading the stabilization performances. Both history and random criteria are taken into
account in the selection process. Experiments on real cases confirm the effectiveness of the proposed approach even in
In this paper we present a technique which infers interframe motion by tracking SIFT features through consecutive frames:
feature points are detected and their stability is evaluated through a combination of geometric error measures and fuzzy
logic modelling. Our algorithm does not depend on the point detector adopted prior to SIFT descriptor creation: therefore
performance have been evaluated against a wide set of point detection algorithms, in order to investigate how to increase
stabilization quality with an appropriate detector.