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<div>Figure 13. Eulerian video magnification of registered and segmented FA frames (A) without mean intensity projection and (B) with mean intensity projection of masks. See Supplementary Videos for the frames shown here.</div><div></div><div></div><div></div><div></div><div></div><div>eulerian video magnification software download</div><div></div><div>DOWNLOAD: https://t.co/6h3YaYAqoE </div><div></div><div></div><div>Introduction: Eulerian magnification amplifies very small movements in video, revealing otherwise invisible motion. This raises the possibility that it could enable clinician visualisation of subclinical tremor using a standard camera. We tested whether Eulerian magnification of apparently atremulous hands reveals a Parkinsonian tremor more frequently in Parkinson's than in controls.</div><div></div><div></div><div>Method: We applied Eulerian magnification to smartphone video of 48 hands that appeared atremulous during recording (22 hands from 11 control participants, 26 hands from 17 idiopathic Parkinson's participants). Videos were rated for Parkinsonian tremor appearance (yes/no) before and after Eulerian magnification by three movement disorder specialist neurologists.</div><div></div><div></div><div>Results: The proportion of hands correctly classified as Parkinsonian or not by clinicians was significantly higher after Eulerian magnification (OR = 2.67; CI = [1.39, 5.17]; p </div><div></div><div></div><div>Conclusion: Eulerian magnification slightly improves clinician ability to identify apparently atremulous hands as Parkinsonian. This suggests that some of the apparent tremor revealed may be subclinical Parkinson's (pathological) tremor, and Eulerian magnification may represent a first step towards contactless visualisation of such tremor. However, the technique also reveals apparent tremor in control hands. Therefore, our method needs additional elaboration and would not be of direct clinical use in its current iteration.</div><div></div><div></div><div>The most basic version of our processing looks at intensity variations over time at each pixel and amplifies them. This simple processing reveals both subtle color variations and small motions because, for small sub-pixel motions or large structures, motion is linearly related to intensity change through a first-order Taylor series expansion (Section 2). This approach to motion magnification breaks down when the amplification factor is large and the Taylor approximation is no longer accurate. Thus, for most motion magnification applications we develop a different approach, transforming the image into a complex steerable pyramid, in which position is explicitly represented by the phase of spatially localized sinusoids. We exaggerate the phase variations observed over time, modifying the coefficients of the pyramid representation. Then, the pyramid representation is collapsed to produce the frames of a new video sequence that shows amplified versions of the small motions (Section 3). Both Eulerian approaches lead to faster processing and fewer artifacts than the previous Lagrangian approach. However, the Eulerian approaches only work well for small motions, not arbitrary ones.</div><div></div><div></div><div>Making small color changes and motions visible adds a dimension to the analysis that goes beyond simply measuring color and position changes. The visualization lets a viewer interpret the small changes, and find patterns that simply measuring numbers would not reveal. It builds intuition and understanding of the motions and changes being revealed. We show results of Eulerian video magnification in a wide variety of fields, from medicine and civil engineering to analyzing subtle vibrations due to sound. Videos and all of our results are available on our project webpage ( ).</div><div></div><div></div><div>The core idea of Eulerian video magnification is to independently process the time series of color values at each pixel. We do this by applying standard 1D temporal signal processing to each time series to amplify a band of interesting temporal frequencies, for example, around 1 Hz (60 beats per minute) for color changes and motions related to heart-rate. The new resulting time series at each pixel yield an output video where tiny changes that were impossible to see in the input, such as the reddening of a person's face with each heart beat or the subtle breathing motion of a baby, are magnified and become clearly visible.</div><div></div><div></div><div>By the same analysis as before, this is approximately equal to a new video in which the variations in ++ are larger by a factor 1 + +#. This shows that linear Eulerian video magnification can be used to magnify many subtle, temporal phenomena. It is agnostic to the underlying imaging model and can even work in cases where brightness constancy is not true as long as the changes are small.</div><div></div><div></div><div>Phase-based magnification works perfectly in this case because the motions are global and because the transform breaks the image into a representation consisting of exact sinusoids (formally, the Fourier transform diagonalizes the translation operator). In most cases, however, the motions are not global, but local. This is why we break the image into local sinusoids using the complex steerable pyramid.</div><div></div><div></div><div>Temporal narrowband linear filters provide a good way to improve signal-to-noise ratios for motions that occur in a narrow range of frequencies, such as respiration and vibrations. To prevent phase-wrapping issues when using these filters, we first unwrap the phases in time. The filters can also be used to isolate motions in an object that correspond to different frequencies. For example, a pipe vibrates at a preferred set of modal frequencies, each of which has a different spatial pattern of vibration. We can use video magnification to reveal these spatial patterns by amplifying the motions only corresponding to a range of temporal frequencies. A single frame from each motion magnified video is shown in Figure 8, along with the theoretically expected shape.21</div><div></div><div></div><div>where K-U is a Gaussian convolution kernel given by exp . The indices of A and -a have been suppressed for readability. We applied this processing to all of our motion magnification videos with -U equal to 2 pixels in each pyramid level.</div><div></div><div></div><div>The world is full of subtle changes that are invisible to the naked eye. Video magnification allows us to reveal these changes by magnifying them. We present a selection of our magnification results and extensions of our techniques by us and other authors below.</div><div></div><div></div><div>Video magnification has also contributed to new scientific discoveries in biology. Sellon et al. magnified the subtle motions of an in-vitro mammalian tectorial membrane,18 a thin structure in the inner ear. This helped explain this membrane's role in frequency selectivity during hearing.</div><div></div><div></div><div>One interesting source of small motions is sound. When sound hits an object, it causes that object to vibrate. These vibrations are normally too subtle and too fast to be seen, but we can sometimes reveal them in motion magnified, high-speed videos of the object (Figure 9a). This shows that sound can produce a visual motion signal. Video magnification gives us a way to visualize this signal, but we can also quantitatively analyze it to recover sound from silent videos of the objects (Figure 9b). For example, we can recover intelligible speech and music from high-speed videos of a vibrating potato chip bag or houseplant (Figures 1 and 10). We call this technique The Visual Microphone.</div><div></div><div></div><div>The Eulerian approach to motion magnification is robust and fast, but works primarily when the motions are small. If the motions are large, this processing can introduce artifacts. However, one can detect when this happens and suppress magnification in this case.22 Elgharib et al.8 also demonstrate it is possible to magnify tiny motions in the presence of large ones by first stabilizing the video. There are limits to how well spatio-temporal filtering can remove noise and amplified noise can cause image structures to move incoherently.</div><div></div><div></div><div>Eulerian video magnification is a set of simple and robust algorithms that can reveal and analyze tiny motions. It is a new type of microscope, not made of optics, but of software taking an ordinary video as input and producing one in which the temporal changes are larger. It reveals a new world of tiny motions and color changes showing us hidden vital signs, building movements and vibrations due to sound waves. Our visualization may have applications in a variety of fields such as healthcare, biology, mechanical engineering, and civil engineering.</div><div></div><div></div><div>In this study, we propose a noninvasive and easily scalable alternative to current invasive remote pressure monitoring systems by combining the bedside examination with modern image processing techniques. Eulerian video magnification is an image processing method by which visually imperceptible periodic motions can be deconstructed and amplified into movements discernible to the naked eye.13 We describe the application of Eulerian video magnification to the jugular venous pulse examination and demonstrate its potential as a novel method of noninvasive monitoring of right-sided filling pressures.</div><div></div><div></div><div>The AMPLIFY pilot study suggests that computerized motion amplification can improve the accuracy of clinical bedside JVP measurement. Historically, studies of the bedside exam have reported variable accuracy, and some suggest that it tends to underestimate the central venous or right atrial pressure.14,15 Our data confirm this tendency. Furthermore, our data suggest that the bedside exam is inaccurate at characterizing right-sided filling pressures: 27% of characterizations made at the bedside by two independent cardiologists were completely erroneous, which may reflect the high proportion of obesity in this study. Despite the challenging population, motion magnification resulted in less central venous pressure discrepancy with right heart catheterization as compared to bedside or unamplified video assessments. Perhaps more relevantly, it reduced significant categorical disagreements with right heart catheterization compared to the other noninvasive modalities.</div><div></div><div> 31c5a71286</div>
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