An Automated Method for Tracking Clouds in Planetary Atmospheres

We present an automated method for cloud tracking which can be applied to planetary images. The method is based on a digital correlator which compares two or more consecutive images and identifies patterns by maximizing correlations between image blocks. This approach bypasses the problem of feature detection. Four variations of the algorithm are tested on real cloud images of Jupiter’s white ovals from the Galileo mission, previously analyzed in Vasavada et al. [Vasavada, A.R., Ingersoll, A.P., Banfield, D., Bell, M., Gierasch, P.J., Belton, M.J.S., Orton, G.S., Klaasen, K.P., Dejong, E., Breneman, H.H., Jones, T.J., Kaufman, J.M., Magee, K.P., Senske, D.A. 1998. Galileo imaging of Jupiter’s atmosphere: the great red spot, equatorial region, and white ovals. Icarus, 135, 265, doi:10.1006/icar.1998.5984]. Direct correlation, using the sum of squared differences between image radiances as a distance estimator (baseline case), yields displacement vectors very similar to this previous analysis. Combining this distance estimator with the method of order ranks results in a technique which is more robust in the presence of outliers and noise and of better quality. Finally, we introduce a distance metric which, combined with order ranks, provides results of similar quality to the baseline case and is faster. The new approach can be applied to data from a number of space-based imaging instruments with a non-negligible gain in computing time

Dense Motion Estimation for Smoke

Motion estimation for highly dynamic phenomena such as smoke is an open challenge for Computer Vision. Traditional dense motion estimation algorithms have difficulties with non-rigid and large motions, both of which are frequently observed in smoke motion. We propose an algorithm for dense motion estimation of smoke. Our algorithm is robust, fast, and has better performance over different types of smoke compared to other dense motion estimation algorithms, including state of the art and neural network approaches. The key to our contribution is to use skeletal flow, without explicit point matching, to provide a sparse flow. This sparse flow is upgraded to a dense flow. In this paper we describe our algorithm in greater detail, and provide experimental evidence to support our claims.Comment: ACCV201

Coastal wave measurements during passage of tropical storm Amy

Aerial photographic and laser profilometer data of waves generated by tropical storm Amy are presented. The data mission consisted primarily of two legs, one in the direction of the wind waves, and the second along the direction of swell propagation, using Jennette's Pier at Nags Head, North Carolina, as a focal point. At flight time, Amy's center was 512 nmi from shore and had maximum winds of 60 knots. The storm's history is presented, along with a satellite photograph, showing the extent of the storm on the day of the flight. Flight ground tracks are presented along with sample aerial photographs of the wave conditions showing approximate wavelength and direction. Sample wave energy spectra are presented both from the laser profilometer onboard the aircraft, and from the Corps of Engineers Research Center (CERC) shore gauge at Nags Head, North Carolina

A Fourier approach to cloud motion estimation

A Fourier technique is described for estimating cloud motion from pairs of pictures using the phase of the cross spectral density. The method allows motion estimates to be made for individual spatial frequencies, which are related to cloud pattern dimensions. Results obtained are presented and compared with the results of a Fourier domain cross correlation scheme. Using both artificial and real cloud data show that the technique is relatively sensitive to the presence of mixtures of motions, changes in cloud shape, and edge effects

The survey of the Basilica di Collemaggio in L’Aquila with a system of terrestrial imaging and most proven techniques

The proposed job concerns the evaluation of a series of surveys carried out in the context of a campaign of studies begun in 2015 with the objective of comparing the accuracies obtainable with the systems of terrestrial imaging, compared to unmanned aerial vehicle imaging and laser scanner survey. In particular, the authors want to test the applicability of a system of imaging rover (IR), an innovative terrestrial imaging system, that consists of a multi-camera with integrated global positioning system (GPS)/global navigation satellite system (GNSS) receiver, that is very recently released technique, and only a few literature references exist on the specific subject. In detail, the IR consists of a total of 12 calibrated cameras – seven “panorama” and five downward-looking – providing complete site documentation that can potentially be used to make photogrammetric measurements. The data acquired in this experimentation were then elaborated with various software packages in order to obtain point clouds and a three-dimensional model in different cases, and a comparison of the various results obtained was carried out. Following, the case study of the Basilica di Santa Maria di Collemaggio in L’Aquila is reported; Collemaggio is an UNESCO world heritage site; it was damaged during the seismic event of 2009, and its restoration is still in progress