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

A Developmental Model of Infantile Nystagmus

The possibility that infantile nystagmus (IN) may reflect a failure in early sensorimotor integration has been proposed for more than a century, but is only recently being borne out in animal studies. The underlying neural and genetic substrate for this plasticity is complex. We propose that, in most cases, IN develops as a developmental response to reduced contrast sensitivity to high-spatial frequencies in an early "critical period," however caused, whether by structural malformations (e.g. foveal hypoplasia) or poor optics (e.g. cataract). As shown by psychophysics, contrast sensitivity to low spatial frequencies is enhanced by motion of the image across the retina. Based on our previous theoretical study (Harris & Berry, Nonlinear Dynamics, 2006), we argue that the best compromise between moving the image and maintaining the image near the fovea (or its remnant) is to oscillate the eyes with jerk nystagmus with increasing velocity waveforms, as seen empirically. The generation of jerk waveforms relies heavily on the saccadic system, which is immature in infancy. Pendular waveforms may therefore provide an alternative to jerk waveforms, and may explain why they are seen more often in young infants. We discuss the implications of this developmental model for the need to synchronize sensory and motor developments in normal development. Failure of this synchronization may also explain some idiopathic cases

A Distal Model of Congenital Nystagmus as Nonlinear Adaptive Oscillations

Congenital nystagmus (CN) is an incurable pathological spontaneous oscillation of the eyes with an onset in the first few months of life. The pathophysiology of CN is mysterious. There is no consistent neurological abnormality, but the majority of patients have a wide range of unrelated congenital visual abnormalities affecting either the cornea, lens, retina or optic nerve. In this theoretical study, we show that these eye oscillations could develop as an adaptive response to maximize visual contrast with poor foveal function in the infant visuomotor system, at a time of peak neural plasticity. We argue that in a visual system with abnormally poor high spatial frequency sensitivity, image contrast is not only maintained by keeping the image on the fovea (or its remnant) but also by some degree of image motion. Using the calculus of variations, we show that the optimal trade-off between these conflicting goals is to generate oscillatory eye movements with increasing velocity waveforms, as seen in real CN. When we include a stochastic component to the start of each epoch (quick-phase inaccuracy) various observed waveforms (including pseudo-cycloid) emerge as optimal strategies. Using the delay embedding technique, we find a low fractional dimension as reported in real data. We further show that, if a velocity command-based pre-motor circuitry (neural integrator) is harnessed to generate these waveforms, the emergence of a null region is inevitable. We conclude that CN could emerge paradoxically as an ‘optimal’ adaptive response in the infant visual system during an early critical period. This can explain why CN does not emerge later in life and why CN is so refractory to treatment. It also implies that any therapeutic intervention would need to be very early in life

Congenital Nystagmus as Non-Linear Adaptive Oscillations

Congenital Nystagmus (CN) is a pathological involuntary oscillation of the eyes with an onset within the first few months of life, with an incidence of about 1:3000. It is a life-long oculomotor disorder that cannot be explained by any underlying neurological abnormality which might compromise adaptive mechanisms. There is no cure, and CN has so far defied explanation in spite of numerous attempts to model the disorder. In this theoretical study we show that these eye oscillations could develop as an adaptive response to maximise visual contrast with poor foveal function in the infant visuomotor system, at a time of peak neural plasticity. We propose that CN is a normal developmental adaptive response to an abnormal congenital sensory input. This can explain why CN does not emerge later in life and why CN is so refractory to treatment. It also implies that any therapeutic intervention would need to be very early in life

A Developmental Model of Congenital Nystagmus

Purpose: Congenital nystagmus (CN) is a spontaneous oscillation of the eyes with an onset in the first few months of life. In 90% of affected children there is an associated underlying sensory defect (foveal hypoplasia, cone dysfunction, cataracts, etc.). In 10% no underlying visual defect can be found, and the nystagmus is labelled as ‘idiopathic’. CN appears to be a developmental anomaly of sensorimotor integration, as it is not have an onset later in infancy or beyond, but why such a wide variety of early onset visual defects should lead to life-long oscillation of the eyes is a mystery. Previous models have focussed on a systems level approach to explain how CN might be generated by known oculomotor circuits. We ask, instead, why CN might occur. Model: Our basic tenet is that infant visuomotor development is highly plastic during some early ‘critical’ period. A defect of foveal vision occurring during (and only during) this period leads to an anomalous connectivity in the oculomotor circuitry, which becomes permanent thereafter. We propose that circuitry normally used for precise foveal registration of a visual object (gaze holding, fixation, and smooth pursuit) develops to maintain some degree of image motion, as this would maximise contrast for a low spatial frequency system. However, this motion is in conflict with maintaining the image on the fovea (or its remnant). We explore the best oculomotor strategy to cope with this conflict. Results: The optimal strategy (in the least squares sense) is to oscillate the eyes in one meridian with alternating slow and quick (saccade) phases. Remarkably, the optimal waveform profile has an increasing-velocity profile. Many of the unique waveforms seen empirically in CN are also optimal strategies given realistic uncertainty in the initial position of a slow phase. Using non-linear dynamical systems analysis, we show that these ‘optimal’ oscillations have similar fractional correlation dimensions to observed data. We also show that a ‘null region’, as commonly observed in CN, would be an inevitable consequence of a velocity driven oculomotor system. Conclusions: We have developed a new approach to understanding oculomotor development, in which we examine the best strategy to maximise visual contrast. In a normal foveate visual system with fine oculomotor control, the best strategy is to develop good foveal registration, which we call ‘fixation’, and ‘smooth pursuit’. If, however, the fovea is absent or not being stimulated (eg. cataracts), the best strategy would be to develop oscillations of the type seen in CN. It implies that the chaotic oscillations are the result of a physiological developmental adaptive process. This is in contrast to the prevailing view that CN is a disease that can be ‘cured’. It is not surprising that CN has proven remarkably refractory to therapeutic intervention with only minimal (if any) long-term successes using drugs, surgery, or even biofeedback. We argue that CN is as adaptive and permanent as normal eye movements are in a normally sighted individual

Evidence for Auto-Correlation and Symmetry Detection in Primary Visual Cortex

The detectability of patterns in random dot arrays was measured as a function of dot density and compared with the statistical limit set by different methods of detecting the pattern. For filtering, cross-correlation, convolution, or template matching, the limit is expected to be inversely proportional to the square root of dot density. But for auto-correlation, which can detect symmetries of various types, the limit is unaffected by dot density under many conditions. Confirming previous results, we found that the coherence-threshold is often constant for Glass patterns, but the range of constancy depends on details of the display procedure. Coherence-thresholds were found to increase when the average number of dots expected at each location rose towards or exceeded a value of one; we therefore think it results from the non-linear effects of occlusion that occur when a later-programmed dot falls in the same location as an earlier one. To test this, these non-linear effects were prevented by arranging the luminance of each location to be directly proportional to the number of times that location was covered by a dot. Millions of dots can be used for these images, and they retain the streakiness of Glass patterns, while discrete dots disappear. The constant coherence threshold for detecting this streakiness is maintained over a huge range of dot densities, extending right down to the range where discrete dots become visible and up to patterns that are essentially full-tone images with no discrete dots. At threshold, all these patterns have similar auto-correlation functions, as we can see from the way both low dot-number Glass-patterns and these mega-dot, multi-tone, Glass-like images are formed. This startling fact raises the question whether primary visual cortex computes auto-correlations as well as, or even instead of, the local, Fourier-type, wavelet analysis of the currently popular paradigm

Evidence for a neural model to evaluate symmetry in V1

50 years ago Hubel and Wiesel discovered simple and complex cells in V1, but there is still no consensus on their functional roles. It is agreed that complex cells are more often selective for direction of motion than simple cells, that there are differences in the way they combine information within their receptive fields, and that complex cells probably receive most of their input from simple cells, but what this serial hierarchy achieves is not understood. There is another puzzling dichotomy that we think is related, namely that of cross-correlation, which is widely accepted as the operation performed on the input image by simple cells, and auto-correlation, which some think underlies the perception of Glass patterns, and possibly motion. We propose the hypothesis that complex cells signal auto-correlations in the visual image, but to evaluate them they require the preliminary analysis done by simple cells, and also pinwheels - structures intervening between simple cells and complex cells that were quite unknown to Hubel and Wiesel. We shall first present psychophysical evidence, using a new kind of random dot display, which suggests that both cross-correlation and auto-correlation are performed in early vision. We then point to recent evidence on the micro-circuitry of pinwheels, and mappings of their intrinsic activity, which shows how pinwheels might enable complex cells to respond selectively to autocorrelations in the input image that activates the simple cells. Auto-correlation is a powerful tool for detecting symmetry, and many may be surprised by evidence that such an abstract property is detected so early in visual perception

Velocities of Venus clouds derived from VIRTIS observations

Retrograde superrotation is a well known feature of the atmosphere of Venus, with Venus’ cloud tops rotating in only 4.4 days, much faster than the 243-day rotation period of the solid globe. A good characterization of the circulation of the venusian atmosphere is essential in order to understand the mechanisms controlling superrota- tion. VIRTIS, onboard ESA’s Venus Express, is one of the most flexible instruments for such a characterization. The VIRTIS-M imaging spectrometer, operating in the range 0.25 to 5 micrometers, has acquired images of Venus’ clouds from the cloud tops, in visible wavelengths, to the lower cloud layer, close to 40 km, at infrared wavelengths. We present velocity determinations from automated cloud tracking in the night side at 1.74, 2.3 and 5 micrometers, from high to mid latitudes in the south- ern hemisphere. The method is based on a digital correlator which compares two or more consecutive images and identifies patterns by maximizing correlations between image blocks (Luz, Berry and Roos-Serote, 2008, New Ast. 13, 224). Notable features are the variability of the winds and the detection of a clear transition region between 75S and 80S. The meridional component is suggestive of a polar Hadley cell. Wave motions are detected at the transition latitudes with wavenumbers 3 and 8 for the zonal and meridional components. We estimate the contribution from the subsolar to antisolar-point wind component to be higher than 10 m/s

A Cloud Tracking Tool for Planetary Orbiter Images

During their operations phase, planetary missions continuously produce a wealth of data that tend to overwhelm research teams. Spectral imagers, in particular, produce data cubes in which the wavelength dimension adds to the two spatial dimensions. Tracking of atmospheric features in order to derive winds and the construction of global maps from such large data volumes becomes particularly time-consuming if done manually. This highlights the importance of automated procedures capable of analysing sequences of data cubes with minimal user interaction. A tool for cloud tracking for such a purpose is currently under development in our group. In its present state it is based on synthetic images and uses a simple method of multiple matrix comparison to derive wind components. Deriving winds from data from the Venus Express - Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS) instrument will be a possible application. We shall present an overview of the method, its benchmarking and the current status and future development of the project

South polar dynamics of the Venusian atmosphere from VIRTIS/Venus Express mapping in the thermal range

We report on measurements of Venus cloud velocities from VIRTIS/Venus Express observations of the south polar region of Venus. Cloud tracking has been performed using a method of automated digital correlation. Tracking has been performed on pairs of monochromatic VIRTIS images selected mainly in the 5 μm window, but also at 1.74, 2.3, 3.93 micrometers. Wind measurements from vector retrievals based on automated feature tracking show high variability, indicating the presence of important transient motions. The time-averaged zonal winds indicate different day and night side regimes. On the day side both the zonal wind component (u) and the meridional one (v) are approximately uniform between 84S and 76S, with u ∼ −40 m/s and v ∼ −10 m/s. On the night side the zonal wind decreases poleward, from a maximum at 76S. The meridional wind is smaller than on the day side and appears to change sign from poleward to equatorward at 76S. The cold collar boundary appears to be a transition region not only for the temperature, but for the winds as well. In this region wave motions are also apparent, with amplitudes on the order of 40 m/s for u′ and 10 m/s for v′