Tracking was generally good, but objects occasionally shifted or jittered.
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The car bounced and jittered along the road, sending pale dust and pebbles flying amid scruffs of agave and prickly pear.
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A thin folder jittered in his hand, its transparency revealing a document whose tidy bullet points and blurbs belied the man's unkempt appearance.
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As he spoke, his leg jittered like a sewing-machine needle, and I got the impression that sitting still was torture for him.
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He struck poses with every muscle clenched, he jittered across the stage, he repeatedly kicked around his microphone stand and a stool he barely used.
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Reporters on the plane, anxious for any developments, jittered at the mere site of a Clinton aide headed toward the press, and when Jennifer Palmieri, Clinton's spokeswoman, walked toward the back of the plane, the press cabin jerked with energy.
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The line for mezcal jittered along the x-axis, jumping up dramatically every inch or two—the chemical profile of mezcal can include furfural, which carries hints of bread, nuts, and caramel, and napthalene, a hydrocarbon that lends a note of tar.
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It's not that the constant churn somehow instantly absolves all the back-rimmed free throws and palpably jittered players and long stretches of clammy pack-zone boredom; even if you are inclined to forgive all that, it is still only precisely what it is.
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Category 5 Hurricanes showing the relationship between wind speed (X) and pressure (Y). The blue and green lines represent the means of the X and Y values, respectively. The Quadrants have been labeled. The points have been jittered to reduce overlap of observations. The scatterplot shows the maximum wind speed (X) and minimum pressure (Y) for 35 Category 5 Hurricanes.
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In a typical efMRI, after every trial the hemodynamic response is allowed to return to baseline. In rapid event-related fMRI, trials are randomized and the HRF is deconvolved afterwards. In order for this to be possible, every possible combination of trial sequences must be used and the inter-trial intervals jittered so that the time in between trials is not always the same.
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With staggered PRF the radar's own targets appear stable in range in relation to the transmit pulse, whilst the 'jamming' echoes may move around in apparent range (uncorrelated), causing them to be rejected by the receiver. Staggered PRF is only one of several similar techniques used for this, including jittered PRF (where the pulse timing is varied in a less-predictable manner), pulse-frequency modulation, and several other similar techniques whose principal purpose is to reduce the probability of unintentional synchronicity. These techniques are in widespread use in marine safety and navigation radars, by far the most numerous radars on planet Earth today.
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In this way a determination of visibility to the light and therefore illumination by the light can be made for the rendered pixel. So this texturing operation is a boolean test of whether the pixel is lit, however multiple samples can be tested for a given pixel and the boolean results summed and averaged. In this way in combination with varying parameters like sampled texel location and even jittered depth map projection location a post-depth-comparison average or percentage of samples closer and therefore illuminated can be computed for a pixel. Critically, the summation of boolean results and generation of a percentage value must be performed after the depth comparison of projective depth and sample fetch, so this depth comparison becomes an integral part of the texture filter.
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The resulting end-to-end system achieves a drastic improvement in the state of the art on this end-to-end task, reaching the same level of performance as the best systems operating on aligned, closely cropped images (no outside training data). It also performs well on two newer datasets, similar to LFW, but more difficult: significantly jittered (misaligned) version of LFW and SUFR-W (for example, the model's accuracy in the LFW "unaligned & no outside data used" category is 87.55±1.41% compared to state-of-the-art APEM (adaptive probabilistic elastic matching): 81.70±1.78%). The theory was also applied to a range of recognition tasks: from invariant single object recognition in clutter to multiclass categorization problems on publicly available data sets (CalTech5, CalTech101, MIT-CBCL) and complex (street) scene understanding tasks that requires the recognition of both shape-based as well as texture- based objects (on StreetScenes data set). The approach performs really well: It has the capability of learning from only a few training examples and was shown to outperform several more complex state-of-the-art systems constellation models, the hierarchical SVM-based face-detection system.
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