Sentence examples for distributions of image from inspiring English sources

We distinguish between approaches that classify interactions directly from distributions of image features and those that first detect faces or bodies and then classify the interaction.

We therefore assessed whether the cumulative probability distributions of image speeds in Figure 5 accurately predicted the psychophysical functions derived from observers viewing similar motion stimuli (see Figure 3).

The probability distributions of image speeds and projected distances were determined by systematically analyzing the entire image plane of the frustum at 30° increments over 360° with templates configured to correspond with the distances traversed by stimuli in the psychophysical testing (1° 7°; see Figure 2B).

Left: blurred image A. Right: Bottom: Sharp image B. Figure 2 Gradient distributions of images.

So we categorize the distributions of images into symmetric and asymmetric.

Perspective projection also explains why the cumulative probability distributions of image-source relationships have the shape they do, and thus how they influence the percentile rank of motion stimuli (see Figures 5 7).

We elaborately design a novel fuzzy membership function to represent the distribution of image samples.

It calculates the prior distribution of the present image using the posterior distribution of the previous adjacent image as well as the prior distribution of image variations.

The reason is that whitening transformation changes the distribution of image patches and learned features.

On the other hand, whitening transformation can just change the distribution of image features.

Coincidentally, whitening transformation can affect the distribution of image features by making image patches less correlated with each other.