Under the assumption of an aspect model, an image can be seen as a mixture of unobserved (latent) aspects that are defined by consistent co-occurrences of image patches (or their features) within the image collection.
The pixel model is based on the co-occurrence of image variations described by HOG of pixels in each region.
The basic idea of color by correlation is to precompute a correlation matrix which describes the extent to which proposed illuminants are compatible with the occurrence of image chromaticities [34, 35].
When the frequency of occurrence of image speeds of test stimuli has the same percentile rank as the reference stimulus, the frequency of occurrence of physical sources generating these image speeds is, by definition, the same.
As an example of how we determined the relationship between ambiguous image sequences and their moving 3-D sources, consider the frequency of occurrence of image speeds traversing 2° on the image plane.
By analyzing a database of moving objects projected onto an image plane we show that this phenomenology can be quantitatively accounted for by the frequency of occurrence of image speeds generated by perspective transformation.
If the speed-distance effect indeed arises from an empirical strategy of vision, then the relative occurrence of image speeds traveling over different projected distances on the image plane should accurately predict the responses of observers in psychophysical testing.
In keeping with earlier evidence [2], [3], we hypothesized that this effect is determined by the frequency of occurrence of image speeds traversing different projected distances in accumulated experience.
To assess the hypothesis that the speed-distance effect is determined by the frequency of occurrence of image speeds over various projected distances, we first established how images traversing 1° 7° on the image plane were related to moving objects in the 3-D virtual environment.
To obtain data about the frequency of occurrence of images needed to predict the speed-distance effect, we situated a frustum in the center of a spherical virtual environment to approximate the process of retinal image formation (Figure 2A; see [2] for details).
Gray entropy mathematical model is established according to the gray level-gradient co-occurrence matrix of image and the simulated annealing algorithm is used to solve the gray entropy model.
