Image statistics of American Sign Language: comparison with faces and natural scenes.

Several lines of evidence suggest that the image statistics of the environment shape visual abilities. To date, the image statistics of natural scenes and faces have been well characterized using Fourier analysis. We employed Fourier analysis to characterize images of signs in American Sign Language (ASL). These images are highly relevant to signers who rely on ASL for communication, and thus the image statistics of ASL might influence signers' visual abilities. Fourier analysis was conducted on 105 static images of signs, and these images were compared with analyses of 100 natural scene images and 100 face images. We obtained two metrics from our Fourier analysis: mean amplitude and entropy of the amplitude across the image set (which is a measure from information theory) as a function of spatial frequency and orientation. The results of our analyses revealed interesting differences in image statistics across the three different image sets, setting up the possibility that ASL experience may alter visual perception in predictable ways. In addition, for all image sets, the mean amplitude results were markedly different from the entropy results, which raises the interesting question of which aspect of an image set (mean amplitude or entropy of the amplitude) is better able to account for known visual abilities.