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A computational model of a visual system is proposed in terms of perception of shape and depth from texture. Humans can perceive 3D shape and depth from texture variation. In this study, the relationships between spectral features and 3D perception are examined using stimuli with varied frequency spectra generated from white noise. Experimental results suggest that frequency spectra in the human visual system are characterized by the average peak frequency for both perspective and orthographic images. On the other hand, computational analysis indicates that normalization of this average peak frequency provides depth in perspective images and slant in orthographic images. To verify these results, a network model is designed, and the models characteristics are compared to those of human vision. The simulation results agree well with psychophysical experiments. Reproduction of human depth perception is achieved for a variety of images. © 1999 Scripta Technica, Syst Comp Jpn, 30(5): 917, 1999

Network algorithm for shape-from-texture based on human visual psychophysics and computational analysis