Behind the Scenes of a Random Dot Stereogram

Terrell, Maria Shea; Terrell, Robert E.

doi:10.1080/00029890.1994.11997018

Cited by 2 publications

(1 citation statement)

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“…As part of the research efforts on autostereograms, Tyler and Clarke [31] also invented more complex autostereograms capable of encoding multiple depths. This concept was further examined by [27].…”

Section: Introductionmentioning

confidence: 99%

Depth perception in autostereograms: 1/f noise is best

et al. 2016

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An autostereogram is a single image that encodes depth information that pops out when looking at it. The trick is achieved by replicating a vertical strip that sets a basic two-dimensional pattern with disparity shifts that encode a three-dimensional scene. It is of interest to explore the dependency between the ease of perceiving depth in autostereograms and the choice of the basic pattern used for generating them. In this work we confirm a theory proposed by [5] to explain the process of autostereographic depth perception, providing a measure for the ease of "locking into" the depth profile, based on the spectral properties of the basic pattern used. We report the results of three sets of psychophysical experiments using autostereograms generated from two-dimensional random noise patterns having power spectra of the form 1/f β . The experiments were designed to test the ability of human subjects to identify smooth, low resolution surfaces, as well as detail, in the form of higher resolution objects in the depth profile, and to determine limits in identifying small objects as a function of their size. In accordance with the theory, we discover a significant advantage of the 1/f noise pattern (pink noise) for fast depth lock-in and fine detail detection, showing that such patterns are optimal choices for autostereogram design. Validating the theoretical model predictions strengthens its underlying assumptions, and contributes to a better understanding of the visual system's binocular disparity mechanisms.

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Section: Introductionmentioning

confidence: 99%

Depth perception in autostereograms: 1/f noise is best

et al. 2016

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Structural analysis of stereograms for CNN depth detection

Radványi

1999

IEEE Trans. Circuits Syst. I

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The usefulness of combining stereogram techniques with cellular neural network (CNN) analogic procedures in stereo depth extraction has been demonstrated before. Due to the local processing philosophy in the CNN paradigm, the detailed and well-established knowledge of local stereogram properties is of high importance. The paper gives an algorithmic definition for synthetic stereograms; a means for structural analysis of different types of random stereograms. In order to confine the generally propagating effect of three-dimensional (3-D) surface features into a restricted neighborhood in a stereogram, the concept of difference stereograms and the related difference surface is introduced for coding local surface variations into stereograms. The difference stereogram, with its local structure, is especially suitable for CNN processing, and it is closely connected to the pattern projection technique of depth extraction.

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Behind the Scenes of a Random Dot Stereogram

Cited by 2 publications

References 1 publication

Depth perception in autostereograms: 1/f noise is best

Depth perception in autostereograms: 1/f noise is best

Structural analysis of stereograms for CNN depth detection

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