It is proposed in this paper that many geometrical optical illusions, as well as illusory patterns due to motion signals in line drawings, are due to the statistics of visual computations. The interpretation of image patterns is preceded by a step where image features such as lines, intersections of lines, or local image movement must be derived. However, there are many sources of noise or uncertainty in the formation and processing of images, and they cause problems in the estimation of these features; in particular, they cause bias. As a result, the locations of features are perceived erroneously and the appearance of the patterns is altered. The bias occurs with any visual processing of line features; under average conditions it is not large enough to be noticeable, but illusory patterns are such that the bias is highly pronounced. Thus, the broader message of this paper is that there is a general uncertainty principle which governs the workings of vision systems, and optical illusions are an artifact of this principle.
The application of nonlinear anisotropic diffusion filtering to reduce noise and enhance contours in images obtained by two-dimensional planar laser-induced fluorescence (PLIF) spectroscopy is presented. In this process the diffusion coefficient is locally adapted, becoming negligible as object boundaries are approached. Noise is efficiently removed, and object contours are strongly enhanced. The technique is demonstrated with PLIF images obtained from the OH radical recorded in turbulent flames. We show that nonlinear diffusion is suitable as a preprocessing step, before image segmentation becomes possible, and we demonstrate how the technique is applied for the quantitative extraction of flame reaction boundaries from PLIF data.
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