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Elizabeth Arsenault, Curtis Baker; Segmentation mechanisms are sensitive to and can segment by higher-order statistics in naturalistic textures. Journal of Vision 2011;11(11):1160. doi: https://doi.org/10.1167/11.11.1160.
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© ARVO (1962-2015); The Authors (2016-present)
Texture segmentation depends on the statistical properties of the textures in question, but which properties are biologically relevant remains unclear. Previously, we determined that contrast boundary segmentation in a single texture was affected by global phase structure – in particular, the texture density (VSS 2009). Here, we manipulate density and broadband phase alignment for boundaries between pairs of textures differing in orientation or phase alignment to uncover the statistical sensitivities of segmentation mechanisms. We created synthetic micropattern textures that mimic important statistical properties of natural textures (VSS 2009). We were able to remove all higher-order statistics by globally phase-scrambling the texture, or remove local phase alignments by phase-scrambling the micropatterns, while varying texture sparseness by changing the number of micropatterns. We created two types of texture boundaries using a quilting method: (1) orientation modulations, where one texture had vertical micropatterns and the other horizontal, and (2) phase alignment boundaries between different pairings of the intact, local (LS), and global (GS) scramble conditions. We obtained modulation-depth thresholds for all boundary types at a series of micropattern densities. Orientation-defined boundaries become easier to segment as density increases, with boundaries between GS textures being easier to segment than those between either intact or LS textures. As density increases, boundaries between GS and either intact or LS become more difficult, but boundaries between intact and LS textures become easier. We observe that boundaries defined by changes in phase alignment are no more difficult to segment than those defined by changes in orientation These results lend support to the idea that sparseness is an important texture dimension impacting performance in segmentation tasks. Our findings suggest that early inputs to segmentation mechanisms are sensitive to higher-order statistics such as sparseness as well as simple attributes such as contrast and orientation.
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