Higher order image structure enables boundary segmentation in the absence of luminance or contrast cues

Abstract

Lower order image statistics, which can be described by an image's Fourier energy content, enable segmentation when they are different on either side of a boundary. We have previously demonstrated that the spatial distribution of the energy in an image (described by its higher order statistics or structure) could influence segmentation thresholds for contrast- and orientation-defined boundaries, even though it was the same on either side of the boundary and thus task irrelevant (Zavitz & Baker, 2013). Here we examined whether higher order statistics can also enable segmentation when boundaries are defined by differences in structure or density of texture elements. We used micropattern-based naturalistic synthetic textures to manipulate the sparseness, global phase alignment, and local phase alignment of carrier textures and measured segmentation thresholds based on forced-choice judgments of boundary orientation. We found that both global phase structure and sparseness, but not local phase alignment, enable segmentation and that local structure also has a small effect on segmentation thresholds in both cases. Simulations of a two-stage filter model with a compressive intermediate nonlinearity can reproduce the major features of the experimental data, segmenting boundaries defined by higher order statistics alone while capturing the influence of global image structure on segmentation thresholds.

Modeling second-order boundary perception: A machine learning approach

“…Most previous FRF models in human psychophysics studies were conceptual or qualitative, without quantitative fitting of model parameters to data. Two recent studies [28, 90] estimated free parameters of an FRF model by fitting threshold data as functions of stimulus parameters. Since thresholds are typically determined at only a few levels of a one-dimensional independent variable, this yields relatively few data points, greatly limiting the number of model parameters one can estimate.…”

“…The boundary between the two regions was tapered smoothly, as was the outer edge of the circular region, so that it smoothly decayed into the background (see stimulus images in Fig 1). The functional form of the tapering was a half-cosine going from 1 to -1 over a proportion (0.2) of the stimulus width, as used previously [28, 90, 108]. Such tapering was a precaution against first-order artifacts.…”

High-quality binary fringe patterns generation by combining optimization on global and local similarity

“…Based on the theory of references [22,23], every S x and S y can respectively be calculated by using formula:…”

On texture, form, and fixational eye movements

“…Because textures are defined as patterns with position-invariant statistical properties [10], the responses of neurons tuned to textures are often analyzed using multi-stage models that combine position-invariance with selective tuning to conjunctions of edges of different angles [1**, 2–5,11,12] (Figure 2). Analyses of V2 responses to natural stimuli using such models have yielded three organizing principles for their feature-selectivity [11].…”

“…Impressionist paintings serve to illustrate the point that visual forms can be defined by changes in textures as well as by changes in brightness [1**, 2–5] (see Fig. 1 for an example).…”