May 2008
Volume 8, Issue 6
Free
Vision Sciences Society Annual Meeting Abstract  |   May 2008
Texture segmentation in natural images: Contribution of higher-order image statistics to psychophysical performance
Author Affiliations
  • Curtis Baker
    McGill Vision Research, Department of Ophthalmology
  • Ahmad Yoonessi
    Department of Neurology and Neurosurgery
  • Elizabeth Arsenault
    Department of Psychology, McGill University, Montreal, Canada
Journal of Vision May 2008, Vol.8, 350. doi:10.1167/8.6.350
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      Curtis Baker, Ahmad Yoonessi, Elizabeth Arsenault; Texture segmentation in natural images: Contribution of higher-order image statistics to psychophysical performance. Journal of Vision 2008;8(6):350. doi: 10.1167/8.6.350.

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Abstract

Perceptual segmentation of a boundary between two textures is conventionally thought to be based upon differences in their Fourier energy, i.e. in their low-order texture statistics. Most evidence supporting (or contradicting) this idea has arisen from studies using various synthetic texture patterns. But what role, if any, do higher-order texture statistics play in segmenting natural images? Here we extracted high resolution texture regions from monochrome photographs of natural scenes, rich in higher-order statistics. By phase-scrambling these textures, we could remove their high-order statistics, leaving mean luminance and RMS contrast unchanged. Using pairs of natural or phase-scrambled textures, we created RMS-balanced texture quilt boundaries in half-disc stimuli. We also created similar contrast boundaries from individual textures. Employing forced choice judgments of boundary orientation (left- vs. right-oblique), we measured modulation-depth thresholds for both contrast and texture boundaries. If only the low-order statistics are used, then phase-scrambling should have no effect on psychophysical performance. Boundaries between these texture pairs could usually be segregated (thresholds: 35–70%), though in some cases even 100% modulation-depth did not produce reliable performance. In most instances, phase-scrambling made the task impossible. However in a minority of scrambled texture pairs, thresholds were measurable and in some, performance was improved. Contrast boundaries yielded lower modulation-depth thresholds (10–30%) which were impervious to or improved by phase-scrambling, particularly at lower texture contrasts. These results suggest that higher-order texture statistics contribute importantly to boundary segmentation in natural scenes.

Baker, C. Yoonessi, A. Arsenault, E. (2008). Texture segmentation in natural images: Contribution of higher-order image statistics to psychophysical performance [Abstract]. Journal of Vision, 8(6):350, 350a, http://journalofvision.org/8/6/350/, doi:10.1167/8.6.350. [CrossRef]
Footnotes
 Funded by an NSERC grant to CLB (OPG0001978).
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