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Journal Article

Phase noise and the classification of natural images


Wichmann,  FA
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Wichmann, F., Braun, D., & Gegenfurtner, K. (2006). Phase noise and the classification of natural images. Vision Research, 46(8-9), 1520-1529. doi:10.1016/j.visres.2005.11.008.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D22B-9
We measured the effect of global phase manipulations on a rapid animal categorization task. The Fourier spectra of our images of natural scenes were manipulated by adding
zero-mean random phase noise at all spatial frequencies. The phase noise was the
independent variable, uniformly and symmetrically distributed between 0 degree and
±180 degrees. Subjects were remarkably resistant to phase noise. Even with ±120 degree
phase noise subjects were still performing at 75 correct. The high resistance of the
subjects’ animal categorization rate to phase noise suggests that the visual system is
highly robust to such random image changes. The proportion of correct answers closely
followed the correlation between original and the phase noise-distorted images. Animal
detection rate was higher when the same task was performed with contrast reduced
versions of the same natural images, at contrasts where the contrast reduction mimicked
that resulting from our phase randomization. Since the subjects’ categorization rate was
better in the contrast experiment, reduction of local contrast alone cannot explain the
performance in the phase noise experiment. This result obtained with natural images
differs from those obtained for simple sinusoidal stimuli were performance changes due
to phase changes are attributed to local contrast changes only. Thus the global phasechange
accompanying disruption of image structure such as edges and object boundaries
at different spatial scales reduces object classification over and above the performance
deficit resulting from reducing contrast. Additional colour information improves the categorization performance by 2 .