  Mooney images can contribute to our understanding of the processes involved
in visual perception, because they allow a dissociation between image content
and image understanding. Mooney images are generated by first smoothing and
subsequently thresholding an image. In most previous studies this was performed
manually, using subjective criteria for generation. This manual process could
eventually be avoided by using automatic generation techniques. The field of
computer image processing offers numerous techniques for image thresholding,
but these are only rarely used to create Mooney images. Furthermore, there is
little research on the perceptual effects of smoothing and thresholding.
Therefore, in this study we investigated how the choice of different
thresholding techniques and amount of smoothing affects the interpretability of
Mooney images for human participants. We generated Mooney images using four
different thresholding techniques and, in a second experiment, parametrically
varied the level of smoothing. Participants identified the concepts shown in
Mooney images and rated their interpretability. Although the techniques
generate physically-different Mooney images, identification performance and
subjective ratings were similar across the different techniques. This indicates
that finding the perfect threshold in the process of generating Mooney images
is not critical for Mooney image interpretability, at least for
globally-applied thresholds. The degree of smoothing applied before
thresholding, on the other hand, requires more tuning depending on the noise of
the original image and the desired interpretability of the resulting Mooney
image. Future work in automatic Mooney image generation should pursue local
thresholding techniques, where different thresholds are applied to image
regions depending on the local image content.
