Perception of stationary plaids: The role of spatial filters in edge analysis

  title={Perception of stationary plaids: The role of spatial filters in edge analysis},
  author={Mark A. Georgeson and Tim S. Meese},
  journal={Vision Research},
Adaptive Filtering in Spatial Vision: Evidence from Feature Marking in Plaids
It is suggested that combination and segmentation of spatial filters in the patchwise Fourier domain underpins the perceptual segmentation observed in the experiments.
Contrast Gain Control in Plaid Pattern Detection
A plaid pattern is mediated by a combination of orientation-selective mechanisms, which can be explained by a multiple-mechanism divisive inhibition model, which contains several orientation- selective mechanisms.
The spatial characteristics of plaid-form-selective mechanisms
Orientation processing mechanisms revealed by the plaid tilt illusion
A common rule for integration and suppression of luminance contrast across eyes, space, time, and pattern
This work measured triplets of dipper functions for targets and pedestals involving interdigitated stimulus pairs and found a simple arrangement of summation and counter-suppression achieves integration of various stimulus attributes without distorting the underlying contrast code.
The time course of feature integration in plaid patterns revealed by meta- and paracontrast masking.
The present study reveals the time course of this process by applying meta- and paracontrast masking to both simple oriented and plaid gratings, and discusses in how far these results could also be explained by the dynamics of cross-orientation suppression and how they might relate to the process of feature integration in plaids.
Low spatial frequencies are suppressively masked across spatial scale, orientation, field position, and eye of origin.
The results confirm that above detection threshold, cross-channel masking involves contrast suppression and not (purely) mask-induced noise, and conclude that cross-Channel masking can be a powerful phenomenon, particularly at low test spatial frequencies and when mask and test are presented to different eyes.
Feedback and Surround Modulated Boundary Detection
A biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1), which shows a big improvement compared to the current non-learning and biologically- inspired state-of-the-art algorithms while being competitive to the learning-based methods.
Contour integration and scale combination processes in visual edge detection.
This work determined spatial-frequency tuning for the detection of contours composed of broadband edge elements, alternating with narrow-band Gabor elements, to determine how these two types of combination fit together.


Edge Computation in Human Vision: Anisotropy in the Combining of Oriented Filters
Perceived spatial structure was found to depend on plaid orientation: compound structures were perceived more often when the plaid components were balanced around the cardinal axes of the retina, and it was suggested that the principles governing the combination of oriented-filter outputs might be learnt during the development of the visual system by using a Hebb-type rule.
Human vision combines oriented filters to compute edges
  • M. Georgeson
  • Physics
    Proceedings of the Royal Society of London. Series B: Biological Sciences
  • 1992
The outlines of a model for edge finding in human vision are proposed, where two-component plaid components are processed through cortical, orientationselective filters that are subject to attenuation by forward masking and adaptation, and zero crossings in the combined output are used to determine edge locations.
The Perceived Spatial Frequency, Contrast, and Orientation of Illusory Gratings
The results are interpreted in terms of inhibition and disinhibition in an organized matrix of tuned channels, and the dominant pattern of inhibition in the matrix is inferred.
From filters to features: location, orientation, contrast and blur.
An algorithm for recovering the blur of edges is derived as the square-root of the ratio of 1st to 3rd derivatives at the edge location, which successfully predicts blur matching between Gaussian edges and a variety of other test waveforms, including sine waves.