Neural Dynamics of Perceptual Grouping: Textures, Boundaries, And Emergent Segmentations

Grossberg, Stephen; Mingolla, Ennio

doi:10.1016/s0166-4115(08)61760-4

Cited by 89 publications

(173 citation statements)

References 61 publications

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“…[11][12][13][14][15] The neural filling-in models differ from one another in their details, but an important common feature is the assumption that achromatic color signals diffuse spatially from the locations of borders within a neural color map of the image until their flow is stopped by the neural representation of another border within the color map. The spreading color signals are thus contained within closed boundaries.…”

Section: Computational Modelsmentioning

confidence: 99%

Progress on a computational model of achromatic color processing

Rudd

2003

SPIE Proceedings

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This paper reports further progress on a computational model of human achromatic color perception first presented at Human Vision and Electronic Imaging VI. The model predicts the achromatic colors of regions within 2D images comprising arbitrary geometric arrangements of luminance patches separated by sharp borders (i.e., Land Mondrian patterns). The achromatic colors of regions of homogeneous luminance are computed from the log luminance ratios at borders. Separate lightness and darkness induction signals are generated at the locations of borders in a cortical representation of the image and spread directionally over several degrees of visual angle. The color assigned to each point in the image is a weighted sum of all of the lightness and darkness signals converging on that point. The spatial convergence of induction signals can be modeled as a diffusive color filling-in process and realized in a neural network. The model has previously been used to predict lightness matches in psychophysical experiments conducted with stimuli consisting of disks and surrounding rings. Here a formal connection is made between the model equations used to predict lightness matches in these experiments and Stevens' power law model of the relationship between brightness and physical intensity. A neural mechanism involving lateral interactions between neurons that detect borders and generate spreading achromatic color induction signals is proposed to account for observed changes in the parameters of the model, including the brightness law exponent, with changes in surround size. A medium gray paper viewed against a black background appears light gray, but an identical paper viewed simultaneously against a white background appears dark gray. This well-known perceptual phenomena, known as simultaneous contrast, is one of the simplest examples of achromatic color induction, a class of phenomena in which the perceived lightness of a particular region of a visual image is influenced by other regions within the scene or image. The ultimate goal of the project reported on here is to build a quantitative model of achromatic color induction that can account for psychophysical lightness judgments made by human observers in response to achromatic images of arbitrary complexity. Here the discussion will be restricted to images that contain sharp borders delineating regions having homogeneous luminance.The results of both perceptual and neurophysiological studies suggest that all color induction effects (including, but not limited to, achromatic color induction) originate from borders. For example, Krauskopf 1 presented observers with a visual stimulus comprising a red disk surrounded by a green background. The red disk was stabilized on the observer's retina, but the green background was not. The stabilized red disk disappeared after a few seconds and the stimulus was seen as a homogenous green field. In other words, the observer's visual system perceptually filled in the area of the stabilized disk with the color of the background. The standa...

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Section: Computational Modelsmentioning

confidence: 99%

Progress on a computational model of achromatic color processing

Rudd

2003

SPIE Proceedings

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“…It is as easy to fuse as the dense RDS, and it is interesting to observe that the visual system interpolates a surface in depth where there are no dots present (as if it were a "convex hull"). [This "filling in" phenomenon inspired many model builders of early vision; the interested reader might consult Grossberg and Mingolla (1985). ]…”

Section: Left Eyementioning

confidence: 99%

Early vision and focal attention

Julesz

1991

Rev. Mod. Phys.

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At the thirty-year anniversary of the introduction of the technique of computer-generated random-dot stereograms and random-dot cinematograms into psychology, the impact of the technique on brain research and on the study of artificial intelligence is reviewed. The main finding-that stereoscopic depth perception (stereopsis), motion perception, and preattentive texture discrimination are basically bottomup processes, which occur without the help of the top-down processes of cognition and semantic memory-greatly simplifies the study of these processes of early vision and permits the linking of human perception with monkey neurophysiology. Particularly interesting are the unexpected findings that stereopsis (assumed to be local) is a global process, while texture discrimination (assumed to be a global process, governed by statistics) is local, based on some conspicuous local features (textons). It is shown that the top-down process of "shape (depth) from shading" does not affect stereopsis, and some of the models of machine vision are evaluated. The asymmetry effect of human texture discrimination is discussed, together with recent nonlinear spatial filter models and a novel extension of the texton theory that can cope with the asymmetry problem. This didactic review attempts to introduce the physicist to the field of psychobiology and its problems-including metascientific problems of brain research, problems of scientific creativity, the state of artificial intelligence research (including connectionist neural networks) aimed at modeling brain activity, and the fundamental role of focal attention in mental events.

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“…By comparison, object surfaces and support structures, such as the background, typically comprise regions where these properties vary more slowly. Not surprisingly, the processing of both image discontinuities and the grouping of features over space have featured prominently in models of image segmentation (Grossberg & Mingolla, 1985; Koechlin, Anton, & Burnod, 1999; Li, 2000; Malik & Perona, 1990; Roelfsema, Lamme, Spekreijse, & Bosch, 2002; Thielscher & Neumann, 2003) and in empirical studies (Bach & Meigen, 1992; Lamme, Van Dijk, & Spekreijse, 1992; Landy & Bergen, 1991; Marcus & Van Essen, 2002; Nothdurft, 1991; Song & Baker, 2006; Sutter & Graham, 1995). …”

Section: Introductionmentioning

confidence: 99%

Figure-ground interaction in the human visual cortex

et al. 2008

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Discontinuities in feature maps serve as important cues for the location of object boundaries. Here we used multi-input nonlinear analysis methods and EEG source imaging to assess the role of several different boundary cues in visual scene segmentation. Synthetic figure/ground displays portraying a circular figure region were defined solely by differences in the temporal frequency of the figure and background regions in the limiting case and by the addition of orientation or relative alignment cues in other cases. The use of distinct temporal frequencies made it possible to separately record responses arising from each region and to characterize the nature of nonlinear interactions between the two regions as measured in a set of retinotopically and functionally defined cortical areas. Figure/background interactions were prominent in retinotopic areas, and in an extra-striate region lying dorsal and anterior to area MT+. Figure/background interaction was greatly diminished by the elimination of orientation cues, the introduction of small gaps between the two regions, or by the presence of a constant second-order border between regions. Nonlinear figure/background interactions therefore carry spatially precise, time-locked information about the continuity/discontinuity of oriented texture fields. This information is widely distributed throughout occipital areas, including areas that do not display strong retinotopy.

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Neural Dynamics of Perceptual Grouping: Textures, Boundaries, And Emergent Segmentations

Cited by 89 publications

References 61 publications

Progress on a computational model of achromatic color processing

Progress on a computational model of achromatic color processing

Early vision and focal attention

Figure-ground interaction in the human visual cortex

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