On a common circle: natural scenes and Gestalt rules.

By Mariano Sigman, Guillermo A. Cecchi, Charles D. Gilbert, M. Magnasco. Proceedings of the National Academy of Sciences of the United States of America, 2001

336 citations recorded by the source corpus.

To understand how the human visual system analyzes images, it is essential to know the structure of the visual environment. In particular, natural images display consistent statistical properties that distinguish them from random luminance distributions. We have studied the geometric regularities of oriented elements (edges or line segments) present in an ensemble of visual scenes, asking how much information the presence of a segment in a particular location of the visual scene carries about the presence of a second segment at different relative positions and orientations. We observed strong long-range correlations in the distribution of oriented segments that extend over the whole visual field. We further show that a very simple geometric rule, cocircularity, predicts the arrangement of segments in natural scenes, and that different geometrical arrangements show relevant differences in their scaling properties. Our results show similarities to geometric features of previous physiological and psychophysical studies. We discuss the implications of these findings for theories of early vision.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/1e5cc92146b582ec8a2c9dd89d706d0ace5a3020

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Citations in the corpus

Visual Parsing After Recovery From Blindness (2009, background): “These cues have conventionally been assumed to be of fundamental significance for spatial integration ( Sigman, Cecchi, Gilbert, & Magnasco, 2001; Ullman, 1996; Wertheimer, 1938).”
Visual perception and the statistical properties of natural scenes. (2008, background): “For example, if there is a large response from an oriented edge sensor, then it is likely that a neighboring colinear (or cocircular) edge sensor also has a large response (Geisler et al. 2001, Sigman et al. 2001).”
A systematic comparison between visual cues for boundary detection. (2016, methodology): “The use of Gabor filters, the energy model and normalization are standard in studies of natural boundary statistics (e.g., Krüger, 1998; Geisler et al., 2001; Geisler, Perry, & Ing, 2008; Sigman et al., 2001).”
Natural image statistics and neural representation. (2001, background): “Similar results have been obtained using other forms of ICA (Bell & Sejnowski 1997, van Hateren & van der Schaaf 1998, Lewicki & Olshausen 1999), and Hyvärinen & Hoyer (2000) have derived complex cell properties by extending ICA to operate on subspaces.”
Lateral connectivity and contextual interactions in macaque primary visual cortex. (2002, background): “, 1995, 2000), as well as statistical analyses of the geometric properties of natural scenes (Sigman et al., 2001; Geisler et al., 2001).”
Mechanisms of perceptual organization provide auto-zoom and auto-localization for attention to objects (2010, background): “The mechanism we propose works with generic, zero-threshold linear neurons and the connection patterns are plausibly related to the statistics of natural visual scenes (16, 17).”
Reinforcement learning, efficient coding, and the statistics of natural tasks (2015, background): “This suggests an intriguing analogy to the importance of edges in image statistics (see [11 ,50]).”
A Century of Gestalt Psychology in Visual Perception I. Perceptual Grouping and Figure-Ground Organization (2012, background): “Kruger (1998) and later Sigman et al. (2001) found evidence for collinearity, cocircularity and parallelism in the statistics of natural images.”
Visual objects in context (2004, background): “as well as analysis shortcuts produced by the expectations that the context frames provide, possibly mediated by long-range cortical connection”