Local figure-ground cues are valid for natural images.

Figure-ground organization refers to the visual perception that a contour separating two regions belongs to one of the regions. Recent studies have found neural correlates of figure-ground assignment in V2 as early as 10-25 ms after response onset, providing strong support for the role of local bottom-up processing. How much information about figure-ground assignment is available from locally computed cues? Using a large collection of natural images, in which neighboring regions were assigned a figure-ground relation by human observers, we quantified the extent to which figural regions locally tend to be smaller, more convex, and lie below ground regions. Our results suggest that these Gestalt cues are ecologically valid, and we quantify their relative power. We have also developed a simple bottom-up computational model of figure-ground assignment that takes image contours as input. Using parameters fit to natural image statistics, the model is capable of matching human-level performance when scene context limited.

[1]  Mary Henle,et al.  Vision and artifact , 1977 .

[2]  R. von der Heydt,et al.  Coding of Border Ownership in Monkey Visual Cortex , 2000, The Journal of Neuroscience.

[3]  J. Elder,et al.  Ecological statistics of Gestalt laws for the perceptual organization of contours. , 2002, Journal of vision.

[4]  E. Brunswik,et al.  Ecological cue-validity of proximity and of other Gestalt factors. , 1953, The American journal of psychology.

[5]  M. Peterson Object Recognition Processes Can and Do Operate Before Figure–Ground Organization , 1994 .

[6]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[7]  Joseph J. Atick,et al.  What Does the Retina Know about Natural Scenes? , 1992, Neural Computation.

[8]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[9]  G. Woodman,et al.  Lower region: a new cue for figure-ground assignment. , 2002, Journal of experimental psychology. General.

[10]  F. Qiu,et al.  Figure and Ground in the Visual Cortex: V2 Combines Stereoscopic Cues with Gestalt Rules , 2005, Neuron.

[11]  Jitendra Malik,et al.  A Probabilistic Multi-scale Model for Contour Completion Based on Image Statistics , 2002, ECCV.

[12]  D. Ruderman The statistics of natural images , 1994 .

[13]  R. von der Heydt,et al.  Mechanisms of contour perception in monkey visual cortex. I. Lines of pattern discontinuity , 1989, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[14]  W. Metzger Gesetze des Sehens , 1937 .

[15]  Jeffrey S. Perry,et al.  Edge co-occurrence in natural images predicts contour grouping performance , 2001, Vision Research.

[16]  Walter Gerbino,et al.  Convexity and Symmetry in Figure-Ground Organization , 1976 .

[17]  Li Zhaoping,et al.  Border Ownership from Intracortical Interactions in Visual Area V2 , 2005, Neuron.

[18]  Jitendra Malik,et al.  Learning affinity functions for image segmentation: combining patch-based and gradient-based approaches , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[19]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[20]  Jitendra Malik,et al.  Figure/Ground Assignment in Natural Images , 2006, ECCV.

[21]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[22]  Josh H. McDermott,et al.  Psychophysics with junctions in real images. , 2010, Perception.

[23]  E. Reed The Ecological Approach to Visual Perception , 1989 .

[24]  Robert A. Frazor,et al.  Independence of luminance and contrast in natural scenes and in the early visual system , 2005, Nature Neuroscience.

[25]  Jeffrey A. Sloan,et al.  Spatial frequency analysis of the visual environment: Anisotropy and the carpentered environment hypothesis , 1978, Vision Research.