Processing convexity and concavity along a 2-D contour: figure–ground, structural shape, and attention

Bertamini, Marco; Wagemans, Johan

doi:10.3758/s13423-012-0347-2

Cited by 44 publications

(41 citation statements)

References 123 publications

(164 reference statements)

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“…So, the closure of a curve implies a perceptual bias towards convexity. This perceptual bias, which is explicitly present in a model formulated by Feldman & Singh (2005), has been documented in psychophysical experiments by Bertamini & Wagemans (2013), Braunstein, Hoffman, & Saidpour (1989), Driver & Baylis (1996), Elder & Zucker (1993), and Liu, Jacobs, & Basri (1999).…”

Section: Introductionmentioning

confidence: 70%

Spatially-global integration of closed, fragmented contours by finding the shortest-path in a log-polar representation

Kwon¹,

Agrawal²,

Li³

et al. 2016

Vision Research

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Finding the occluding contours of objects in real 2D retinal images of natural 3D scenes is done by determining, which contour fragments are relevant, and the order in which they should be connected. We developed a model that finds the closed contour represented in the image by solving a shortest path problem that uses a log-polar representation of the image; the kind of representation known to exist in area V1 of the primate cortex. The shortest path in a log-polar representation favors the smooth, convex and closed contours in the retinal image that have the smallest number of gaps. This approach is practical because finding a globally-optimal solution to a shortest path problem is computationally easy. Our model was tested in four psychophysical experiments. In the first two experiments, the subject was presented with a fragmented convex or concave polygon target among a large number of unrelated pieces of contour (distracters). The density of these pieces of contour was uniform all over the screen to minimize spatially-local cues. The orientation of each target contour fragment was randomly perturbed by varying the levels of jitter. Subjects drew a closed contour that represented the target’s contour on a screen. The subjects’ performance was nearly perfect when the jitter-level was low. Their performance deteriorated as jitter-levels were increased. The performance of our model was very similar to our subjects’. In two subsequent experiments, the subject was asked to discriminate a briefly-presented egg-shaped object while maintaining fixation at several different positions relative to the closed contour of the shape. The subject’s discrimination performance was affected by the fixation position in much the same way as the model’s.

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Section: Introductionmentioning

confidence: 70%

Spatially-global integration of closed, fragmented contours by finding the shortest-path in a log-polar representation

Kwon¹,

Agrawal²,

Li³

et al. 2016

Vision Research

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“…A significant disruption to perception of symmetry is caused by a mismatch in the coding of contour information, that is, when convexities on one side correspond to concavities on the other and vice versa . Importantly, the link between symmetry and objectness is specific to reflection symmetry, and Bertamini et al .…”

Section: Visual Processing Of Symmetry and Its Role In Figure–ground mentioning

confidence: 99%

The neural basis of visual symmetry and its role in mid‐ and high‐level visual processing

Bertamini

Silvanto

Norcia

et al. 2018

Annals of the New York Academy of Sciences

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Symmetry is an important and prominent feature of the visual world. It has been studied as a basis for image segmentation and perceptual organization, but it also plays a role in higher level processes, such as face and object perception. Over the past decade, there has been progress in the study of the neural mechanisms of symmetry perception in humans and other animals. There is extended activity in the ventral stream, including the lateral occipital complex (LOC) and VO1; this activity starts in V3 and it occurs independently of the task (automatic response). Additionally, when the task requires processing of symmetry, the activation may emerge for objects that are symmetrical, even though they do not project a symmetrical image. There is also some evidence of hemispheric lateralization, especially for the LOC. We review the studies on the cortical basis of visual symmetry processing and its links to encoding of other aspects of the visual world, such as faces and objects.

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“…In particular, we focus on the partitioning of cluttered scenes into basic object parts without the need for training data. As inspiration for a general rule for breaking scenes into elemental parts, we look to psychophysical studies, mostly performed on 2D images, which suggest that the transition between convex and concave image parts might be indicative of the separation between objects and/or their parts [13,25,21,15,7,4]. While this feature has been used in machine vision to some degree [10,17,20,24,11] success has remained limited and more recent studies were forced to combine this feature with additional, often very complex feature constellations to achieve good scene partitioning [20,24,11].…”

Section: Introduction and State-of-the-artmentioning

confidence: 99%

Object Partitioning Using Local Convexity

Stein

Schoeler

Papon

et al. 2014

2014 IEEE Conference on Computer Vision and Pattern Recognition

141

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The problem of how to arrive at an appropriate 3D-segmentation of a scene remains difficult. While current state-of-the-art methods continue to gradually improve in benchmark performance, they also grow more and more complex, for example by incorporating chains of classifiers, which require training on large manually annotated datasets. As an alternative to this, we present a new, efficient learning-and model-free approach for the segmentation of 3D point clouds into object parts. The algorithm begins by decomposing the scene into an adjacency-graph of surface patches based on a voxel grid. Edges in the graph are then classified as either convex or concave using a novel combination of simple criteria which operate on the local geometry of these patches. This way the graph is divided into locally convex connected subgraphs, which -with high accuracy -represent object parts. Additionally, we propose a novel depth dependent voxel grid to deal with the decreasing point-density at far distances in the point clouds. This improves segmentation, allowing the use of fixed parameters for vastly different scenes. The algorithm is straightforward to implement and requires no training data, while nevertheless producing results that are comparable to stateof-the-art methods which incorporate high-level concepts involving classification, learning and model fitting.

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Processing convexity and concavity along a 2-D contour: figure–ground, structural shape, and attention

Cited by 44 publications

References 123 publications

Spatially-global integration of closed, fragmented contours by finding the shortest-path in a log-polar representation

Spatially-global integration of closed, fragmented contours by finding the shortest-path in a log-polar representation

The neural basis of visual symmetry and its role in mid‐ and high‐level visual processing

Object Partitioning Using Local Convexity

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