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Skeletal representations of shape in human vision: evidence for a pruned medial axis model

Ayzenberg, Vladislav, Chen, Yunxiao, Yousif, Sami R. and Lourenco, Stella F. (2019) Skeletal representations of shape in human vision: evidence for a pruned medial axis model. Journal of Vision, 19 (6). pp. 1-21. ISSN 1534-7362

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Identification Number: 10.1167/19.6.6

Abstract

A representation of shape that is low dimensional and stable across minor disruptions is critical for object recognition. Computer vision research suggests that such a representation can be supported by the medial axis-a computational model for extracting a shape's internal skeleton. However, few studies have shown evidence of medial axis processing in humans, and even fewer have examined how the medial axis is extracted in the presence of disruptive contours. Here, we tested whether human skeletal representations of shape reflect the medial axis transform (MAT), a computation sensitive to all available contours, or a pruned medial axis, which ignores contours that may be considered ''noise.'' Across three experiments, participants (N = 2062) were shown complete, perturbed, or illusory two-dimensional shapes on a tablet computer and were asked to tap the shapes anywhere once. When directly compared with another viable model of shape perception (based on principal axes), participants' collective responses were better fit by the medial axis, and a direct test of boundary avoidance suggested that this result was not likely because of a task-specific cognitive strategy (Experiment 1). Moreover, participants' responses reflected a pruned computation in shapes with small or large internal or external perturbations (Experiment 2) and under conditions of illusory contours (Experiment 3). These findings extend previous work by suggesting that humans extract a relatively stable medial axis of shapes. A relatively stable skeletal representation, reflected by a pruned model, may be well equipped to support real-world shape perception and object recognition.

Item Type: Article
Additional Information: © 2019 The Authors
Divisions: Statistics
Subjects: Q Science > Q Science (General)
Date Deposited: 28 Jun 2019 08:15
Last Modified: 20 Jun 2020 02:48
URI: http://eprints.lse.ac.uk/id/eprint/101082

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