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Binocular Vision Supports the Development of Scene Segmentation Capabilities: Evidence from a Deep Learning Model

Goutcher, Ross and Barrington, Chris and Hibbard, Paul and Graham, Bruce (2021) 'Binocular Vision Supports the Development of Scene Segmentation Capabilities: Evidence from a Deep Learning Model.' Journal of Vision, 21 (7). p. 13. ISSN 1534-7362

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The application of deep learning techniques has led to substantial progress in solving a number of critical problems in machine vision, including fundamental problems of scene segmentation and depth estimation. Here, we report a novel deep neural network (DNN) model, capable of simultaneous scene segmentation and depth estimation from a pair of binocular images. By manipulating the arrangement of binocular image pairs, presenting the model with standard left-right image pairs, identical image pairs or swapped left-right images, we show that performance levels depend upon the presence of appropriate binocular image arrangements. Segmentation and depth estimation performance are both impaired when images are swapped. Segmentation performance levels are maintained, however, for identical image pairs, despite the absence of binocular disparity information. Critically, these performance levels exceed those found for an equivalent, monocularly trained, segmentation model. These results provide evidence that binocular image differences support both the direct recovery of depth and segmentation information, and the enhanced learning of monocular segmentation signals. This finding suggests that binocular vision may play an important role in visual development. Better understanding of this role may hold implications for the study and treatment of developmentally acquired perceptual impairments.

Item Type: Article
Uncontrolled Keywords: Deep learning; Binocular vision; artificial neural networks; scene segmentation; distance; depth
Divisions: Faculty of Science and Health
Faculty of Science and Health > Psychology, Department of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 03 Aug 2021 08:26
Last Modified: 01 Sep 2022 11:04

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