Article

Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model

Citation

Goutcher R, Barrington C, Hibbard PB & Graham B (2021) Binocular vision supports the development of scene segmentation capabilities: Evidence from a deep learning model. Journal of Vision, 21 (7), Art. No.: 13. https://doi.org/10.1167/jov.21.7.13

Abstract
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 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 on 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.

Keywords
deep learning; binocular vision; segmentation; depth perception

Journal
Journal of Vision: Volume 21, Issue 7

StatusPublished
FundersMoD Ministry of Defence (MoD)
Publication date31/07/2021
Publication date online21/07/2021
Date accepted by journal23/06/2021
URLhttp://hdl.handle.net/1893/33001
eISSN1534-7362