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Local depth edge detection in humans and deep neural networks

Local depth edge detection in humans and deep neural networks
Local depth edge detection in humans and deep neural networks
Distinguishing edges caused by a change in depth from other types of edges is an important problem in early vision. We investigate the performance of humans and computer vision models on this task. We use spherical imagery with ground-truth LiDAR range data to build an objective ground-truth dataset for edge classification. We compare various computational models for classifying depth from non-depth edges in small images patches and achieve the best performance (86%) with a convolutional neural network. We investigate human performance on this task in a behavioral experiment and find that human performance is lower than the CNN. Although human and CNN depth responses are correlated, observers’ responses are better predicted by other observers than by the CNN. The responses of CNNs and human observers also show a slightly different pattern of correlation with low-level edge cues, which suggests that CNNs and human observers may weight these features differently for classifying edges.
2473-9944
2681-2689
IEEE
Ehinger, Krista
3738096b-076a-4137-964e-bdb8a163f9e8
Adams, Wendy
25685aaa-fc54-4d25-8d65-f35f4c5ab688
Graf, Erich
1a5123e2-8f05-4084-a6e6-837dcfc66209
Elder, James H.
f7d4f18e-09dd-4e5c-8fc9-b03064c9ff71
Ehinger, Krista
3738096b-076a-4137-964e-bdb8a163f9e8
Adams, Wendy
25685aaa-fc54-4d25-8d65-f35f4c5ab688
Graf, Erich
1a5123e2-8f05-4084-a6e6-837dcfc66209
Elder, James H.
f7d4f18e-09dd-4e5c-8fc9-b03064c9ff71

Ehinger, Krista, Adams, Wendy, Graf, Erich and Elder, James H. (2018) Local depth edge detection in humans and deep neural networks. In ICCV Workshop on Mutual Benefits of Cognitive and Computer Vision. IEEE. pp. 2681-2689 .

Record type: Conference or Workshop Item (Paper)

Abstract

Distinguishing edges caused by a change in depth from other types of edges is an important problem in early vision. We investigate the performance of humans and computer vision models on this task. We use spherical imagery with ground-truth LiDAR range data to build an objective ground-truth dataset for edge classification. We compare various computational models for classifying depth from non-depth edges in small images patches and achieve the best performance (86%) with a convolutional neural network. We investigate human performance on this task in a behavioral experiment and find that human performance is lower than the CNN. Although human and CNN depth responses are correlated, observers’ responses are better predicted by other observers than by the CNN. The responses of CNNs and human observers also show a slightly different pattern of correlation with low-level edge cues, which suggests that CNNs and human observers may weight these features differently for classifying edges.

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EhingerAdamsGrafElder2017 - Accepted Manuscript
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Accepted/In Press date: 22 August 2017
e-pub ahead of print date: 23 January 2018
Venue - Dates: 2017 IEEE International on Computer Vision Workshop, Italy, 2017-10-22 - 2017-10-29

Identifiers

Local EPrints ID: 413753
URI: http://eprints.soton.ac.uk/id/eprint/413753
ISSN: 2473-9944
PURE UUID: f7379d4f-39ae-4055-8850-a7a9bf1cf587
ORCID for Wendy Adams: ORCID iD orcid.org/0000-0002-5832-1056
ORCID for Erich Graf: ORCID iD orcid.org/0000-0002-3162-4233

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Date deposited: 05 Sep 2017 16:30
Last modified: 07 Oct 2020 04:25

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Contributors

Author: Krista Ehinger
Author: Wendy Adams ORCID iD
Author: Erich Graf ORCID iD
Author: James H. Elder

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