Improved deep learning architecture for depth estimation from single image
Joint Authors
Chan, Huah Yong
Abu Waidah, Suhaila F. A.
Source
Jordanian Journal of Computetrs and Information Technology
Issue
Vol. 6, Issue 4 (31 Dec. 2020), pp.434-445, 12 p.
Publisher
Princess Sumaya University for Technology
Publication Date
2020-12-31
Country of Publication
Jordan
No. of Pages
12
Main Subjects
Engineering & Technology Sciences (Multidisciplinary)
Abstract EN
Numerous benefits of depth estimation from the single image field on medicine, robot video games and 3D reality applications have garnered attention in recent years.
Closely related to the third dimension of depth, this operation can be accomplished using human vision, though considered challenging due to the various issues when using computer vision.
The differences in the geometry, the texture of the scene, the occlusion scene boundaries and the inherent ambiguity exist because of the minimal information that could be gathered from a single image.
This paper, therefore, proposes a novel depth estimation in the field of architecture, which includes the stages that can manage depth estimation from a single RGB image.
An encoder-decoder architecture has been proposed, based on the improvement yielded from DenseNet that extracted the map of an image using skip connection technique.
This paper also takes on the reverse Huber loss function that essentially suits our architecture hand driven by the value distributions that are commonly present in depth maps.
Experimental results have indicated that the depth estimation architecture that employs the NYU Depth v2 dataset has a better performance than the other state-of-the-art methods that tend to have fewer parameters and require fewer training time.
American Psychological Association (APA)
Abu Waidah, Suhaila F. A.& Chan, Huah Yong. 2020. Improved deep learning architecture for depth estimation from single image. Jordanian Journal of Computetrs and Information Technology،Vol. 6, no. 4, pp.434-445.
https://search.emarefa.net/detail/BIM-1415799
Modern Language Association (MLA)
Abu Waidah, Suhaila F. A.& Chan, Huah Yong. Improved deep learning architecture for depth estimation from single image. Jordanian Journal of Computetrs and Information Technology Vol. 6, no. 4 (Dec. 2020), pp.434-445.
https://search.emarefa.net/detail/BIM-1415799
American Medical Association (AMA)
Abu Waidah, Suhaila F. A.& Chan, Huah Yong. Improved deep learning architecture for depth estimation from single image. Jordanian Journal of Computetrs and Information Technology. 2020. Vol. 6, no. 4, pp.434-445.
https://search.emarefa.net/detail/BIM-1415799
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 443-445
Record ID
BIM-1415799