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Monocular Depth Estimation Using Deep Learning With Active Vision.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Monocular Depth Estimation Using Deep Learning With Active Vision./
Author:	Meier, Joseph Raymond.
Description:	1 online resource (34 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798382611037

Monocular Depth Estimation Using Deep Learning With Active Vision.
Meier, Joseph Raymond.

Monocular Depth Estimation Using Deep Learning With Active Vision. - 1 online resource (34 pages)

Source: Masters Abstracts International, Volume: 85-11.

Thesis (M.S.)--Tarleton State University, 2024.

Includes bibliographical references

Grasping the structure of an environment within images is a core challenge for machine perception. Three-dimensional data is instrumental in robotic path planning, autonomous driving, scene reconstruction, and a wide array of applications. The conventional approaches of stereo vision and laser scanning are not always the ideal selection for a particular system or available expenditure. Predicting depth from images taken with a single camera offers a potential solution to these cases albeit with inherent constraints.This research proposes a monocular depth estimation method applying deep learning techniques coupled with multiple linear regression on images captured by a 12- megapixel camera with a motorized varifocal lens. The approach simulates world coordinates projected onto the image plane with camera parameters of varying focal lengths. BoostedDepth single image relative depth estimation, YOLOv8 image detection and segmentation process the real-world images to establish an initial absolute metric depth prediction. This preliminary prediction defines the optimal range of world coordinates to train the regression model. Applying the model with the image coordinates of the base image and the magnitude of disparity between a set of images at distinct magnification produces the depth estimate.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382611037Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Monocular depth estimation methodIndex Terms--Genre/Form:

554714
Electronic books.

Monocular Depth Estimation Using Deep Learning With Active Vision.
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504 $a Includes bibliographical references
520 $a Grasping the structure of an environment within images is a core challenge for machine perception. Three-dimensional data is instrumental in robotic path planning, autonomous driving, scene reconstruction, and a wide array of applications. The conventional approaches of stereo vision and laser scanning are not always the ideal selection for a particular system or available expenditure. Predicting depth from images taken with a single camera offers a potential solution to these cases albeit with inherent constraints.This research proposes a monocular depth estimation method applying deep learning techniques coupled with multiple linear regression on images captured by a 12- megapixel camera with a motorized varifocal lens. The approach simulates world coordinates projected onto the image plane with camera parameters of varying focal lengths. BoostedDepth single image relative depth estimation, YOLOv8 image detection and segmentation process the real-world images to establish an initial absolute metric depth prediction. This preliminary prediction defines the optimal range of world coordinates to train the regression model. Applying the model with the image coordinates of the base image and the magnitude of disparity between a set of images at distinct magnification produces the depth estimate.
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538 $a Mode of access: World Wide Web
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653 $a Deep learning techniques
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