Enhancing Monocular Depth Estimation via Image Pre-processing Techniques

Miswar Syed; Abdulrahman Javaid; Asaad Alduais; M. Hashem Shullar; Uthman Baroudi; Mustafa Alnasser

doi:10.1109/CICN56167.2022.10008288

Enhancing Monocular Depth Estimation via Image Pre-processing Techniques

Miswar Syed, Abdulrahman Javaid, Asaad Alduais, M. Hashem Shullar, Uthman Baroudi^*, Mustafa Alnasser

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Robots and drones are getting popular in many applications nowadays. Autonomous operations of drones and robots are highly desirable to minimize human interventions and enhance operation efficiency. However, there are several challenges that need to be overcome before robots and drones can be automated with minimum hardware requirements. Currently, robotics industry employs costly sensors such as Lidar to estimate distance between a vehicle and objects. Recent advancement in Artificial Intelligence (AI) encouraged researcher to investigate techniques to estimate the distance between vehicle and objects using monocular camera and AI. However, distance (depth) estimation using monocular camera still suffers from low accuracy rate in depth estimation. This paper aims to improve the depth estimation values through applying several image pre-processing techniques such as Nonuniform Illumination Removal, Local Adaptive Thresholding, Histogram Equalization, Adaptive Histogram Equalization, White Balance, and Homo- morphic filtering techniques.

Original language	English
Title of host publication	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	139-143
Number of pages	5
ISBN (Electronic)	9781665487719
DOIs	https://doi.org/10.1109/CICN56167.2022.10008288
State	Published - 2022
Event	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 - Al-Khobar, Saudi Arabia Duration: 4 Dec 2022 → 6 Dec 2022

Publication series

Name	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

Conference

Conference	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Country/Territory	Saudi Arabia
City	Al-Khobar
Period	4/12/22 → 6/12/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Drones
Image pre- processing
Internet of Drones
Monocular depth estimation

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/CICN56167.2022.10008288

Cite this

Syed, M., Javaid, A., Alduais, A., Shullar, M. H., Baroudi, U., & Alnasser, M. (2022). Enhancing Monocular Depth Estimation via Image Pre-processing Techniques. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 (pp. 139-143). (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN56167.2022.10008288

Syed, Miswar ; Javaid, Abdulrahman ; Alduais, Asaad et al. / Enhancing Monocular Depth Estimation via Image Pre-processing Techniques. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 139-143 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

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abstract = "Robots and drones are getting popular in many applications nowadays. Autonomous operations of drones and robots are highly desirable to minimize human interventions and enhance operation efficiency. However, there are several challenges that need to be overcome before robots and drones can be automated with minimum hardware requirements. Currently, robotics industry employs costly sensors such as Lidar to estimate distance between a vehicle and objects. Recent advancement in Artificial Intelligence (AI) encouraged researcher to investigate techniques to estimate the distance between vehicle and objects using monocular camera and AI. However, distance (depth) estimation using monocular camera still suffers from low accuracy rate in depth estimation. This paper aims to improve the depth estimation values through applying several image pre-processing techniques such as Nonuniform Illumination Removal, Local Adaptive Thresholding, Histogram Equalization, Adaptive Histogram Equalization, White Balance, and Homo- morphic filtering techniques.",

keywords = "Drones, Image pre- processing, Internet of Drones, Monocular depth estimation",

author = "Miswar Syed and Abdulrahman Javaid and Asaad Alduais and Shullar, {M. Hashem} and Uthman Baroudi and Mustafa Alnasser",

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year = "2022",

doi = "10.1109/CICN56167.2022.10008288",

language = "English",

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Syed, M, Javaid, A, Alduais, A, Shullar, MH, Baroudi, U & Alnasser, M 2022, Enhancing Monocular Depth Estimation via Image Pre-processing Techniques. in Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Institute of Electrical and Electronics Engineers Inc., pp. 139-143, 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Al-Khobar, Saudi Arabia, 4/12/22. https://doi.org/10.1109/CICN56167.2022.10008288

Enhancing Monocular Depth Estimation via Image Pre-processing Techniques. / Syed, Miswar; Javaid, Abdulrahman; Alduais, Asaad et al.
Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 139-143 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Alnasser, Mustafa

PY - 2022

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AB - Robots and drones are getting popular in many applications nowadays. Autonomous operations of drones and robots are highly desirable to minimize human interventions and enhance operation efficiency. However, there are several challenges that need to be overcome before robots and drones can be automated with minimum hardware requirements. Currently, robotics industry employs costly sensors such as Lidar to estimate distance between a vehicle and objects. Recent advancement in Artificial Intelligence (AI) encouraged researcher to investigate techniques to estimate the distance between vehicle and objects using monocular camera and AI. However, distance (depth) estimation using monocular camera still suffers from low accuracy rate in depth estimation. This paper aims to improve the depth estimation values through applying several image pre-processing techniques such as Nonuniform Illumination Removal, Local Adaptive Thresholding, Histogram Equalization, Adaptive Histogram Equalization, White Balance, and Homo- morphic filtering techniques.

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Syed M, Javaid A, Alduais A, Shullar MH, Baroudi U, Alnasser M. Enhancing Monocular Depth Estimation via Image Pre-processing Techniques. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 139-143. (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). doi: 10.1109/CICN56167.2022.10008288

Enhancing Monocular Depth Estimation via Image Pre-processing Techniques

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this