Version 1
: Received: 29 May 2023 / Approved: 31 May 2023 / Online: 31 May 2023 (03:33:34 CEST)
How to cite:
Xiaoxiao, W.; Beng, N.S.; Wirza bt O. K. Rahmat, R.; Suhaiza binti Sulaiman, P. A Review of Machine Vision Pose Measurement. Preprints2023, 2023052164. https://doi.org/10.20944/preprints202305.2164.v1
Xiaoxiao, W.; Beng, N.S.; Wirza bt O. K. Rahmat, R.; Suhaiza binti Sulaiman, P. A Review of Machine Vision Pose Measurement. Preprints 2023, 2023052164. https://doi.org/10.20944/preprints202305.2164.v1
Xiaoxiao, W.; Beng, N.S.; Wirza bt O. K. Rahmat, R.; Suhaiza binti Sulaiman, P. A Review of Machine Vision Pose Measurement. Preprints2023, 2023052164. https://doi.org/10.20944/preprints202305.2164.v1
APA Style
Xiaoxiao, W., Beng, N.S., Wirza bt O. K. Rahmat, R., & Suhaiza binti Sulaiman, P. (2023). A Review of Machine Vision Pose Measurement. Preprints. https://doi.org/10.20944/preprints202305.2164.v1
Chicago/Turabian Style
Xiaoxiao, W., Rahmita Wirza bt O. K. Rahmat and Puteri Suhaiza binti Sulaiman. 2023 "A Review of Machine Vision Pose Measurement" Preprints. https://doi.org/10.20944/preprints202305.2164.v1
Abstract
This review paper provides a comprehensive overview of machine vision pose measurement algorithms. The paper focuses on the state-of-the-art algorithms and their applications. The paper is structured as follows: The introduction in Section 1 provides a brief overview of the field of machine vision pose measurement. Section 2 describes the commonly used algorithms for machine vision pose measurement. Section 3 discusses the factors that affect the accuracy and reliability of machine vision pose measurement algorithms. Section 4 presents the applications of machine vision pose measurement in various fields. The paper provides specific examples of how machine vision pose measurement is used in each of these fields. Finally, Section 5 summarizes the paper and provides future research directions. The paper highlights the need for more robust and accurate algorithms that can handle varying lighting conditions and occlusion. It also suggests that the integration of machine learning techniques may improve the performance of machine vision pose measurement algorithms. Overall, this review paper provides a comprehensive overview of machine vision pose measurement algorithms, their applications, and the factors that affect their accuracy and reliability. It provides a valuable resource for researchers and practitioners working in the field of computer vision.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.