Bin Sulong, Ghazali and Randles, M (2023) Computer Vision Using Pose Estimation. Wasit Journal of Computer and Mathematics Science, 2 (1). pp. 54-58. ISSN 2788-5887

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Abstract

Pose estimation involves estimating the position and orientation of objects in a 3D space, and it has applications in areas such as robotics, augmented reality, and human-computer interaction. There are several methods for pose estimation, including model-based, feature-based, direct, hybrid, and deep learning-based methods. Each method has its own strengths and weaknesses, and the choice of method depends on the specific requirements of the application, object being estimated, and available data. Advancements in computer vision and machine learning have made it possible to achieve high accuracy and robustness in pose estimation, allowing for the development of a wide range of innovative applications. Pose estimation will continue to be an important area of research and development, and we can expect to see further improvements in the accuracy and robustness of pose estimation methods in the future.

Item Type:	Article
Uncontrolled Keywords:	Machine Learning and Artificial Intelligence; Networking and Information Technology R&D (NITRD)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science and Mathematics
Publisher:	Wasit University
SWORD Depositor:	A Symplectic
Date Deposited:	19 Dec 2024 15:49
Last Modified:	19 Dec 2024 16:00
DOI or ID number:	10.31185/wjcm.111
URI:	https://researchonline.ljmu.ac.uk/id/eprint/25143

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