The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision
Open accessAbstract
Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications.
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Cited by
Papadopoulos, C., Kollias, K.-F., & Fragulis, G. F. (2024) · Future Internet · doi:10.3390/fi16110415 · Cited by 35
Self-citation
Metadata
- Authors
- Manakitsa, N., Maraslidis, G. S., Moysis, L., & Fragulis, G. F.
- Venue
- Technologies
- Publisher
- MDPI AG
- Published
- 2024-01-23
- Volume
- 12
- Issue
- 2
- Pages
- 15
- ISSN
- 2227-7080 (ISSN-L)
- OpenAlex
- W4391130440
- Cited by
- 286
- References
- 22
- FWCI
- 40.40
- Type
- journal article
- Language
- en
- Open access
- gold
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