A State-of-the-Art Computer Vision Adopting Non-Euclidean Deep-Learning Models

A distance metric known as non-Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to...

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Bibliographic Details
Published in:International journal of intelligent systems 2023-05, Vol.2023, p.1-33
Main Authors: Chowdhury, Sakib H., Sany, Md. Robius, Ahamed, Md. Hafiz, Das, Sajal K., Badal, Faisal Rahman, Das, Prangon, Tasneem, Zinat, Hasan, Md. Mehedi, Islam, Md. Robiul, Ali, Md. Firoj, Abhi, Sarafat Hussain, Islam, Md. Manirul, Sarker, Subrata Kumar
Format: Article
Language:English
Subjects:
Algorithms
Computer graphics
Computer vision
Deep learning
Euclidean geometry
Geometry
Graphs
Intelligent systems
Machine learning
Neural networks
Optimization techniques
R&D
Research & development
Social networks
Topology
Virtual reality
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Summary:A distance metric known as non-Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to apply deep learning techniques to non-Euclidean domains including graphs, manifolds, and point clouds is made possible by non-Euclidean deep learning. The use of non-Euclidean deep learning is rapidly expanding to study real-world datasets that are intrinsically non-Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non-Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field’s history. Modern state-of-the-art methods have been described briefly and categorized by application fields. It also highlights the model’s shortcomings in tables and graphs and shows different real-world applicability. Overall, this work contributes to a collective information and performance comparison that will help enhance non-Euclidean deep-learning research and development in the future.
ISSN:0884-8173
1098-111X
DOI:10.1155/2023/8674641