Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Scene Understanding and Contextual Reasoning: Analyzing scene understanding and contextual reasoning techniques for interpreting complex scenes and understanding spatial relationships

Dr. Jean-Pierre Berger
Associate Professor of Artificial Intelligence, Université Claude Bernard Lyon 1, France
Cover

Published 20-09-2022

Keywords

  • Scene understanding,
  • Contextual reasoning

How to Cite

[1]
Dr. Jean-Pierre Berger, “Scene Understanding and Contextual Reasoning: Analyzing scene understanding and contextual reasoning techniques for interpreting complex scenes and understanding spatial relationships”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 68–79, Sep. 2022, Accessed: Nov. 22, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/118

Abstract

Scene understanding and contextual reasoning play crucial roles in computer vision systems, enabling them to interpret complex scenes and understand spatial relationships. This paper provides a comprehensive analysis of various techniques and approaches in these areas. We discuss the challenges associated with scene understanding, such as occlusions, scale variations, and semantic ambiguities, and how contextual reasoning can help address these challenges. We also explore the use of deep learning models, graphical models, and probabilistic reasoning for scene understanding. Additionally, we discuss the importance of context in interpreting scenes and present examples of applications that benefit from improved scene understanding and contextual reasoning.

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References

  1. Prabhod, Kummaragunta Joel. "ANALYZING THE ROLE OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES IN IMPROVING PRODUCTION SYSTEMS." Science, Technology and Development 10.7 (2021): 698-707.
  2. Sadhu, Amith Kumar Reddy, and Ashok Kumar Reddy Sadhu. "Fortifying the Frontier: A Critical Examination of Best Practices, Emerging Trends, and Access Management Paradigms in Securing the Expanding Internet of Things (IoT) Network." Journal of Science & Technology 1.1 (2020): 171-195.
  3. Tatineni, Sumanth, and Karthik Allam. "Implementing AI-Enhanced Continuous Testing in DevOps Pipelines: Strategies for Automated Test Generation, Execution, and Analysis." Blockchain Technology and Distributed Systems 2.1 (2022): 46-81.
  4. Pulimamidi, Rahul. "Emerging Technological Trends for Enhancing Healthcare Access in Remote Areas." Journal of Science & Technology 2.4 (2021): 53-62.
  5. Perumalsamy, Jegatheeswari, Chandrashekar Althati, and Lavanya Shanmugam. "Advanced AI and Machine Learning Techniques for Predictive Analytics in Annuity Products: Enhancing Risk Assessment and Pricing Accuracy." Journal of Artificial Intelligence Research 2.2 (2022): 51-82.
  6. Devan, Munivel, Lavanya Shanmugam, and Chandrashekar Althati. "Overcoming Data Migration Challenges to Cloud Using AI and Machine Learning: Techniques, Tools, and Best Practices." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 1-39.
  7. Althati, Chandrashekar, Bhavani Krothapalli, and Bhargav Kumar Konidena. "Machine Learning Solutions for Data Migration to Cloud: Addressing Complexity, Security, and Performance." Australian Journal of Machine Learning Research & Applications 1.2 (2021): 38-79.
  8. Sadhu, Ashok Kumar Reddy, and Amith Kumar Reddy. "A Comparative Analysis of Lightweight Cryptographic Protocols for Enhanced Communication Security in Resource-Constrained Internet of Things (IoT) Environments." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 121-142.
  9. Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.