Artificial intelligence (AI) is revolutionizing computer vision and imaging tasks across fields like medical imaging, autonomous vehicles, and remote sensing. In medical imaging, AI enables faster, more accurate diagnosis through advanced image analysis and pattern recognition. Autonomous systems leverage AI for precise object detection and navigation in dynamic environments. Key AI algorithms, such as deep learning and convolutional neural networks (CNNs), drive these advancements by automating feature extraction and enhancing image processing capabilities. The evolving demands of computer vision tasks, in turn, push AI models to be more adaptive and efficient. This synergy accelerates progress in both AI and vision technologies, unlocking new levels of automation and insight.
The goal of this Research Topic is to showcase cutting-edge research and advancements at the intersection of artificial intelligence (AI) and imaging technologies. By highlighting innovative applications in fields such as computational imaging, medical imaging, and optical systems, this issue explores how AI-driven solutions are transforming such tasks. It focuses on the latest breakthroughs in AI algorithms that are expanding the frontiers of imaging and vision capabilities. Additionally, this issue seeks to address the challenges and opportunities of applying AI to real-world scenarios, from medical diagnostics to autonomous systems and remote sensing, and how these technologies enhance precision, efficiency, and adaptability in such applications. Ultimately, the research topic aims to inspire further innovation and collaboration between AI and imaging science communities.
The scope of this research topic encompasses the intersection of artificial intelligence (AI) and imaging technologies, focusing on both theoretical advancements and practical applications. The interest includes:
1. Computational Imaging: AI techniques for enhancing image acquisition, reconstruction, and processing.
2. Medical Imaging: Applications of AI in diagnostic imaging, including disease detection, segmentation, and treatment planning using advanced AI models.
3. Optical Systems: Integration of AI with optical detection and imaging systems, improving performance in tasks such as object detection, optical sensing, and imaging in low-light or scattering conditions.
4. Complex Environments: Vision systems in challenging environments, such as underwater, space, or highly dynamic scenarios, leveraging AI for improved adaptability and decision-making.
5. Artificial Intelligence and Machine Learning: Development and application of deep learning, neural networks, and other AI models for vision tasks, including image classification, feature extraction, and anomaly detection.
6. Computer Vision in Real-World Applications: AI-powered vision systems for autonomous vehicles, robotics, surveillance, and environmental monitoring.
This topic encourages contributions that explore the synergy between AI algorithms and advanced imaging tasks, aiming to push the boundaries of innovation in these fields.
Keywords:
Computational Imaging, Optical Systems, Complex Environments, Artificial Intelligence, Computer Vision, AI in Medical Imaging, Advanced Optical Detection, AI-Enhanced Imaging Technologies
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Artificial intelligence (AI) is revolutionizing computer vision and imaging tasks across fields like medical imaging, autonomous vehicles, and remote sensing. In medical imaging, AI enables faster, more accurate diagnosis through advanced image analysis and pattern recognition. Autonomous systems leverage AI for precise object detection and navigation in dynamic environments. Key AI algorithms, such as deep learning and convolutional neural networks (CNNs), drive these advancements by automating feature extraction and enhancing image processing capabilities. The evolving demands of computer vision tasks, in turn, push AI models to be more adaptive and efficient. This synergy accelerates progress in both AI and vision technologies, unlocking new levels of automation and insight.
The goal of this Research Topic is to showcase cutting-edge research and advancements at the intersection of artificial intelligence (AI) and imaging technologies. By highlighting innovative applications in fields such as computational imaging, medical imaging, and optical systems, this issue explores how AI-driven solutions are transforming such tasks. It focuses on the latest breakthroughs in AI algorithms that are expanding the frontiers of imaging and vision capabilities. Additionally, this issue seeks to address the challenges and opportunities of applying AI to real-world scenarios, from medical diagnostics to autonomous systems and remote sensing, and how these technologies enhance precision, efficiency, and adaptability in such applications. Ultimately, the research topic aims to inspire further innovation and collaboration between AI and imaging science communities.
The scope of this research topic encompasses the intersection of artificial intelligence (AI) and imaging technologies, focusing on both theoretical advancements and practical applications. The interest includes:
1. Computational Imaging: AI techniques for enhancing image acquisition, reconstruction, and processing.
2. Medical Imaging: Applications of AI in diagnostic imaging, including disease detection, segmentation, and treatment planning using advanced AI models.
3. Optical Systems: Integration of AI with optical detection and imaging systems, improving performance in tasks such as object detection, optical sensing, and imaging in low-light or scattering conditions.
4. Complex Environments: Vision systems in challenging environments, such as underwater, space, or highly dynamic scenarios, leveraging AI for improved adaptability and decision-making.
5. Artificial Intelligence and Machine Learning: Development and application of deep learning, neural networks, and other AI models for vision tasks, including image classification, feature extraction, and anomaly detection.
6. Computer Vision in Real-World Applications: AI-powered vision systems for autonomous vehicles, robotics, surveillance, and environmental monitoring.
This topic encourages contributions that explore the synergy between AI algorithms and advanced imaging tasks, aiming to push the boundaries of innovation in these fields.
Keywords:
Computational Imaging, Optical Systems, Complex Environments, Artificial Intelligence, Computer Vision, AI in Medical Imaging, Advanced Optical Detection, AI-Enhanced Imaging Technologies
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.