Deep Learning in Video and Image Processing: Challenges, Solutions, and Future Directions
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 January 2025 | Viewed by 241
Special Issue Editors
Interests: deep learning; machine learning; video processing; image processing; Internet of Things; cybersecurity; embedded systems
Special Issues, Collections and Topics in MDPI journals
Interests: automotive electronics; embedded HPC (high-performance computing); enabling technologies IoT (Internet of Things)
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Special Issue “Challenges and Solutions in Real-Time Deep Learning and Machine Learning for Video and Image Processing on Edge Platforms” focuses on advancing the integration of deep learning and machine learning (ML) techniques with video and image processing directly on edge devices. This collection of papers aims to address the unique challenges of executing computationally intensive ML algorithms in real time on resource-constrained devices, such as those with limited processing power, memory, and energy consumption. The purpose is to explore innovative solutions that enhance the efficiency, accuracy, and reliability of ML applications in real-world scenarios. The scope covers a broad spectrum of topics including, but not limited to, algorithm optimization, hardware–software co-design, energy-efficient ML models, and real-time data processing techniques. This Special Issue will significantly contribute to the existing literature by bridging the gap between theoretical ML advancements and practical edge computing implementations. While current research predominantly focuses on cloud-based solutions or offline processing, this Special Issue emphasizes the need for immediate, localized processing, which is crucial for latency-sensitive applications. Examples of real-world applications include surveillance systems that require instant anomaly detection, medical imaging for real-time diagnosis, autonomous vehicles needing immediate object recognition and decision-making, smart cameras in urban traffic management, augmented reality devices for interactive user experiences, industrial automation for monitoring and control, wildlife monitoring for real-time tracking, disaster response systems for rapid situational analysis, smart home devices for enhanced security and convenience, and wearable technology for health monitoring and personalized feedback. By presenting cutting-edge research and practical case studies, this Special Issue will serve as a valuable resource for researchers, engineers, and practitioners aiming to develop and deploy efficient ML solutions on edge platforms, ultimately advancing the field of real-time video and image processing.
Dr. Abdussalam Elhanashi
Prof. Dr. Sergio Saponara
Guest Editors
Manuscript Submission Information
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Keywords
- real-time machine learning
- edge computing
- video processing
- image processing
- algorithm optimization
- hardware–software co-design
- energy-efficient ML
- latency-sensitive applications
- computational efficiency
- resource-constrained devices
- anomaly detection
- real-time diagnostics
- autonomous vehicles
- object recognition
- urban traffic management
- augmented reality
- industrial automation
- wildlife monitoring
- disaster response systems
- smart home devices
- wearable technology
- health monitoring
- personalized feedback
- real-time data processing
- ML model deployment
- edge AI
- smart cameras
- low-power computing
- data privacy
- real-world ML applications
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