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IEEE-ICM-SVECOM 2021 : IEEE-ICM-Special Session on Signal, Vision and Edge Computing

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Link: http://icmieee2021.org
 
When Dec 19, 2021 - Dec 22, 2021
Where Cairo
Submission Deadline Oct 30, 2021
Categories    signal processing   image processing   computer vision   edge computing
 

Call For Papers

Signal, Vision and Edge Computing
Signal, image, and video processing are fundamental research as well as industrial problems. The traditional challenges are related to the computing complexity of the algorithms and the availability and amount of input data. Today with the shift toward crowd sourcing and edge computing new challenges as well as opportunities are added to the scene. Via Internet of Things (IoT) technology cameras and other sensors are connected directly to computing devices, and deliver processed data and information to the network. This opened the doors for many new applications. Such as autonomous and connected vehicles, security and safety via video surveillance for smart cities, smart agriculture, quality control and automation in industry 4.0, connected healthcare, and many other applications. However handling and processing the data is changing, requesting new algorithms, evaluation metrics and frameworks.
In this session we invite researchers to submit original work and reviews in topics close to algorithms and applications of signal and image processing on edge devices. The topics of interest include (but are not limited to):

• Advances in Signal Processing and Computer Vision
• Object Detection, Recognition and Tracking
• Scene Understanding, and Annotation
• Computer Vision for Robotics
• Computer Vision for Safety and Security
• Application of Computer Vision for Autonomous and Connected Vehicles
• Application of Computer Vision for Industry 4.0, Smart Cities, Intelligent Transportation.
• Application of Signal Processing and Computer Vision for Connected Healthcare • Embedded AI on Edge Devices
• Computer Vision & IoT
• Cloud Services for Computer Vision
• Video Surveillance on Edge Devices
• Cooperative and distributed learning for smart devices
• Federated Learning on Distributed Edge Computing
• Split Learning on Edge and Cloud Computing
• Edge Computing Security and Data Privacy

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