Sensor Data Fusion Based on Deep Learning for Computer Vision and Medical Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (25 May 2022) | Viewed by 51447
Special Issue Editors
Interests: computer vision; human–computer interaction; biometrics; medical image processing and understanding; artificial intelligence; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: deep learning; semantic segmentation; image classification; medical image analysis; computer-aided diagnosis (CAD); biometrics (finger vein and iris segmentation)
Special Issues, Collections and Topics in MDPI journals
Interests: medical image analysis; weakly supervised learning; reinforcement learning; computer aided diagnosis (CAD)
Special Issues, Collections and Topics in MDPI journals
Interests: image segmentation; image classification; medical image analysis; biometrics (fingerprints and iris segmentation); deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: database usability; advanced data analytics; graph data management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
It is our pleasure to invite submissions to this Special Issue on “Sensor Data Fusion Based on Deep Learning for Computer Vision and Medical Applications”.
Recent advancements have led to the extensive use of various sensors, such as visible light, near-infrared (NIR), thermal camera sensors, fundus cameras, H&E stains, endoscopy, OCT cameras, and magnetic resonance imaging sensors, in a variety of applications in computer vision, biometrics, video surveillance, image compression and image restoration, medical image analysis, computer-aided diagnosis, etc. Research related to sensor and data fusion, information processing and merging, and fusion architecture for the cooperative perception and risk assessment is needed for computer vision and medical applications. Indeed, prior to ensuring a high level of accuracy in the deployment of computer vision and deep learning applications, it is necessary to guarantee high-quality and real-time perception mechanisms. While computer vision technology has matured, its performance is still affected by various environmental factors, and recent approaches have been attempted to fuse data from various sensors based on deep learning techniques to guarantee higher accuracy. The objective of this Special Issue is to invite high-quality, state-of-the-art research papers that deal with challenging issues in deep-learning-based computer vision and medical applications. We solicit original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal. Topics of interest include, but are not limited to, the following:
- Computer vision by various camera sensors;
- Biometrics and spoof detection by various camera sensors;
- Image classification using various, NIR, VL camera sensors;
- Detection and localization by deep learning by various cameras;
- Deep-learning-based object segmentation/instance segmentation by media sensors;
- Medical image processing and analysis by various camera sensors;
- Deep learning by various camera sensors;
- Multiple-approach fusion that combines deep learning techniques and conventional methods on images obtained by various camera sensors.
Dr. Rizwan Ali Naqvi
Dr. Muhammad Arsalan
Dr. Talha Qaiser
Dr. Tariq Mahmood Khan
Dr. Imran Razzak
Guest Editors
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Keywords
- Sensor data fusion
- Image processing
- Deep feature fusion
- Image/video-based classification
- Semantic segmentation/instance segmentation
- Medical image analysis
- Computer-aided diagnosis
- Computer vision
- Fusion for biometrics
- Fusion for medical applications
- Fusion for semantic information
- Smart sensors
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