Machine Learning in Computer Vision and Image Sensing: Theory and Applications

Dear Colleagues,

Machine learning models have seen tremendous progress over the past decade, with hugely diverse application domains such as Earth observation, spatial computing, natural hazard and environmental modeling, medical imaging, and many more. Computer vision and image sensing area have benefitted immensely from machine learning applications to solve complex and real-world challenges. Advances in machine learning enable us to better analyse image and sensor data. This has driven enormous research endeavours towards solving practical problems in multiple application domain areas including healthcare, agriculture, defence, remote sensing, Earth observation, autonomous navigation, etc. This Special Issue aims to collate a compendium of top-quality research works addressing the broad challenges in both theoretical and application aspects of advanced machine learning models in computer vision and image sensing areas.

The key topics of interest include (but are not limited to):

• Machine learning theory for computer vision and image sensing:
• Developing machine learning models
• Data pre-processing, classification, detection, segmentation
• Managing high resolution data
• Model explainability and bias handling
• Machine learning application for computer vision and image sensing:
• Healthcare and biomedical imaging
• Remote sensing and satellite imaging in earth and environmental modelling
• Multimodal (image + other data) machine learning
• Other emerging applications

Dr. Subrata Chakraborty
Prof. Dr. Biswajeet Pradhan
Guest Editors

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