Recent Trends in Computer Vision and Pattern Recognition for Optical and SAR Images

Dear Colleagues,

Computer vision and pattern recognition are fields of artificial intelligence that aim to enable machines to interpret and understand visual information. In recent years, these fields have seen significant progress, driven by advances in deep learning and the availability of large datasets. Computer vision involves the extraction of information from digital images and videos, while pattern recognition is concerned with the identification of patterns and regularities in data. Some of the emerging trends in computer vision and pattern recognition include the use of generative models for image and video synthesis, change detection, edge detection, attention mechanisms to improve performance in tasks such as object detection and segmentation, transfer learning to reduce the amount of data and computation required for training, explainable AI for providing insights into the decision-making process of models, and multi-modal learning to improve performance in tasks that involve data from multiple modalities. It is evident, that the efficiency of modern imaging systems is closely related with incorporated hardware and software solutions. The fast development of signal processing units and computational platforms allows to integrate sophisticated signal and image processing methods into the existing systems and even to obtain a lot of results in real-time. In particular, the techniques related with computer vision have a very high potential due to a theoretical possibility to make the radar systems almost fully automatic. The growing availability of large amounts of imaging data in the form of 2D images (aerial, satellite, etc.), point clouds, hyperspectral images (HSIs), as well as in the form of image time-series, sets the stage for the design of various deep learning (DL)-based strategies, as well as of standard descriptor-based computer vision approaches.

Dr. Devendhar Gogula

Dr. Aditya Kumar Verma

Guest Editors