Computer Vision
Computer vision, a field focused on enabling computers to "see" and interpret images and videos, aims to develop algorithms that can perform tasks such as object detection, image classification, and scene understanding. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and vision transformers (ViTs), often combined with techniques like multi-modal fusion (integrating data from different sensors) and transfer learning to improve efficiency and accuracy. These advancements are driving significant progress in diverse applications, including precision agriculture, robotics, medical imaging analysis, and autonomous systems, by providing automated, efficient, and objective solutions to complex visual tasks.
Papers
Deep learning-based approach for tomato classification in complex scenes
Mikael A. Mousse, Bethel C. A. R. K. Atohoun, Cina Motamed
The Machine Vision Iceberg Explained: Advancing Dynamic Testing by Considering Holistic Environmental Relations
Hubert Padusinski, Christian Steinhauser, Thilo Braun, Lennart Ries, Eric Sax
A Survey on Future Frame Synthesis: Bridging Deterministic and Generative Approaches
Ruibo Ming, Zhewei Huang, Zhuoxuan Ju, Jianming Hu, Lihui Peng, Shuchang Zhou