Image Space

Image space research focuses on analyzing and manipulating images to extract meaningful information or achieve specific goals, such as predicting 3D structures from 2D images or estimating forces in surgical procedures. Current work emphasizes developing robust models, often employing convolutional neural networks and incorporating techniques like differentiable global positioning and frequency domain analysis, to address challenges like occlusion, scale ambiguity, and noise propagation. These advancements have significant implications for various applications, including human-computer interaction, medical imaging, and security systems, by improving the accuracy and reliability of image-based analyses and predictions.