Real Time Segmentation

Real-time image segmentation aims to rapidly and accurately identify and delineate objects or regions of interest within images or video streams. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and transformer architectures, often combined in hybrid models to optimize both speed and accuracy. These advancements are driving progress in diverse fields, including medical imaging (e.g., tumor and vessel segmentation), autonomous systems (e.g., object recognition for navigation and robotics), and environmental monitoring (e.g., wildfire detection). The ability to perform such segmentation in real-time enables immediate feedback and control, significantly impacting applications requiring rapid analysis of visual data.