2 Dimensional Segmentation

Two-dimensional (2D) image segmentation aims to partition images into meaningful regions based on pixel-level classifications, a fundamental task in computer vision with applications across diverse fields. Current research emphasizes improving the accuracy and robustness of segmentation models, particularly focusing on probabilistic approaches for uncertainty quantification and leveraging 2D segmentation for 3D tasks through techniques like multi-view fusion and projection from rendered images. This work is significant because improved 2D segmentation directly benefits applications such as medical image analysis, autonomous driving, and agricultural monitoring by enabling more accurate and reliable object detection and scene understanding.