Vectorization Method

Vectorization methods convert raster data (like images or maps) into vector graphics, offering compact, resolution-independent representations ideal for editing and scaling. Current research focuses on improving accuracy and efficiency through techniques like region merging, depth ordering of shapes, and the application of machine learning models, including transformers and convolutional neural networks, to optimize shape representation and reduce computational cost. These advancements have significant implications for various fields, enhancing image editing software, enabling efficient map generation, and improving the performance of machine learning algorithms that rely on vectorized data.