TurboPixels is a fast algorithm for computing superpixels, which are dense, compact regions in an image that respect local boundaries. The algorithm uses geometric flows to efficiently generate superpixels with uniform size and shape, and it is significantly faster than existing methods like N-Cuts. It is designed to handle large images with high superpixel densities in minutes. The algorithm is tested on the Berkeley database and compared to other oversegmentation algorithms, showing it produces less undersegmentation and runs faster than N-Cuts. TurboPixels segments images into compact regions by dilating seeds and using a level-set method to prevent merging. It ensures superpixels are compact, connected, and do not overlap. The algorithm is efficient, with complexity roughly linear in image size, and is suitable for large-scale images. It outperforms other methods in terms of speed and accuracy, making it a valuable tool for image segmentation tasks.TurboPixels is a fast algorithm for computing superpixels, which are dense, compact regions in an image that respect local boundaries. The algorithm uses geometric flows to efficiently generate superpixels with uniform size and shape, and it is significantly faster than existing methods like N-Cuts. It is designed to handle large images with high superpixel densities in minutes. The algorithm is tested on the Berkeley database and compared to other oversegmentation algorithms, showing it produces less undersegmentation and runs faster than N-Cuts. TurboPixels segments images into compact regions by dilating seeds and using a level-set method to prevent merging. It ensures superpixels are compact, connected, and do not overlap. The algorithm is efficient, with complexity roughly linear in image size, and is suitable for large-scale images. It outperforms other methods in terms of speed and accuracy, making it a valuable tool for image segmentation tasks.