The paper introduces TurboPixels, a geometric-flow-based algorithm for computing dense oversegmentation of images, known as superpixels. The algorithm aims to produce segments that respect local image boundaries while limiting undersegmentation through a compactness constraint. It is designed to be very fast, with complexity approximately linear in image size, making it suitable for megapixel-sized images with high superpixel densities. The authors demonstrate high-quality results on complex images and compare the algorithm's performance to other oversegmentation algorithms using the Berkeley database. TurboPixels outperforms algorithms like N-Cuts in terms of undersegmentation and offers significant speed improvements over them. The paper also discusses the theoretical foundations, implementation details, and experimental results, highlighting the algorithm's efficiency and effectiveness in handling large images and achieving high-quality superpixel segmentation.The paper introduces TurboPixels, a geometric-flow-based algorithm for computing dense oversegmentation of images, known as superpixels. The algorithm aims to produce segments that respect local image boundaries while limiting undersegmentation through a compactness constraint. It is designed to be very fast, with complexity approximately linear in image size, making it suitable for megapixel-sized images with high superpixel densities. The authors demonstrate high-quality results on complex images and compare the algorithm's performance to other oversegmentation algorithms using the Berkeley database. TurboPixels outperforms algorithms like N-Cuts in terms of undersegmentation and offers significant speed improvements over them. The paper also discusses the theoretical foundations, implementation details, and experimental results, highlighting the algorithm's efficiency and effectiveness in handling large images and achieving high-quality superpixel segmentation.