Fast and robust superpixel generation method

Superpixel segmentation approach, as a preprocessing step in computer vision tasks, groups pixels into perceptually coherence atomic regions to replace the pixel grid in images and reduce the primitives and redundancy of subsequent works. In this study, the authors proposed a fast and robust superpixel generation method based on non-iterative framework with the constraint of linear path. They collected neighbouring pixels as the initial superpixels one by one in the conventional order at first. To make the superpixels attach to the most object boundaries well and robust to noise, they defined a new distance measurement between pixels and superpixel seeds by considering the colour difference of pixels in the neighbourhood and along the linear path from the pixel to the seed. Meanwhile, they proposed a new way to set parameters adaptively based on the intrinsic quality of images. Then, they refined the initial superpixels by merging the smallest ones until the number of superpixel meets the expectation. The experimental results on clean and noisy images demonstrate that the proposed method is effective and presents a competitive performance in computational efficiency with the state-of-the-art real-time method.