Really quick shift: Image segmentation on a GPU

Abstract. The paper presents an exact GPU implementation of the quick shift image segmentation algorithm. Variants of the implementation which use global memory and texture caching are presented, and the paper shows that a method backed by texture caching can produce a 10-50X speedup for practical images, making computation of super-pixels possible at 5-10Hz on modest sized (256x256) images

A Memory and Computation Efficient Sparse Level-Set Method

Since its introduction, the level set method has become the favorite technique for capturing and tracking moving interfaces, and found applications in a wide variety of scientific fields. In this paper we present efficient data structures and algorithms for tracking dynamic interfaces through the level set method. Several approaches which address both computational and memory requirements have been very recently introduced. We show that our method is up to 8.5 times faster than these recent approaches. More importantly, our algorithm can greatly benefit from both fine- and coarse-grain parallelization by leveraging SIMD and/or multi-core parallel architectures.