GPU-accelerated Convex Multi-phase Image Segmentation
Subrahmanyam Gorthi, Arnaud Le Carvennec, Hadrien Copponnex, Xavier Bresson, Jean‐Philippe Thiran · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2011
Image segmentation is a key area of research in computer vision. Recent advances facilitated reformulation of the non-convex multi-phase segmentation problem as a convex optimization problem (see for example [2, 4, 9, 10, 13, 16]). Recently, [3] proposed a new convex relaxation approach for a class of vector-valued minimization problems, and this approach is directly applicable to the widely used classi-cal Mumford-Shah segmentation model [11]. While the ap-proach in [3] provides the much deserved convexification, it achieves this at the expense of an increased computational complexity due to the increased dimensionality of the refor-mulated problem; however, the algorithm proposed in [3] can indeed profit from a parallelized implementation. In this paper, we present a GPU-based implementation of the convex formulation for Mumford-Shah piecewise constant multi-phase image segmentation algorithm proposed in [3]. The main goal of this paper is to provide insights into the way the algorithm has been parallelized in order to obtain good speedup. We present multi-phase segmentation results both on synthetic and real images. The speedup of GPU-based implementation is evaluated on three different GPUs. For sufficiently large images, the speedup achieved on GTX 285 GPU is around 40, compared to an optimized CPU-implementation. The speedups obtained from GPU-based implementation are quite satisfactory. We also made our CUDA code available online1. 1.