Scalable Seams for Gigapixel Panoramas

Sujin Philip (1) , Brian Summa (1) , Julien Tierny (2) , Peer-Timo Bremer (3) , Valerio Pascucci (1)

1 SCI Institute - Scientific Computing and Imaging Institute
2 Télécom ParisTech
3 LLNL - Lawrence Livermore National Laboratory

Gigapixel panoramas are an increasingly popular digital image application. They are often created as a mosaic of smaller images composited into a larger single image. The mosaic acquisition can occur over many hours causing the individual images to differ in exposure and lighting conditions. Therefore, to give the appearance of a single seamless image a blending operation is necessary. The quality of this blending depends on the magnitude of discontinuity along the boundaries between the images. Often image boundaries, or seams, are first computed to minimize this transition. Current techniques based on the multi-labeling Graph Cuts method are too slow and memory intensive for panoramas many gigapixels in size. In this paper we present a multithreaded out-of-core seam computing technique that is fast, has a small memory footprint, and gives near perfect scaling up to the number of physical cores of our test system. With this method the time required to compute image boundaries for gigapixel imagery improves from many hours (or even days) to just a few minutes on commodity hardware while still producing boundaries with energy that is on-par, if not better, than Graph Cuts.

Domaines: Informatique [cs]

Sujin Philip, Brian Summa, Julien Tierny, Peer-Timo Bremer, Valerio Pascucci. Scalable Seams for Gigapixel Panoramas. Eurographics Symposium on Parallel Graphics and Visualization, May 2013, Girona, Spain. ⟨hal-01206243⟩