Big snapshot stitching with scarce overlap

Alexandros Stavros Iliopoulos, Jun Hu, Nikos Pitsianis, Xiaobai Sun, Michael Gehm, David Brady

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


We address certain properties that arise in gigapixel-scale image stitching for snapshot images captured with a novel micro-camera array system, AWARE-2. This system features a greatly extended field of view and high optical resolution, offering unique sensing capabilities for a host of important applications. However, three simultaneously arising conditions pose a challenge to existing approaches to image stitching, with regard to the quality of the output image as well as the automation and efficiency of the image composition process. Put simply, they may be described as the sparse, geometrically irregular, and noisy (S.I.N.) overlap amongst the fields of view of the constituent micro-cameras. We introduce a computational pipeline for image stitching under these conditions, which is scalable in terms of complexity and efficiency. With it, we also substantially reduce or eliminate ghosting effects due to misalignment factors, without entailing manual intervention. Our present implementation of the pipeline leverages the combined use of multicore and GPU architectures. We present experimental results with the pipeline on real image data acquired with AWARE-2.

Original languageEnglish (US)
Title of host publication2013 IEEE High Performance Extreme Computing Conference, HPEC 2013
PublisherIEEE Computer Society
ISBN (Print)9781479913657
StatePublished - 2013
Externally publishedYes
Event2013 IEEE High Performance Extreme Computing Conference, HPEC 2013 - Waltham, MA, United States
Duration: Sep 10 2013Sep 12 2013

Publication series

Name2013 IEEE High Performance Extreme Computing Conference, HPEC 2013


Conference2013 IEEE High Performance Extreme Computing Conference, HPEC 2013
Country/TerritoryUnited States
CityWaltham, MA

ASJC Scopus subject areas

  • Software


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