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Image Stitching: Exploring Practices, Software and Performance, D.Williams & P. D. Burns
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Image Stitching: Exploring Practices, Software and Performance, D.Williams & P. D. Burns

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Presentation by Don Williams at IS&T's Archiving 2013 Conference

Presentation by Don Williams at IS&T's Archiving 2013 Conference

Published in: Technology, Art & Photos

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  • 1. Image Stitching: Exploring Practices, Software and Performance DON WILLIAMS; IMAGE SCIENCE ASSOCIATES; WILLIAMSON, NY/US, PETER D. BURNS; BURNS DIGITAL IMAGING; FAIRPORT, NY/USIS&T’s Archiving 2013 Conference, Washington DC April 2013
  • 2. Image Stitching The merging of separate, neighboring digital images of portions of an object into a single, larger digital object. Requires integration of both spatial and luminance image information. Identified under FADGI gap analysis Increased popularity Are the results as analytically accurate as they appear ?
  • 3. Categories High total ownership  Research institutes, restoration studios, galleries, museums, collectors, auction houses  Step-and-repeat robotics: SatScan™ Art, ResolutionArt, Google Art  Well characterized imaging performance, and mechanical constraints  High value objects Affordable COTS hardware and software  Institutional libraries, small collections, service bureaus  COTS hardware and/or software  Less calibrated systems, demanding productivity, challenging and varied content.
  • 4. Typical Stitching Workflow using COTS resources Object identified, mechanically constrained and scan parameters selected Multiple captures performed  Manual or mechanical translation  6 - 30 separate captures Images uploaded to servers or dedicated computer Into the software sausage factory  Results QC’d  Redo with new approaches or software parameters if unacceptable Manually edit in image editors Set limits on time/image Save and move on
  • 5. Typical Stitching Software Operation Align – ( seam carving, content aware resize)  Identify approximate relative location of the component images  Identify corresponding features in overlap areas  Select stitching boundaries and margins  Correct for distortion, perspective, intensity differences. Merge  Combine image tiles and create boundaries
  • 6. COTS Software ? Choices are overwhelming Developed as creative tools (edit vs. calibrate ?) Usually yield visually pleasing results but … Pshop Photomerge, Autopano, PTGui Ease of use –  Few excellent results vs. many good ones ?  How many choices do you need ?
  • 7. Good News, Bad News
  • 8. Synthetic Stitching Experiment
  • 9. Before Stitching
  • 10. After Stitching
  • 11. Steps in Modern Stitching Operations
  • 12. Low Energy Seam Carving Boundary Path (PhotoShop)
  • 13. Sources of Variability/Errors Lens performance Capture conditions  Overlap  Rotation, flatness  Illumination variability Mechanics Software complexity Computational power and storage Object characteristics Algorithm idiosyncrasies Operator training
  • 14. Error Detection/Prevention/Correction  Detection - Visual cueing features  Alignment - at seam interfaces  Blending – image equalization processing  Prevention & Correction  Good image practices and equipment  Use simple fill and digital cloning tools  Avoid complex operations
  • 15. Tactical Approaches Take an incremental approach Observe and benefit from algorithm idiosyncrasies Archive component tiles for future processing Try it again ! Take care in original capture  Placement, hardware  Reasonable overlap Object Triage ?  Fragile vs. non fragile  Sizes ?
  • 16. Alternative Solutions Large flatbed scanners  Cruse  Zuetschel  I2S Large Sheet Fed scanners  WideTek 36DS, etc.  Contex
  • 17. Conclusions Most Automerge tools do a good first order job, but …… Visually appealing results ≠ Spatially accurate results. Good imaging practices and moderated image processing ( lens and lighting profiles) can reduce geometric distortions significantly. Most errors tend to be due align rather than merge operations. Keep post processing edits simple. Better full reference distortion metrics needed to assess stitching goodness.
  • 18. Gratitudes  Dave Mathews, Image Collective  Northwestern University Stanford University, Green Library  Jeff Chien, Adobe Systems Inc. For more information contact: Don Williams or Peter Burns