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Image Stitching: Exploring Practices, Software and Performance, D.Williams & P. D. Burns
1. Image Stitching: Exploring Practices,
Software and Performance
DON WILLIAMS;
IMAGE SCIENCE ASSOCIATES; WILLIAMSON, NY/US,
PETER D. BURNS;
BURNS DIGITAL IMAGING; FAIRPORT, NY/US
IS&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 ?
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