DON WILLIAMS; IMAGE SCIENCE ASSOCIATES; WILLIAMSON, NY/US,

PETER D. BURNS;

BURNS DIGITAL IMAGING; FAIRPORT, NY/US

Image Stitching: Exploring Practices, Software and Performance

IS&T’s Archiving 2013 Conference, Washington DC April 2013

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 ?

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.

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

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

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 ?

Good News, Bad News

Synthetic Stitching Experiment

Before Stitching

After Stitching

Steps in Modern Stitching Operations

Low Energy Seam Carving Boundary Path (PhotoShop)

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

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

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 ?

Alternative Solutions

Large flatbed scanners Cruse Zuetschel I2S

Large Sheet Fed scanners WideTek 36DS, etc. Contex

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.

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