Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Morphological image compositing.

Pierre Soille1

  • 1Joint Research Center of the European Commission, Ispra, Italy. Pierre.Soille@jrc.it

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 28, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Differential area profiles: decomposition properties and efficient computation.

IEEE transactions on pattern analysis and machine intelligence·2011
Same author

Constrained connectivity for hierarchical image decomposition and simplification.

IEEE transactions on pattern analysis and machine intelligence·2008
See all related articles

This study introduces a novel image compositing method using mathematical morphology to seamlessly blend images. The technique places seams along image structures, reducing visibility and minimizing artifacts like cloud cover in satellite imagery.

Area of Science:

  • Computer Vision
  • Image Processing
  • Remote Sensing

Background:

  • Image mosaicking combines multiple images into a single mosaic.
  • Image compositing selects unique pixel values where images overlap.
  • Current methods may struggle with visible seams or transient objects.

Purpose of the Study:

  • To propose a novel image compositing procedure.
  • To minimize the visibility of seams in image mosaics.
  • To seamlessly remove transient objects in overlapping image regions.

Main Methods:

  • Utilizing mathematical morphology and marker-controlled segmentation.
  • Developing a selection rule for unique pixel values in overlapping areas.
  • Applying the methodology to satellite image composition, focusing on cloud cover minimization.

Related Experiment Videos

Main Results:

  • Demonstrated seam placement along salient image structures.
  • Reduced seam visibility without radiometric corrections or blending.
  • Successfully minimized transient objects in overlapping regions.
  • Illustrated effectiveness in satellite image mosaicking with cloud cover.

Conclusions:

  • The proposed mathematical morphology-based compositing method effectively reduces seam visibility.
  • The technique seamlessly handles transient objects in overlapping image areas.
  • This approach offers an efficient solution for satellite image composition, particularly for minimizing cloud cover.