Jove
Visualize
Contact Us

Related Experiment Videos

Image segmentation using a texture gradient based watershed transform.

Paul R Hill1, C Nishan Canagarajah, David R Bull

  • 1University of Bristol, Bristol BS5 6JF, UK. paul.hill@bristol.ac.uk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
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

Narrative predicts cardiac synchrony in audiences.

Scientific reports·2024
Same author

BVI-VFI: A Video Quality Database for Video Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2023
Same author

Denoising imaging polarimetry by adapted BM3D method.

Journal of the Optical Society of America. A, Optics, image science, and vision·2018
Same author

Fixation Prediction and Visual Priority Maps for Biped Locomotion.

IEEE transactions on cybernetics·2017
Same author

Approximate Message Passing Reconstruction of Quantitative Acoustic Microscopy Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2017
Same author

Gaze location prediction for broadcast football video.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2013
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles
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

This study introduces a novel texture gradient method to improve image segmentation for content-based retrieval. The new approach enhances the watershed transform for better analysis of textured regions.

Area of Science:

  • Computer Vision
  • Image Analysis
  • Signal Processing

Background:

  • Image segmentation is crucial for image analysis and content-based retrieval.
  • The watershed transform is a common segmentation technique but struggles with perceptually homogeneous textured regions.

Purpose of the Study:

  • To introduce a novel method for segmenting perceptually homogeneous textured image regions.
  • To improve content-based image retrieval by enhancing segmentation accuracy.

Main Methods:

  • Introduced the concept of a "texture gradient" for segmentation.
  • Extracted texture information and its gradient using a non-decimated complex wavelet transform.
  • Employed a novel marker location algorithm and a marker-driven watershed transform.

Related Experiment Videos

Main Results:

  • The combined algorithm effectively segments regions based on both texture and intensity.
  • Demonstrated improved segmentation for textured image regions.

Conclusions:

  • The proposed texture gradient-based watershed segmentation effectively addresses limitations of traditional methods.
  • The enhanced segmentation is suitable for content-based image retrieval applications.