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

Adaptive scale fixing for multiscale texture segmentation.

Kung-Hao Liang1, Tardi Tjahjadi

  • 1School of Engineering, University of Warn Coventry CV4 7AL, UK.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 27, 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

A Deep Learning Prognostic Model for Diabetes Patients Using Bilateral Fundus Imaging.

Journal of diabetes science and technology·2026
Same author

Population-specific polygenic risk scores for people of Han Chinese ancestry.

Nature·2025
Same author

Deep neural network-based detection of lead contamination via Förster resonance energy transfer in live cells.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2025
Same author

Discovery of TBK1-Associated Oncogenic Mechanisms in Patients With Upper Tract Urothelial Carcinoma and Pre-Existing End-Stage Renal Disease.

Clinical genitourinary cancer·2025
Same author

Multimodal single-cell transcriptomics with patient-specific iPSC-derived airway organoids as a drug screening approach for cystic fibrosis with nonsense mutations.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2025
Same author

Predictive biosignatures for hospitalization in patients with virologically confirmed COVID-19.

Journal of the Chinese Medical Association : JCMA·2024
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

This study introduces a novel multiscale texture segmentation method. It adaptively uses spatial and feature resolutions for improved unsupervised image analysis and texture boundary detection.

Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Unsupervised texture segmentation faces challenges in determining optimal spatial and feature resolutions.
  • Integrating information across multiple scales is crucial for accurate segmentation.

Purpose of the Study:

  • To develop an unsupervised multiscale texture segmentation approach.
  • To address the adaptive determination of spatial and feature resolutions.
  • To effectively utilize information across different scales for improved segmentation.

Main Methods:

  • A multiresolution pyramid is employed for texture feature extraction.
  • Adaptive integration of feature values across scales is performed.
  • The variance ratio criterion is used for automatic determination of texture count.

Related Experiment Videos

Main Results:

  • The proposed method accurately detects texture boundaries by using coarse resolution for centers and fine resolution for borders.
  • Adaptive integration of features across scales enhances segmentation performance.
  • Experimental results show superior performance compared to single-scale methods on synthetic and real images.

Conclusions:

  • The developed multiscale scheme significantly improves unsupervised texture segmentation.
  • The approach offers a robust solution for adaptive resolution selection and information utilization across scales.
  • This method provides a foundation for more advanced texture analysis in image processing.