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

Range image segmentation using surface selection criterion.

Alireza Bab-Hadiashar1, Niloofar Gheissari

  • 1Faculty of Engineering and Industrial Sciences, Swinbume University of Technology, Melbourne, Australia. abab-hadiashar@swin.edu.au

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 13, 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

Modernizing Livestock Operations: Smart Feedlot Technologies and Their Impact.

Animals : an open access journal from MDPI·2026
Same author

Uncertainty-Aware Information Pursuit for Interpretable and Reliable Medical Image Analysis.

IEEE transactions on medical imaging·2026
Same author

An Information-Theoretic Method to Automatic Shortcut Avoidance and Domain Generalization for Dense Prediction Tasks.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Incidental detection of prostate cancer with computed tomography scans.

Scientific reports·2021
Same author

Automatic segmentation for synchrotron-based imaging of porous bread dough using deep learning approach.

Journal of synchrotron radiation·2021
Same author

Motion segmentation of RGB-D sequences: Combining semantic and motion information using statistical inference.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2020
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

This study introduces a new method for surface segmentation using a novel strain energy criterion. The algorithm accurately segments complex objects with both planar and curved surfaces, improving range data analysis.

Area of Science:

  • Computer Vision
  • Geometric Modeling
  • Surface Reconstruction

Background:

  • Accurate surface segmentation is crucial for 3D object recognition and analysis.
  • Existing model selection techniques often struggle with complex surfaces and identifying surface associations.
  • Robust range data segmentation is needed for industrial applications.

Purpose of the Study:

  • To develop a novel criterion for surface model selection.
  • To present a robust range data segmentation algorithm for complex objects.
  • To improve the identification of associations between surface parts.

Main Methods:

  • Introduced a novel criterion based on minimizing strain energy of fitted surfaces.
  • Developed a range data segmentation algorithm that simultaneously identifies surface type and separates points.

Related Experiment Videos

  • Evaluated performance against existing model selection techniques.

    • Main Results:

    • The novel strain energy criterion demonstrated effective model selection.
    • The segmentation algorithm successfully segmented complex objects with planar and curved surfaces.
    • The technique identified associations between separated surface parts with identical Cartesian equations.

    Conclusions:

    • The proposed strain energy criterion offers a robust solution for model selection in surface segmentation.
    • The developed algorithm provides accurate segmentation of complex 3D objects from range data.
    • The ability to detect surface part associations has significant implications for industrial applications.