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 Video

Updated: Jun 17, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

OBJCUT: efficient segmentation using top-down and bottom-up cues.

M Pawan Kumar1, P H S Torr, A Zisserman

  • 1Stanford University, Stanford, CA, USA. pawan@cs.stanford.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 16, 2010
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

Mathematical discoveries from program search with large language models.

Nature·2023
Same author

ISSLS PRIZE in Clinical Science 2023: comparison of degenerative MRI features of the intervertebral disc between those with and without chronic low back pain. An exploratory study of two large female populations using automated annotation.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2023
Same author

Optimizing Average Precision Using Weakly Supervised Data.

IEEE transactions on pattern analysis and machine intelligence·2015
Same author

Parameter Estimation and Energy Minimization for Region-Based Semantic Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2015
Same author

Temporal models for mitotic phase labelling.

Medical image analysis·2014
Same author

Learning from M/EEG data with variable brain activation delays.

Information processing in medical imaging : proceedings of the ... conference·2014
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

This study introduces OBJCUT, a novel probabilistic method for object segmentation that eliminates manual input and improves shape priors. It enhances image segmentation accuracy for various object categories.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Traditional grid conditional random fields (CRF) for image segmentation require manual seed input and lack robust shape priors.
  • Existing methods struggle with large intraclass variations in shape, appearance, and spatial distribution.

Purpose of the Study:

  • To develop an automated probabilistic method for object category-specific image segmentation.
  • To overcome limitations of traditional CRF methods by incorporating global, top-down shape information.
  • To improve segmentation accuracy for both articulated and nonarticulated objects.

Main Methods:

  • Implemented a probabilistic model with shape potentials derived from object pose estimation.
  • Utilized layered pictorial structures for articulated objects and exemplar sets for nonarticulated objects.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jun 17, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

  • Developed the OBJCUT method featuring efficient sampling algorithms and graph cut optimization.

    • Main Results:

    • Achieved automated object pose estimation, removing the need for manual user interaction.
    • Successfully incorporated global shape priors, enhancing segmentation accuracy.
    • Demonstrated superior performance compared to state-of-the-art methods on diverse object categories.

    Conclusions:

    • The proposed probabilistic framework with shape potentials offers a significant advancement in object category-specific image segmentation.
    • OBJCUT provides an efficient and accurate solution for segmenting complex objects in images.
    • The method's ability to handle variations in object categories sets a new benchmark in the field.