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 Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

You might also read

Related Articles

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

Sort by
Same author

Erratum for: Associations of MRI-derived Paraspinal IMAT and LMM with Cardiometabolic Risk Factors: Results from a German Cohort.

Radiology·2026
Same author

ProtoFlow: interpretable and robust surgical workflow modeling with learned dynamic scene graph prototypes.

International journal of computer assisted radiology and surgery·2026
Same author

Toward comprehensive real-time scene understanding in ophthalmic surgery through multimodal image fusion.

International journal of computer assisted radiology and surgery·2026
Same author

DefSynUS: Real-time patient-specific intrahepatic vessel identification via deformation-aware CT-US domain adaptation.

International journal of computer assisted radiology and surgery·2026
Same author

Smartphone photogrammetry for rapid 3D surface modeling of head and neck specimens to support frozen section communication: A feasibility pilot study.

Oral oncology·2026
Same author

Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis.

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

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

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

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

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

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

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

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

Task-KV: Task-aware KV Cache Optimization via Semantic Differentiation of Attention Heads.

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

Achieving Text-based Person Retrieval with Any Granularity.

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

Related Experiment Video

Updated: May 23, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

Gradient response maps for real-time detection of textureless objects.

Stefan Hinterstoisser1, Cedric Cagniart, Slobodan Ilic

  • 1Department of Computer Aided Medical Procedures (CAMP), Technische Universität München, Garching bei München 85478, Germany. hinterst@in.tum.de

IEEE Transactions on Pattern Analysis and Machine Intelligence
|March 24, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a fast, real-time 3D object detection method that avoids lengthy training and works with untextured objects. It uses a novel image representation for robust template matching, improving speed and clutter resistance.

More Related Videos

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Related Experiment Videos

Last Updated: May 23, 2026

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
07:08

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Computer Vision
  • Robotics
  • Machine Learning

Background:

  • Real-time 3D object instance detection is crucial for applications like robotics and augmented reality.
  • Existing methods often require extensive training and struggle with untextured objects or background clutter.

Purpose of the Study:

  • To develop a novel, efficient, and robust method for real-time 3D object instance detection.
  • To address limitations of current methods, particularly regarding training time and handling untextured objects.

Main Methods:

  • A novel image representation for template matching based on spread image gradient orientations.
  • Efficient processing leveraging modern computer architectures to handle thousands of templates in real-time.
  • Extension of the method using dense depth sensor data and 3D surface normal orientations for enhanced performance.

Main Results:

  • The proposed method achieves real-time performance without a time-consuming training stage.
  • Demonstrated robustness to small image transformations and effectiveness with untextured objects.
  • Significantly faster and more robust to background clutter compared to state-of-the-art methods in experiments.

Conclusions:

  • The novel image representation enables efficient and robust real-time 3D object instance detection.
  • The method offers a significant advancement in speed and reliability for 3D object recognition tasks.
  • The approach is adaptable and can be enhanced with depth sensor data for improved accuracy.