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 11, 2026

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

Automatic image segmentation for concealed object detection using the expectation-maximization algorithm.

Dong-Su Lee1, Seokwon Yeom, Jung-Young Son

  • 1Department of Computer and Communication Engineering, Daegu University, Gyeongsan, Gyeongbuk 712-714, Korea.

Optics Express
|July 1, 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

An Encoder-Decoder Architecture within a Classical Signal-Processing Framework for Real-Time Barcode Segmentation.

Sensors (Basel, Switzerland)·2023
Same author

Wavefront Characteristics of a Digital Holographic Optical Element.

Micromachines·2023
Same author

Chemical gradients on graphene <i>via</i> direct mechanochemical cleavage of atoms from chemically functionalized graphene surfaces.

Nanoscale advances·2023
Same author

Evaluation of eye response using a wearable display with automatic interpupillary distance adjustment.

Optics express·2022
Same author

Author Correction: Static moiré patterns in moving grids.

Scientific reports·2022
Same author

Static moiré patterns in moving grids.

Scientific reports·2020
Same journal

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026
Same journal

Millisecond photon-to-photon latency and high-speed volumetric projection system for optogenetics.

Optics express·2026
Same journal

Polarization-encoded coaxial structured light for high-precision 3D surface profilometry.

Optics express·2026
Same journal

Discrete freeform optical design based on collaborative optimization of point cloud and local normals.

Optics express·2026
Same journal

Ultrafast ghost imaging with 25 GHz speckle switching and wavelength-division multiplexing.

Optics express·2026
Same journal

Atomic vapor cells fabricated by femtosecond laser welding of standard-optical-quality glass.

Optics express·2026
See all related articles

This study introduces a multi-level Expectation-Maximization (EM) method for improved concealed object detection using passive millimeter wave (MMW) imaging. The new approach enhances security by more accurately segmenting hidden items compared to traditional EM methods.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Signal Processing

Background:

  • Passive millimeter wave (MMW) imaging offers unique capabilities for detecting concealed objects under clothing, beneficial for security applications.
  • Accurate image segmentation is crucial for distinguishing hidden objects from background clutter in MMW imagery.
  • Existing methods may have limitations in precisely separating complex concealed items.

Purpose of the Study:

  • To develop and evaluate an advanced image segmentation technique for enhanced concealed object detection in passive MMW imaging.
  • To improve the accuracy of separating concealed objects from their surroundings.
  • To demonstrate the superiority of the proposed method over conventional approaches.

Main Methods:

  • Implementation of a multi-level Expectation-Maximization (EM) algorithm for image segmentation.

Related Experiment Videos

Last Updated: Jun 11, 2026

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

  • Application of the EM method to generate a Gaussian Mixture Model (GMM) of the MMW image data.
  • Performance evaluation using the average probability of error metric.

    • Main Results:

    • The multi-level EM method demonstrated more accurate separation of concealed objects compared to the conventional EM method.
    • Quantitative analysis confirmed improved segmentation performance.
    • The proposed technique effectively isolates target objects in MMW images.

    Conclusions:

    • The multi-level EM method represents a significant advancement in passive MMW image segmentation for concealed object detection.
    • This technique offers enhanced accuracy and potential for improved security screening.
    • Further research can explore applications in various MMW imaging scenarios.