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

Random Sampling Method01:09

Random Sampling Method

11.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.8K
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

852
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
852

You might also read

Related Articles

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

Sort by
Same author

Mask-Guided Self-Supervised Video Object Segmentation.

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

Recent Progress of Single-Ion Conducting Polymer Electrolytes for Rechargeable Mono- and Multivalent Cation-Based Metal Batteries.

Angewandte Chemie (International ed. in English)·2026
Same author

Spatio-Temporal Decoupled Knowledge Compensator for Few-Shot Action Recognition.

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

MPLDM: Multi-modal prosthetic loosening diagnostic model for total hip arthroplasty.

Medical image analysis·2025
Same author

Large-Scale Omnidirectional Person Positioning.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Chemical knowledge-informed framework for privacy-aware retrosynthesis learning.

Nature communications·2025

Related Experiment Video

Updated: May 2, 2026

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
07:12

Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

Published on: January 6, 2026

773

Lazy random walks for superpixel segmentation.

Jianbing Shen, Yunfan Du, Wenguan Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |February 26, 2014
    PubMed
    Summary

    This study introduces a new lazy random walk (LRW) algorithm for image superpixel segmentation. The novel approach effectively segments complex textures and weak boundaries, outperforming existing methods.

    More Related Videos

    Automated Analysis of C. elegans Fluorescence Images using SegElegans
    06:27

    Automated Analysis of C. elegans Fluorescence Images using SegElegans

    Published on: October 10, 2025

    819
    An Unbiased Approach of Sampling TEM Sections in Neuroscience
    10:56

    An Unbiased Approach of Sampling TEM Sections in Neuroscience

    Published on: April 13, 2019

    9.4K

    Related Experiment Videos

    Last Updated: May 2, 2026

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment
    07:12

    Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

    Published on: January 6, 2026

    773
    Automated Analysis of C. elegans Fluorescence Images using SegElegans
    06:27

    Automated Analysis of C. elegans Fluorescence Images using SegElegans

    Published on: October 10, 2025

    819
    An Unbiased Approach of Sampling TEM Sections in Neuroscience
    10:56

    An Unbiased Approach of Sampling TEM Sections in Neuroscience

    Published on: April 13, 2019

    9.4K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Superpixel segmentation is crucial for image analysis, simplifying images into meaningful regions.
    • Existing methods struggle with weak boundaries and complex textures, limiting segmentation accuracy.
    • Developing robust superpixel algorithms is essential for advancing computer vision tasks.

    Purpose of the Study:

    • To introduce a novel image superpixel segmentation method using a lazy random walk (LRW) algorithm.
    • To enhance superpixel segmentation performance, particularly in challenging regions with weak boundaries and complex textures.
    • To improve the efficiency and accuracy of superpixel generation through iterative optimization.

    Main Methods:

    • Initialization of seed positions and application of the lazy random walk (LRW) algorithm to compute pixel probabilities.
    • Determination of initial superpixel boundaries using pixel probabilities and commute time.
    • Iterative optimization of superpixels via a new energy function incorporating commute time and texture measurements.
    • Refinement of superpixels by relocating centers and dividing large superpixels.

    Main Results:

    • The LRW algorithm effectively segments weak boundaries and complex texture regions.
    • Global probability maps and commute time strategy improve segmentation quality.
    • Iterative optimization enhances superpixel performance by refining their positions and sizes.
    • Experimental results demonstrate superior performance compared to previous superpixel approaches.

    Conclusions:

    • The proposed lazy random walk (LRW) algorithm offers a significant advancement in image superpixel segmentation.
    • The method excels in handling challenging image areas, providing more accurate and refined superpixels.
    • This approach holds promise for various computer vision applications requiring high-quality image segmentation.