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

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.8K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
5.8K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.1K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.1K
Aggregates Classification01:29

Aggregates Classification

950
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
950
Fixation and Sectioning01:03

Fixation and Sectioning

7.3K
Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...
7.3K
Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

284
The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
The grading, or particle-size distribution, of sand is determined using sieve analysis, with standard sizes ranging from 150 μm to 10 mm (ASTM No. 100 sieve to 3⁄8 in. sieve). Sand is...
284

You might also read

Related Articles

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

Sort by
Same author

[Arthroscopic reconstruction of anterior cruciate ligament with preservation of the remnant bundle].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2013
Same author

[Anterior cruciate ligament reconstruction with tendon graft enveloped by preserved remnants].

Zhongguo gu shang = China journal of orthopaedics and traumatology·2013
Same author

Genetic and molecular biological characterization of two homologous cheR genes from Leptospira interrogans.

Acta biochimica et biophysica Sinica·2013
Same author

Upregulation of glycoprotein nonmetastatic B by colony-stimulating factor-1 and epithelial cell adhesion molecule in hepatocellular carcinoma cells.

Oncology research·2013
Same author

Effect of implantation of biodegradable magnesium alloy on BMP-2 expression in bone of ovariectomized osteoporosis rats.

Materials science & engineering. C, Materials for biological applications·2013
Same author

[Texture variation of CC 5052 aluminum alloy slab from surface to center layer by XRD].

Guang pu xue yu guang pu fen xi = Guang pu·2013
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: Jan 8, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
12:08

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

25.0K

Unified Granularity Controller for Interactive Segmentation.

Yian Zhao, Kehan Li, Pengchong Qiao

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 11, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel granularity-controllable interactive segmentation (IS) paradigm, UniGraCo, to precisely control segmentation detail. UniGraCo overcomes ambiguity and redundancy, offering a more efficient and practical interactive tool for users.

    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

    537
    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
    06:48

    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

    Published on: January 7, 2019

    9.4K

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
    12:08

    From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

    Published on: August 13, 2014

    25.0K
    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

    537
    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
    06:48

    Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

    Published on: January 7, 2019

    9.4K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Analysis

    Background:

    • Interactive Segmentation (IS) deduces human intent from sparse prompts for object segmentation.
    • Ambiguity in sparse-to-dense mapping leads to user trial-and-error and suboptimal segmentation granularity.
    • Existing multi-granularity IS methods lack scalability and produce redundant outputs.

    Purpose of the Study:

    • To develop a granularity-controllable IS paradigm that resolves ambiguity and allows precise user control over segmentation detail.
    • To introduce a Unified Granularity Controller (UniGraCo) supporting diverse control signals for varied segmentation needs.
    • To enhance system efficiency and practicality in interactive segmentation tasks.

    Main Methods:

    • Proposed a Unified Granularity Controller (UniGraCo) with multi-type optional control signals for flexible granularity adjustment.
    • Developed an automated data engine for low-cost generation of high-quality, multi-granularity mask-control signal data pairs.
    • Designed a granularity-controllable learning strategy to train IS models efficiently and stably, preserving segmentation capabilities.

    Main Results:

    • UniGraCo demonstrated significant advantages over existing methods in intricate instance and part-level segmentation scenarios.
    • The automated data engine effectively reduced annotation costs while generating abundant training data.
    • The granularity-controllable learning strategy successfully endowed pre-trained IS models with precise control over segmentation granularity.

    Conclusions:

    • UniGraCo offers a creative and effective solution for precise granularity control in interactive segmentation.
    • The proposed paradigm significantly improves upon existing multi-granularity IS methods in terms of efficiency and practicality.
    • UniGraCo shows strong potential as a practical interactive tool for complex segmentation tasks.