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

Masking and Demasking Agents01:19

Masking and Demasking Agents

3.8K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.8K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

576
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
576
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

693
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
693
Reducing Line Loss01:18

Reducing Line Loss

429
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
429
Encoding01:19

Encoding

947
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
947
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

5.3K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
5.3K

You might also read

Related Articles

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

Sort by
Same author

Transgene-free generation of mouse post-gastrulation whole embryo models solely from naive ESCs and iPSCs.

Cell stem cell·2025
Same author

Bridge the Intra-Class Gap: K-Shot Multi-Scale Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation.

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

Advanced Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection.

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

Foundation Models Defining a New Era in Vision: A Survey and Outlook.

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

Robust Perception and Precise Segmentation for Scribble-Supervised RGB-D Saliency Detection.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

SipMaskv2: Enhanced Fast Image and Video Instance Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2023
Same journal

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

852

Sparse Coding for Alpha Matting.

Jubin Johnson, Ehsan Shahrian Varnousfaderani, Hisham Cholakkal

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study reinterprets alpha matting as sparse coding, enabling better estimation from unpaired foreground (F) and background (B) samples. The novel approach improves both image and video matting performance compared to existing methods.

    More Related Videos

    AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
    06:03

    AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

    Published on: June 23, 2023

    878
    Profiling Maternal Behavior Responses During Whole-Brain Imaging
    07:12

    Profiling Maternal Behavior Responses During Whole-Brain Imaging

    Published on: January 24, 2025

    1.5K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    852
    AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
    06:03

    AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

    Published on: June 23, 2023

    878
    Profiling Maternal Behavior Responses During Whole-Brain Imaging
    07:12

    Profiling Maternal Behavior Responses During Whole-Brain Imaging

    Published on: January 24, 2025

    1.5K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Traditional alpha matting relies on the compositing equation using paired foreground (F) and background (B) samples.
    • The quality of alpha mattes is highly dependent on the careful selection of these (F,B) pairs.

    Purpose of the Study:

    • To reframe the alpha matting problem as a sparse coding task for improved accuracy.
    • To develop a method that utilizes unpaired foreground and background samples effectively.
    • To extend the framework for enhanced video matting with temporal coherence.

    Main Methods:

    • Reinterpreting matting as sparse coding of pixel features using unpaired F and B samples.
    • Employing non-parametric probabilistic segmentation to determine pixel certainty for dictionary formation.
    • Extending the sparse coding framework to videos using multi-frame dictionaries and a multi-frame graph model.

    Main Results:

    • Achieved superior alpha estimation by removing the constraint of (F,B) pairs.
    • Effectively handled temporal coherence requirements in video matting.
    • Demonstrated state-of-the-art performance in both quantitative and qualitative evaluations on benchmark datasets.

    Conclusions:

    • The proposed sparse coding approach offers a more robust and flexible solution for alpha matting.
    • The method significantly advances the state-of-the-art in both image and video matting.
    • The framework provides an efficient and effective way to generate high-quality alpha mattes.