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Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

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Synovial joints are the most common type of joint in the body. A key structural characteristic for a synovial joint is the presence of a joint cavity. This fluid-filled space is where the articulating surfaces of the bones contact each other. Also, unlike fibrous or cartilaginous joints, the articulating bone surfaces at a synovial joint are not directly connected to each other with fibrous connective tissue or cartilage. This gives the bones of a synovial joint the ability to move smoothly...
7.0K
Structural Joints: Fibrous Joints01:03

Structural Joints: Fibrous Joints

3.8K
Fibrous joints are a type of joint where the bones are connected by fibrous connective tissue. These joints provide stability and minimal to no movement between the articulating bones. There are three types of fibrous joints.
Suture
All the bones of the skull, except for the mandible, are joined to each other by a fibrous joint called a suture. The fibrous connective tissue found at a suture strongly unites the adjacent skull bones and thus helps to protect the brain and form the face. In...
3.8K
Structural Joints: Cartilaginous Joints01:17

Structural Joints: Cartilaginous Joints

4.1K
As the name indicates, at a cartilaginous joint, the adjacent bones are united by cartilage, a tough but flexible type of connective tissue. Unlike synovial joints, these types of joints lack a joint cavity and involve bones joined together by either hyaline cartilage or fibrocartilage.
There are two types of cartilaginous joints:
Synchondrosis
A synchondrosis ("joined by cartilage") is a cartilaginous joint where bones are connected by hyaline cartilage. Synchondrosis may be temporary...
4.1K
Joints01:26

Joints

35.8K
Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
35.8K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K

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Related Experiment Video

Updated: Feb 7, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Joint Video Object Discovery and Segmentation by Coupled Dynamic Markov Networks.

Ziyi Liu, Le Wang, Gang Hua

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 31, 2018
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    This study introduces a novel method for joint object discovery and segmentation in noisy videos where targets disappear intermittently. The approach improves performance on challenging noisy datasets compared to existing methods.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Probabilistic Modeling

    Background:

    • Extracting segmentation masks from noisy videos is challenging due to object intermittency.
    • Existing methods often address object discovery or segmentation separately, not jointly.
    • Noisy video datasets with intermittent object presence require specialized approaches.

    Purpose of the Study:

    • To develop a unified method for joint object discovery and segmentation in noisy videos.
    • To demonstrate the synergistic benefits of combining discovery and segmentation tasks.
    • To introduce and validate performance on new noisy video datasets.

    Main Methods:

    • Proposed a principled probabilistic model with coupled dynamic Markov networks for discovery and segmentation.
    • Employed Bayesian inference with belief propagation and bi-directional message passing.
    • Validated the method on five datasets, including three standard and two newly introduced noisy datasets.

    Main Results:

    • The proposed method shows improved performance on noisy video datasets compared to state-of-the-art.
    • Performance is slightly inferior on non-noisy datasets where objects are always present.
    • Bi-directional message passing effectively facilitates collaboration between discovery and segmentation.

    Conclusions:

    • Jointly addressing object discovery and segmentation in noisy videos is beneficial.
    • The coupled probabilistic model effectively handles intermittent object presence.
    • The method offers a robust solution for challenging video segmentation tasks with noise.