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Related Concept Videos

Structural Joints: Synovial Joints01:16

Structural Joints: Synovial Joints

6.9K
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...
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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

4.0K
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...
4.0K
Joints01:26

Joints

35.7K
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...
35.7K
Method of Joints01:30

Method of Joints

1.3K
The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint.
Since plane truss members are in the same plane, each joint is subjected to a coplanar and concurrent force system. To apply the method of joints, the first step is to...
1.3K
Introduction to Joints00:58

Introduction to Joints

4.8K
The adult human body usually has 206 bones, and except for the hyoid bone in the neck, each bone is connected to at least one other bone. Joints are the location where bones come together. Many joints allow for movement between the bones. At these joints, the articulating surfaces of the adjacent bones can move smoothly against each other. However, the bones of other joints may be joined by connective tissue or cartilage. These joints are designed for stability and provide little or no...
4.8K

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Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
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Data Augmentation-Based Joint Learning for Heterogeneous Face Recognition.

Bing Cao, Nannan Wang, Jie Li

    IEEE Transactions on Neural Networks and Learning Systems
    |October 30, 2018
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    Summary
    This summary is machine-generated.

    This study introduces a novel data augmentation-based joint learning (DA-JL) approach to improve heterogeneous face recognition (HFR). The DA-JL method enhances accuracy by balancing intraclass and interclass variations in cross-modality face matching.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Biometrics

    Background:

    • Heterogeneous face recognition (HFR) is crucial for security but challenged by significant cross-modality image discrepancies (e.g., shape, style, color).
    • Existing HFR methods often fail due to limited information from heterogeneous face images alone.

    Purpose of the Study:

    • To propose a novel data augmentation-based joint learning (DA-JL) approach to enhance heterogeneous face recognition.
    • To address the challenge of substantial discrepancies between cross-modality face images.

    Main Methods:

    • The DA-JL approach incorporates synthesized images to mutually transform cross-modality differences.
    • It augments intraclass scale for more discriminative information while balancing reduced interclass diversity.
    • Similarity scores are derived using the log-likelihood ratio.

    Main Results:

    • The DA-JL method demonstrated superior performance across diverse databases, including sketch, infrared, low-resolution, and occluded face images.
    • Experiments validated the effectiveness of the proposed approach against state-of-the-art methods.

    Conclusions:

    • The DA-JL approach effectively improves heterogeneous face recognition by leveraging data augmentation and joint learning.
    • The method successfully balances intraclass augmentation and interclass diversity for robust cross-modality face matching.