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Structural Classification of Joints

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Functional Classification of Joints01:09

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Functional Classification of Joints
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Cross-Modal Multivariate Pattern Analysis
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Cross-Modal Multivariate Pattern Analysis

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Joint Attributes and Event Analysis for Multimedia Event Detection.

Zhigang Ma, Xiaojun Chang, Zhongwen Xu

    IEEE Transactions on Neural Networks and Learning Systems
    |June 23, 2017
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    Summary
    This summary is machine-generated.

    This study introduces video attributes for multimedia event detection (MED), improving accuracy by learning from external video content. The proposed method enhances event detection by incorporating these dynamic attributes into a joint framework.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Multimedia Analysis

    Background:

    • Semantic attributes are valuable for multimedia event detection (MED).
    • Existing methods inferring attributes from images lack dynamic video information.
    • This limitation hinders optimal performance in video analysis.

    Purpose of the Study:

    • To propose a novel method for learning semantic attributes directly from external videos, termed 'video attributes'.
    • To enhance multimedia event detection by incorporating these video attributes.
    • To improve the dynamic information capture for video analysis.

    Main Methods:

    • Learning semantic attributes from external videos using their semantic labels.
    • Developing a correlation vector to link video attributes to target events.
    • Integrating video attributes into a joint framework with existing event detectors.

    Main Results:

    • Experimental validation on TRECVID MED 2013 and 2014 datasets.
    • Demonstrated advantage over several state-of-the-art algorithms.
    • Significant improvement in multimedia event detection performance.

    Conclusions:

    • Video attributes offer a more effective approach to semantic characterization for MED.
    • The proposed joint framework successfully leverages dynamic information for improved detection.
    • This method provides a promising direction for advancing multimedia event detection research.