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Related Concept Videos

Structural Classification of Joints01:20

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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Orthogonal Trajectories01:26

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Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
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Functional Classification of Joints01:09

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Relative Motion Analysis using Rotating Axes01:25

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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

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Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
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Related Experiment Video

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A Protocol for Real-time 3D Single Particle Tracking
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Patchwise joint sparse tracking with occlusion detection.

Ali Zarezade, Hamid R Rabiee, Ali Soltani-Farani

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 15, 2014
    PubMed
    Summary

    This study introduces a new visual tracking method that effectively handles occlusions and appearance changes. The robust approach improves tracking accuracy by using patch-based reconstruction and sparse representation for object tracking.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Image Processing

    Background:

    • Object tracking in videos is challenging due to occlusions and appearance variations.
    • Existing methods often struggle with maintaining target identity under these conditions.

    Purpose of the Study:

    • To develop a robust visual tracking algorithm that addresses occlusion and appearance change.
    • To improve the accuracy and reliability of object tracking in complex scenarios.

    Main Methods:

    • The target is divided into patches, with appearance modeled using dictionaries of previous target patches.
    • A particle filter with a likelihood based on patch-reconstruction errors identifies the target.
    • Joint sparse representation enforces a common subspace for target and previous candidates.
    • An occlusion detection scheme uses reconstruction errors and a Markov chain to identify occluded patches.

    Main Results:

    • The proposed method demonstrates superior performance compared to state-of-the-art trackers on challenging datasets.
    • The occlusion detection scheme effectively identifies and excludes occluded patches during dictionary updates.
    • Robustness to occlusion and appearance change is significantly enhanced.

    Conclusions:

    • The patch-based dictionary learning and joint sparse representation offer a robust solution for visual tracking.
    • The adaptive occlusion detection mechanism improves tracking stability and accuracy.
    • This approach provides a significant advancement in handling challenging real-world tracking scenarios.