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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.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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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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Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

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Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
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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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Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
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Non-Rigid Graph Registration Using Active Testing Search.

Eduard Serradell, Miguel Amável Pinheiro, Raphael Sznitman

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
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    Summary
    This summary is machine-generated.

    This study introduces a novel graph matching method for deformable structures, robust to non-linear changes and topological differences. It efficiently finds correspondences without initial alignment or appearance data, improving accuracy and speed.

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    Area of Science:

    • Computer Vision
    • Medical Image Analysis
    • Computational Geometry

    Background:

    • Matching branching curvilinear structures like graphs is crucial in various scientific fields.
    • Existing methods often struggle with non-linear deformations, topological variations, and partial data.

    Purpose of the Study:

    • To develop a robust and efficient approach for matching graphs with potential deformations.
    • To overcome limitations of existing methods regarding appearance similarity and initial alignment requirements.

    Main Methods:

    • Utilizing Gaussian process regressions for modeling non-linear geometrical mappings between graphs.
    • Implementing an iterative correspondence establishment and mapping update strategy.
    • Introducing an Active Testing Search for efficient candidate matching in large graphs.

    Main Results:

    • Demonstrated effectiveness on synthetic datasets.
    • Successfully applied to real-world data including angiography, retinal fundus images, and microscopy stacks.
    • Showcased robustness against non-linear deformations and topological differences.

    Conclusions:

    • The proposed graph matching approach is effective and versatile.
    • It offers significant improvements in handling complex deformations and large datasets.
    • The method shows promise for applications in medical imaging and beyond.