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Related Concept Videos

Structural Classification of Joints01:20

Structural Classification of Joints

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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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.Two regions of electron density in a diatomic...
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Functional Classification of Joints

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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.
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

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Published on: November 2, 2012

Superordinate shape classification using natural shape statistics.

John Wilder1, Jacob Feldman, Manish Singh

  • 1Department of Psychology, Center for Cognitive Science, Rutgers University, New Brunswick, United States. jdwilder@ruccs.rutgers.edu

Cognition
|March 29, 2011
PubMed
Summary
This summary is machine-generated.

This study shows that classifying natural shapes like animals or leaves can be done using simple statistical analysis of shape skeletons. Human shape classification aligns well with this statistical approach, suggesting a tuned skeleton analysis underlies our perception.

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Area of Science:

  • Cognitive Science
  • Computer Vision
  • Computational Neuroscience

Background:

  • Understanding how humans categorize complex natural shapes is a fundamental question in cognitive science.
  • Previous research has explored various features for shape classification, but the role of skeletal structures remains under investigation.

Purpose of the Study:

  • To investigate if coarse natural shape categorization (e.g., animal, leaf) can be achieved through statistical analysis of shape skeletons.
  • To compare human shape classification performance with a computational model based on shape skeleton statistics.

Main Methods:

  • Surveyed databases of natural shapes to extract and tabulate shape skeleton parameters for different categories.
  • Developed a naive Bayesian classifier using these natural shape statistics.
  • Conducted experiments where human subjects classified novel shapes into natural categories.

Main Results:

  • Identified shape statistics derived from skeletons that effectively discriminate between natural shape categories.
  • Demonstrated good agreement between human subjects' classifications and the naive Bayesian classifier's predictions.
  • The computational model achieved significant accuracy in classifying shapes based on skeleton parameters.

Conclusions:

  • Human superordinate shape classification appears to involve a simple statistical classification of the shape skeleton.
  • This classification mechanism is 'tuned' to the statistical regularities present in natural shapes.
  • Shape skeleton analysis provides a viable computational framework for understanding natural shape perception.