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Related Concept Videos

Histogram01:05

Histogram

The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
Atomic Nuclei: Types of Nuclear Relaxation01:28

Atomic Nuclei: Types of Nuclear Relaxation

Nuclear relaxation restores the equilibrium population imbalance and can occur via spin–lattice or spin–spin mechanisms, which are first-order exponential decay processes.
In spin–lattice or longitudinal relaxation, the excited spins exchange energy with the surrounding lattice as they return to the lower energy level. Among several mechanisms that contribute to spin–lattice relaxation, magnetic dipolar interactions are significant. Here, the excited nucleus transfers energy to a nearby...
Relaxation of Skeletal Muscles01:29

Relaxation of Skeletal Muscles

The period of muscle contraction primarily influences the duration of stimulation at the neuromuscular junction (NMJ), the presence of free calcium ions in the sarcoplasm, and the availability of energy or ATP to support contractions.
When an action potential reaches the axon terminal, it depolarizes the membrane and opens voltage-gated sodium channels. Sodium ions enter the cell, further depolarizing the presynaptic membrane. This depolarization causes voltage-gated calcium channels to open.
Relative Frequency Histogram01:14

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...

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Authors'Reply.

IEEE transactions on pattern analysis and machine intelligence·2011

Related Experiment Video

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NMR 15N Relaxation Experiments for the Investigation of Picosecond to Nanoseconds Structural Dynamics of Proteins
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Studies in global and local histogram-guided relaxation algorithms.

P A Nagin1, A R Hanson, E M Riseman

  • 1MEMBER, IEEE, Department of Ophthalmology, Tufts New England Medical Center, Boston, MA 02111.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary

This study explores an image segmentation algorithm, revealing histogram overlap and global coefficients cause errors. Solutions involve orientation-dependent coefficients and subimage processing for improved segmentation.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Image segmentation is crucial for image analysis.
  • Existing algorithms face challenges with accuracy and detail preservation.

Purpose of the Study:

  • To investigate an image segmentation algorithm using histogram clustering and probabilistic relaxation labeling.
  • To identify and address sources of pixel labeling errors in the algorithm.

Main Methods:

  • Algorithm evaluation using artificially generated test images with known parameters.
  • Analysis of error sources related to histogram distribution overlap and global compatibility coefficients.
  • Exploration of orientation-dependent compatibility coefficients and subimage processing.

Main Results:

  • Histogram distribution overlap leads to fragmented or missing regions.
  • Global compatibility coefficients in relaxation labeling can cause errors and destroy fine details.
  • Orientation-dependent coefficients and localized processing offer potential solutions.

Conclusions:

  • The developed image segmentation algorithm has limitations due to histogram overlap and global coefficients.
  • Modifications incorporating orientation-dependent coefficients and localized processing are proposed to enhance segmentation accuracy and detail preservation.