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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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Nonlocal Means Two Dimensional Histogram-Based Image Segmentation via Minimizing Relative Entropy.

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Summary

This study introduces a novel image segmentation method that enhances accuracy by incorporating spatial and gray-level pixel information. The approach utilizes a non-local mean filter and a two-dimensional histogram for improved thresholding.

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image segmentationnon-local filterthresholdingtwo dimensional histogram

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Area of Science:

  • Computer Vision
  • Image Processing
  • Pattern Recognition

Background:

  • Traditional thresholding methods often overlook spatial correlation between pixels, leading to suboptimal image segmentation.
  • Ignoring pixel spatial information can result in unsatisfied segmentation outcomes in various applications.

Purpose of the Study:

  • To propose a new image segmentation approach that integrates both spatial and gray-level pixel information.
  • To enhance the accuracy and effectiveness of image thresholding methods.

Main Methods:

  • A non-local mean filter is applied to capture spatial information within pixel neighborhoods.
  • A two-dimensional histogram, termed the non-local mean two-dimensional histogram, is constructed using the original and filtered images.
  • An ideal thresholding vector is selected via a minimum relative entropy criterion.

Main Results:

  • The proposed method significantly improves segmentation accuracy compared to existing thresholding techniques.
  • Incorporating spatial information through the non-local mean filter enhances segmentation performance.
  • The non-local mean two-dimensional histogram effectively utilizes both spatial and gray-level data.

Conclusions:

  • The novel image segmentation method effectively leverages spatial and gray-level information for superior thresholding.
  • This approach offers a significant advancement in image segmentation accuracy and robustness.
  • The non-local mean filter integration provides a robust way to handle spatial dependencies in image thresholding.