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Probability Histograms

Probability Histograms

A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Interactive image segmentation based on level sets of probabilities.

Yugang Liu¹, Yizhou Yu

¹Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

IEEE Transactions on Visualization and Computer Graphics

|April 27, 2011

Summary

This summary is machine-generated.

This study introduces an advanced interactive image segmentation algorithm using level sets. It accurately segments complex objects by integrating classification models and distance transforms, reducing sensitivity to user input.

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Interactive image segmentation is crucial for analyzing complex visual data.
Traditional methods struggle with objects of intricate topology and fragmented appearances.
Level set methods offer advantages for such challenging segmentation tasks.

Purpose of the Study:

To develop a robust and accurate algorithm for interactive image segmentation.
To enhance the level set method by integrating discriminative classification and distance transforms.
To improve segmentation accuracy and reduce user interaction sensitivity.

Main Methods:

The proposed algorithm integrates discriminative classification models and distance transforms with the level set method.
The level set function approximates pixelwise posterior probabilities.
Forces driving the level set evolution include region, edge field, and curvature forces, based on a probabilistic classifier and edge distance transform.

Main Results:

The method effectively avoids local minima and accurately snaps to object boundaries.
A novel technique improves classifier and level set performance over multiple passes.
The final segmentation is less sensitive to initial user interactions.

Conclusions:

The developed algorithm provides a robust and accurate solution for interactive image segmentation.
Integration of classification and distance transforms enhances the capabilities of the level set method.
The technique offers improved performance and user experience for segmenting complex image objects.