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Histogram

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...

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Fast Graph Partitioning Active Contours for Image Segmentation Using Histograms.

Sumit K Nath¹, Kannappan Palaniappan¹

¹Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.

EURASIP Journal on Image and Video Processing

|May 23, 2022

Summary

This summary is machine-generated.

This study enhances the Graph Partitioning Active Contours (GPACs) algorithm for image segmentation. The new method improves speed, accuracy, and reduces memory usage, making it more efficient for complex image analysis.

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Area of Science:

Computer Vision
Image Processing
Computational Mathematics

Background:

The Graph Partitioning Active Contours (GPACs) algorithm offers a novel approach to image segmentation.
Existing GPACs implementations face challenges with computational expense and memory requirements, particularly for large images.
Improvements are needed to enhance the efficiency and applicability of GPACs.

Purpose of the Study:

To develop a computationally efficient reformulation of the GPACs algorithm.
To significantly reduce the memory footprint of the GPACs algorithm.
To improve the accuracy and speed of image segmentation using GPACs.

Main Methods:

Replaced the computation of an approximate dissimilarity matrix with fixed length histograms and a symmetric-centrosymmetric extensor matrix.
Jointly computed terms associated with the complete dissimilarity matrix.
Developed a computationally efficient reformulation of GPACs with a reduced memory footprint.

Main Results:

Achieved significant reductions in memory requirements compared to the original GPACs algorithm.
Demonstrated accelerated convergence of the evolving active contour.
Extended the performance of GPACs to multidimensional images seamlessly.

Conclusions:

The reformulated GPACs algorithm offers substantial improvements in efficiency and performance.
This method provides a more practical and scalable solution for image segmentation tasks.
The enhanced GPACs algorithm is suitable for both 2D and multidimensional image analysis.