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Related Concept Videos

Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
Region of Convergence01:17

Region of Convergence

The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...

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Updated: May 12, 2026

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Combining contour-based and region-based in image segmentation.

Issam Dagher1, Elie Abboud1

  • 1Computer Engineering Department, University of Balamand, Balamand, North Governorate, Lebanon.

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|August 16, 2024
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Summary

This study introduces an optimized image segmentation method by determining the ideal number of clusters. This approach enhances accuracy in applications like medical imaging and object detection.

Keywords:
Image Segmentation. Clustering. Edge detection. Colour frequencies. Texture.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Accurate image segmentation is crucial for diverse fields including medical imaging, machine vision, and object detection.
  • Applications range from tumor and face detection to video surveillance.

Purpose of the Study:

  • To present an optimized clustering approach for enhanced image segmentation.
  • To improve the accuracy and performance of image segmentation techniques.

Main Methods:

  • Combined region-based and contour-based segmentation strategies.
  • Utilized edge detection for initial region identification.
  • Employed Gabor wavelets for texture classification and spatial resolution analysis.
  • Determined the optimal number of clusters for Fuzzy c-means (FCM) using color frequencies.

Main Results:

  • The proposed algorithm demonstrated superior performance compared to existing wavelet and clustering methods.
  • Achieved improved segmentation metrics, including Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), and Matthews Correlation Coefficient (MCC).

Conclusions:

  • Optimizing the number of clusters significantly enhances image segmentation performance.
  • The developed method leads to better detection and localization in segmentation-based applications.