Color Image Segmentation Optimization: Threshold Edge Detection with Harmonic and Wiener Filter Enhancements

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ID: 312736
2025
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Ranked #84 of 705 articles by views in Journal of Computing & Biomedical Informatics

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Abstract
Digital photos can be segmented to find objects, borders, and other relevant information. Segmentation can be done in a variety of ways, including Watershed Segmentation, Region-Based Segmentation, Edge-Based Segmentation, Threshold-Based Segmentation, and Cluster-Based Segmentation. These methods produce a segmented image, which is a compilation of every pixel in the image. Pixels represent the color, texture, and other elements of an image. An image is divided into separate objects or areas. Color Image Segmentation uses threshold-based edge detection, and it’s improved through harmonic and Wiener filters to reduce noise. This method simplifies and changes the representation of an image into something (line, curve drawing that highlights the intensity change, which is more important and easier to explore. In this concept, convert a color image into a gray-scale image and apply different filters (Robert, Prewitt, Sobel, log, and Canny) with edge detection techniques. This applied edge detection technique is also useful with a filter that gives acute and exact results. This research computes the Threshold 0.67 values for color images to check for better performance. For this purpose, use MATLAB. Our technique will help to improve edge detection by the combination of other types of filters, namely Hormonic and Weiner, to eliminate the noise from the image.
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imported_1777056365_69ebba6dc2bb4 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Muhammad Sarfraz
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
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URL
https://jcbi.org/index.php/Main/article/view/980
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