Image Segmentation Algorithm: Pixel Clustering Region Growing Method

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An image segmentation technique known as pixel clustering region growing, which initiates from a seed pixel and incorporates neighboring pixels with similar attributes—such as average grayscale, texture patterns, and color characteristics—into the target region through iterative comparison.

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An image segmentation algorithm—the Pixel Clustering Region Growing Method. This approach begins with a seed pixel and iteratively incorporates neighboring pixels that share similar properties, such as average grayscale, texture patterns, and color characteristics, into the target region. The algorithm's key advantage lies in its ability to effectively partition an image into regions with homogeneous features, facilitating subsequent image analysis and processing tasks. Additionally, the pixel clustering region growing method can be flexibly adjusted and optimized—for instance, by modifying similarity thresholds or neighborhood connectivity rules—to accommodate various types of image segmentation requirements. From an implementation perspective, the algorithm typically utilizes queue or stack data structures to manage pixel candidates and employs distance metrics (e.g., Euclidean distance in color space) for similarity evaluation.

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