Color Image Segmentation Method

This document presents a color image segmentation methodology. The segmentation process involves the following steps: (a) Calculation of predetermined values representing the color dissimilarity of peripheral pixels using the pixel values of the input image - this typically involves implementing color distance metrics (e.g., Euclidean distance in RGB/HSV space) across pixel neighborhoods; (b) Transformation of the calculated values into predetermined proportional values to obtain a converted image, commonly achieved through scaling functions or normalization routines; (c) Segmentation of the transformed image using appropriate algorithms such as thresholding, clustering, or region-growing methods. According to this color image segmentation approach, robust automatic segmentation can be achieved with high processing efficiency, even when handling images containing significant noise levels. The method demonstrates applicability to various color images, including high-noise images and complex image structures. Additionally, this technique enhances image processing efficiency by reducing the requirement for manual intervention. Consequently, this color image segmentation method possesses broad application prospects in computer vision and image processing domains. Key implementation considerations may include: - Color space selection (RGB, HSV, Lab) for dissimilarity calculations - Neighborhood size configuration for peripheral pixel analysis - Scaling algorithms for value transformation (linear/non-linear scaling) - Choice of segmentation algorithm (K-means, watershed, graph-based methods) based on image characteristics