Abstract
This review presents various image segmentation methods using complex networks. Image segmentation is one of the important steps in image analysis as it helps analyze and understand complex images. At first, it has been tried to classify complex networks based on how it being used in image segmentation. In computer vision and image processing applications, image segmentation is essential for analyzing complex images with irregular shapes, textures, or overlapping boundaries. Advanced algorithms make use of machine learning, clustering, edge detection, and region-growing techniques. Graph theory principles combined with community detection-based methods allow for more precise analysis and interpretation of complex images. Hybrid approaches combine multiple techniques for comprehensive, robust segmentation, improving results in computer vision and image processing tasks.
Abstract (translated)
本文介绍了使用复杂网络进行图像分割的方法。图像分割是图像分析的重要步骤，因为它有助于分析和理解复杂图像。首先，试图根据其在图像分割中的使用情况对复杂网络进行分类。在计算机视觉和图像处理应用中，图像分割对于分析复杂图像的形状不规则、纹理或重叠边界至关重要。高级算法利用机器学习、聚类、边缘检测和区域生长技术。社区检测方法与图论原理相结合，允许更精确地分析和解释复杂图像。混合方法结合多种技术进行全面的、稳健的分割，从而提高计算机视觉和图像处理任务的成果。
URL
https://arxiv.org/abs/2401.02758
