Investigations on adaptive connectivity and shape prior based fuzzy graph‐cut colour image segmentation
Adonu Celestine, James Dinesh Peter · Expert Systems · 2020
Abstract Image segmentation is a challenging problem in computer vision with wide application. It is a process which considers the similarity criterion required to separate an image into different homogenous connected regions. First, an Optimized Adaptive Connectivity and Shape Prior in Modified Graph Cut Segmentation method has been applied to handle the structural irregularities in images. Second, an Optimized Adaptive Connectivity and Shape Prior in Modified Fuzzy Graph Cut Segmentation (Opac‐MFGseg) is proposed to partition the images based on feature values. In this method, a fuzzy rule based system is used with optimization algorithm to provide the information on how much a specific feature is involved in image boundaries. The graph obtained from this fuzzy approach is further used in adaptive shape prior in modified graph cuts framework. Moreover, this method supports moving images (videos). In such a situation, a fully dynamic method called Optimized Adaptive Connectivity and Shape Prior in Dynamic Fuzzy Graph Cut Segmentation (Opac‐DFGseg) method is proposed for the image segmentation. The effectiveness of the Opac‐MFGseg and Opac‐DFGseg methods is tested in terms of average sensitivity, precision, area overlap measure, relative error, and accuracy and computation time.