A Study on Advancements in Image Segmentation Techniques

In the domain of computer vision, image segmentation is important in that it is used to divide out image pixels into meaningful segments. Ä°n this review the latest clustering methods, such as, KMeans and Fuzzy CMeans, that cluster pixels based on similarity; however, they often fail in the presence of complex regions. In order to increase the accuracy of those machine learning algorithms, like Support Vector Machines or Random Forests, greater performance results were achieved. Deep learning, especially Convolutional Neural Networks (CNNs), has changed the way of Segmentation techniques, with these architectures: U-Net and Mask RCNN excelling on Semantic tasks. DeepLab and PSPNet combine AI and deep learning in advanced methods that gain significant advantage in accuracy by blending complex feature extraction and hierarchical learning. Based on this analysis, the strengths and limitations of various segmentation techniques are demonstrated, as certain contemporary techniques evolve and reveal their potential application.