|
Abstract
|
The increasing complexity and diversity of modern imaging applications demand more sophisticated segmentation techniques capable of handling challenges such as noise, heterogeneous regions, and feature variability. Traditional clustering-based methods often fall short in addressing these issues, leading to inaccurate or inconsistent results in critical domains such as medical imaging and surveillance. The proposed Possibilistic Multi-View Fuzzy Clustering (PMVFC) method offers a comprehensive solution by combining advanced techniques such as feature and view weighting with neighborhood information integration. This research is essential for developing robust and accurate segmentation tools that can adapt to a wide range of scenarios, ensuring reliable performance in applications where precision is paramount. The outcomes of this research have the potential to advance the state-of-the-art in image segmentation, offering significant benefits across diverse fields.
|