Mathematical morphology and its application to signal and image processing

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The 9th ISMM conference featured a diverse collection of papers centered on mathematical morphology, focusing on image treatment through set and lattice theory. This edition highlighted an increasing interaction with other fields of image and signal processing, showcasing hybrid methods that merge traditional morphological techniques with linear filtering. A notable trend is the rise of adaptive morphological filtering, where the local shape of structuring elements is defined by non-morphological approaches, building on previous PDE-based developments. Similar advancements are evident in segmentation, particularly with morphological active contours. Within the field itself, diversification is significant, with new research areas emerging, such as the morphology of graph-based and complex-based image representations. Additionally, the established domain of connected filtering is seeing new theories, algorithms, and an expansion toward hyperconnected filters. The conference also presented advances in morphological machine learning, multi-valued, and fuzzy morphology. Despite the theoretical reputation of mathematical morphology, practitioners remain focused on practical applications, ensuring relevance in real-world scenarios.