TY - CONF A1 - Prodanov, Dimiter A1 - Vohra, Sumit Kumar T1 - Active Segmentation: Differential Geometry meets Machine Learning T2 - Proceedings of the 23rd International Conference on Computer Systems and Technologies N2 - Image segmentation is an active area of research for more than 30 years. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only can not address the question as to which features are appropriate for a certain classification problem. This paper presents a project supported in part by the International Neuroinformatics Coordination Facility through the Google Summer of code. The project resulted in an automated image segmentation and classification platform, called Active Segmentation for ImageJ (AS/IJ). The platform integrates a set of filters computing differential geometrical invariants and combines them with machine learning approaches. Y1 - 2022 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/8769 SP - 1 EP - 6 ER -