Active Segmentation: Differential Geometry meets Machine Learning
Image segmentation and classification is an active area of research in the last 30 years. Traditional image segmentation algorithms are problem-specific and limited in scope. On the other hand, the use of machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation. However, machine learning only cannot address the question as to which features are appropriate for a certain classification or segmentation problem. The presentation will demonstrate a project supported in part by the INCF through the Google Summer. The project goal is to develop 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 based and combines them with machine learning approaches.