Interactive Segmentation and Classification Library using Machine Learning and Geometric Features
Active Segmentation aims of providing a general purpose workbench that would allow biologists to access state-of-the-art techniques in machine learning and image processing to improve their image segmentation results. Currently, Active Segmenation have various geometric features like Laplace of Gaussian , Gaussian Derivatives etc. It also provide various machine learning algorithms like Support Vector machine, Naive Bayes etc available in WEKA library. The platform allows researchers to add their own feature extraction and machine learning algorithms.
The major goal of active segmentation is to provide a well documented tool for users along with visual insights so that user can understand the underlined methodology