Multi-focal Image Segmentation, Classification and Authentication

In this book, we propose a general framework for multi-focal image segmentation, classification and authentication, the methodology being demonstrated on microscope pollen images. The framework is meant to be generic and based on a brute force-like approach aimed to be efficient not only on any kind, and any number, of pollen images (regardless of the pollen type), but also on any kind of multi-focal images. All stages of the framework are designed to be used in an automatic fashion. First, the optimal focus is selected using the absolute gradient method. Then, pollen grains are extracted from microscope images, followed by the automatic segmentation of their exine. A coarse-to-fine approach ensures a smooth and accurate segmentation of both structures. Finally, feature extraction and selection are performed on pollen grains using a generalized approach and the pollen classification is tested with four classifiers: Weighted Neighbor Distance, Neural Network, Decision Tree and Random Forest. The latter method, which has shown the best and more robust classification accuracy results (above 97% for any number of pollen types), is finally used for a final authentication stage.

Autorentext

Since January 2011, François Chung holds a Ph.D. in medical imageanalysis from Mines ParisTech, France. His Ph.D. was done withinthe Asclepios Research Team, INRIA Sophia-Antipolis, France. In2005, François Chung graduated industrial engineer in computerscience (Ing., M.Sc.) from the Institut Supérieur Industriel deBruxelles (ISIB), Belgium.