A General Framework for Multi-focal Image Classification and Authentication: Application to Microscope Pollen Images
This work addresses the need for automated and generic classification in microscopy and multi-focal imaging, though it appears incremental as it combines existing methods like gradient-based focus selection and snake-based segmentation.
The authors tackled the problem of classifying and authenticating multi-focal images, specifically microscope pollen images, by proposing a general framework that achieved over 97% accuracy across any number of pollen types using a Random Forest classifier.
In this article, we propose a general framework for multi-focal image 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's pipeline 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 using a coarse-to-fine approach involving both clustering and morphological techniques (coarse stage), and a snake-based segmentation (fine stage). Finally, features are extracted and selected using a generalized approach, and their 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 the authentication stage.