Classifying Pollen Using Robust Sequence Alignment of Sparse Z-Stack Volumes

The identification of pollen grains is a task needed in many scientific and industrial applications, ranging from climate research to petroleum exploration. It is also a time-consuming task. To produce data, pollen experts spend hours, sometimes months, visually counting thousands of pollen grains from hundreds of images acquired by microscopes. Most current automation of pollen identification rely on singlefocus images. While this type of image contains characteristic texture and shape, it lacks information about how these visual cues vary across the grain’s surface. In this paper, we propose a method that recognizes pollen species from stacks of multi-focal images. Here, each pollen grain is represented by a multi-focal stack. Our method matches unknown stacks to pre-learned ones using the Longest-Common Sub-Sequence (LCSS) algorithm. The matching process relies on the variations of visual texture and contour that occur along the image stack, which are captured by a low-rank and sparse decomposition technique. We tested our method on 392 image stacks from 10 species of pollen grains. The proposed method achieves a remarkable recognition rate of 99.23%.