Foreground Extraction Algorithm for Monocotyledonous Plants based on CNN and CRF

Abstract

We aim at developing a foreground extraction method for automatic leaf identification in a monocotyledon image. In order to identify accurately all leaves in a two-dimensional plant image, it is critical to extract an exact plant region because accidental holes or breaks in the extracted foreground image may lead to wrong structural results in subsequent analysis steps. However, because monocotyledonous plants such as rice, wheat, and barley have many thin leaves and complex morphology, it is highly possible that their plant images have a lot of holes by self-occlusion between the leaves or by color change and image blurring. In addition, plant images usually have extremely thin regions caused by its thin shape. We propose a foreground extraction algorithm based on a fully convolutional neural network (CNN) and a dense conditional random field (CRF) to retain holes and breaks made by morphological characteristics of monocotyledonous plants and to eliminate the accidental holes and breaks. In our algorithm, a CNN plays a role in labeling pixels as foreground or not and a CRF strengthens connection between foreground pixels. By synergistic integration of both models, our proposed algorithm achieve a better foreground extraction accuracy for plant images. Experiments show that our proposed method effectively extracts foreground regions from a single 2-dimensional monocotyledonous plant image and is fast enough for high-throughput phenotyping.