Correcting and matching time sequence images of plant leaves using Penalized Likelihood Warping and Robust Point Matching

Stress in plants can be measured using chlorophyll fluorescence imaging. The development of patterns in time can give an indication of the type of stress. Since leaves grow and show leaf movements, there is no pixel to pixel correspondence in time laps imaging data. In this article, Penalized Likelihood Warping and Robust Point Matching methods for recovering the pixel to pixel correspondence of a leaf within a time series are studied. It is shown that Robust Point Matching method is more suitable for our application than Penalized Likelihood Warping. Furthermore, Robust Point Matching method is much faster than Penalized Likelihood Warping. After warping an image sequence of a cabbage leaf infected with the bacteria Xanthomonas campestris pv. campestris, it was possible to identify infected spots 30 h after infection, where in unwarped images differences just can be seen 60 h after infection. Time series of the warped image data can be used to study and measure stress patterns in order to detect and identify diseases at an early stage.