Surface fitting for individual image thresholding and beyond

In this study, the authors propose a novel algorithm for background-foreground segmentation. The work is motivated by the need for information about the background that is obscured by objects, in order to achieve accurate segmentation. The algorithm utilises the principle of estimating the occluded background by surface fitting. Edge detection methods are used to detect boundaries between foreground and background, identifying background points as well as foreground points. This categorisation will guarantee that most points used for surface fitting are from the same category and thus the proposed surface fitting with random sample consensus (RANSAC) algorithm will produce an accurate estimate of the surface. The authors algorithm has been applied to the real-world applications of segmenting plant images with inhomogeneous but smooth background and measuring the relative temperature of plants. Comparisons with experimental results show that the proposed algorithm is able to reduce significantly background inhomogeneities in infra-red images for the accurate estimation of temperature differences between background and plants, which provides important clues for fast and cheap genetic screening. The proposed algorithm is also able to overcome the intensity inhomogeneities for accurate image segmentation, particularly for plant root image segmentation with the preservation of lateral plant roots.